Probability and statistics Books

Book SynopsisIn this undergraduate text, the author has distilled the core of probabilistic ideas and methods for computer and data science. The book emphasizes probabilistic and computational thinking rather than theorems and proofs. It provides insights and motivates the students by telling them why probability works and how to apply it.The unique features of the book are as follows:This book contains many worked examples. Numerous instructive problems scattered throughout the text are given along with problem-solving strategies. Several of the problems extend previously covered material. Answers to all problems and worked-out solutions to selected problems are also provided.Henk Tijms is the author of several textbooks in the area of applied probability and stochastic optimization. In 2008, he received the prestigious INFORMS Expository Writing Award for his work. He also contributed engaging probability puzzles to The New York Times' former Numberplay column.

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Book SynopsisIn this undergraduate text, the author has distilled the core of probabilistic ideas and methods for computer and data science. The book emphasizes probabilistic and computational thinking rather than theorems and proofs. It provides insights and motivates the students by telling them why probability works and how to apply it.The unique features of the book are as follows:This book contains many worked examples. Numerous instructive problems scattered throughout the text are given along with problem-solving strategies. Several of the problems extend previously covered material. Answers to all problems and worked-out solutions to selected problems are also provided.Henk Tijms is the author of several textbooks in the area of applied probability and stochastic optimization. In 2008, he received the prestigious INFORMS Expository Writing Award for his work. He also contributed engaging probability puzzles to The New York Times' former Numberplay column.

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Book SynopsisThis book deals with two main topics. The first is a theory that aims to unify the many interpretations of probability presented in the literature. The second uses this comprehensive theory of probability to answer the questions of quantum mechanics that have long been debated. The entire book proposes original solutions that several experimental cases substantiate.

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Book SynopsisNonparametric kernel estimators apply to the statistical analysis of independent or dependent sequences of random variables and for samples of continuous or discrete processes. The optimization of these procedures is based on the choice of a bandwidth that minimizes an estimation error and the weak convergence of the estimators is proved. This book introduces new mathematical results on statistical methods for the density and regression functions presented in the mathematical literature and for functions defining more complex models such as the models for the intensity of point processes, for the drift and variance of auto-regressive diffusions and the single-index regression models.This second edition presents minimax properties with Lp risks, for a real p larger than one, and optimal convergence results for new kernel estimators of function defining processes: models for multidimensional variables, periodic intensities, estimators of the distribution functions of censored and truncated variables, estimation in frailty models, estimators for time dependent diffusions, for spatial diffusions and for diffusions with stochastic volatility.

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Book SynopsisThis volume aims to return to the starting point of the fields of infinite dimensional analysis and quantum probability, fields that are growing rapidly at present, and to seriously attempt mutual interaction between the two, with a view to enumerating and solving the many fundamental problems they entail. For such a purpose, we look for interdisciplinary bridges in mathematics including classical probability and to different branches of physics, in particular, research for new paradigms for information science on the basis of quantum theory.

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Book SynopsisThis book provides an undergraduate-level introduction to discrete and continuous-time Markov chains and their applications, with a particular focus on the first step analysis technique and its applications to average hitting times and ruin probabilities. It also discusses classical topics such as recurrence and transience, stationary and limiting distributions, as well as branching processes. It first examines in detail two important examples (gambling processes and random walks) before presenting the general theory itself in the subsequent chapters. It also provides an introduction to discrete-time martingales and their relation to ruin probabilities and mean exit times, together with a chapter on spatial Poisson processes. The concepts presented are illustrated by examples, 138 exercises and 9 problems with their solutions.Table of ContentsProbability Background.- Gambling Problems.- Random Walks.- Discrete-Time Markov Chains.- First Step Analysis.- Classification of States.- Long-Run Behavior of Markov Chains.- Branching Processes.- Continuous-Time Markov Chains.- Discrete-Time Martingales.- Spatial Poisson Processes.- Reliability Theory.

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Book SynopsisThis book seeks to bridge the gap between statistics and computer science. It provides an overview of Monte Carlo methods, including Sequential Monte Carlo, Markov Chain Monte Carlo, Metropolis-Hastings, Gibbs Sampler, Cluster Sampling, Data Driven MCMC, Stochastic Gradient descent, Langevin Monte Carlo, Hamiltonian Monte Carlo, and energy landscape mapping. Due to its comprehensive nature, the book is suitable for developing and teaching graduate courses on Monte Carlo methods. To facilitate learning, each chapter includes several representative application examples from various fields. The book pursues two main goals: (1) It introduces researchers to applying Monte Carlo methods to broader problems in areas such as Computer Vision, Computer Graphics, Machine Learning, Robotics, Artificial Intelligence, etc.; and (2) it makes it easier for scientists and engineers working in these areas to employ Monte Carlo methods to enhance their research.Trade Review“This monograph ... is intended to be a textbook for graduate students in statistics, computer science and engineering. It covers a very broad range of topics ... . Each chapter is finished by a rather long list of relevant references. Thus, it can be used also as a reference book by researches in the fields of machine learning, pattern recognition ... . it can be a useful reference to many important Monte Carol methods.” (Jaromír Antoch, zbMATH 1483.65001, 2022)“True to its goal, the text offers a comprehensive overview on Monte Carlo methods. … this text is a quality reference for researchers interested in computer vision, computer graphics, machine learning, artificial intelligence and related fields.” (Grant Innerst, MAA Reviews, July 18, 2021)Table of Contents1 Introduction to Monte Carlo Methods.- 2 Sequential Monte Carlo.- 3 Markov Chain Monte Carlo - the Basics.- 4 Metropolis Methods and Variants.- 5 Gibbs Sampler and its Variants.- 6 Cluster Sampling Methods.- 7 Convergence Analysis of MCMC.- 8 Data Driven Markov Chain Monte Carlo.- 9 Hamiltonian and Langevin Monte Carlo.- 10 Learning with Stochastic Gradient.- 11 Mapping the Energy Landscape.

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Book SynopsisThis book eases the task of converting research work into a manuscript, and covers the recent developments in publishing that often stump budding researchers.Few researchers in the biomedical sciences are trained in the essential skills of reporting their results, and they seek help in writing a paper that will be acceptable for publication in the ‘right’ journal, and in presenting their results ‘effectively’ at a meeting. As well as covering the basic aspects of preparing manuscripts for publication, the book discusses best practices and issues relating to the publication of biomedical research, including topics such as peer-review, authorship, plagiarism, conflicts of interest, publication misconduct, electronic publishing and open-access journals. With more than two decades of experience in conducting workshops on writing scientific papers, the editors have brought together the expertise of 29 authors from seven countries to produce this one-stop guide to publishing research in biomedical sciences. This book is intended for young researchers who are beginning their careers and wish to hone their skills and understand the rigors of research writing and publishing. Table of ContentsThe IMRAD Structure.- The Introduction Section.- The Methods Section.- The Results Section.- The Discussion Section.- 1. The ‘Argument Matrix’: A Structured Method to Write and Organize a Manuscript.- Writing an Abstract.- Title, Keywords and Cover Letter.- References and Bibliographic Softwares.- Copyright Issues.- Letters, Editorials and Book Reviews.- Case Reports and Case Series.- Books, and Chapters in Books.- Beyond Substance.- Authorship and Acknowledgements.- How to Choose the Right Journal.- Manuscript preparation.- Reporting Guidelines.- Open Access Journals.- Electronic Publishing.- Editorial Process and Peer-review.- Conflicts of Interest.- Redundant Publications.- Scientific Fraud and Other Types of Scientific Misconduct.- Podium Presentation.- 1.The Poster.

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Book SynopsisThis is the first book to present time series analysis using the SAS Enterprise Guide software. It includes some starting background and theory to various time series analysis techniques, and demonstrates the data analysis process and the final results via step-by-step extensive illustrations of the SAS Enterprise Guide software. This book is a practical guide to time series analyses in SAS Enterprise Guide, and is valuable resource that benefits a wide variety of sectors. Table of Contents1 Introduction 1.1 Overview1.2 SAS Enterprise Guide2 Basic Statistics and Regression Models2.1 Calculating new variables2.2 Normality test2.3 Simple linear regression2.4 Multiple linear regression 3 Time Series3.1 Smoothing methods3.2 ARIMA model3.3 Regression model with AR errors4 Panel Models4.1 Fixed effect4.2 Random effectReferences

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Book SynopsisThis book focuses on the application of data mining techniques in cricket. It provides detailed examples of how data mining can be helpful for decision-making in sports with special reference to cricket, particularly the quantitative features related to Twenty20 cricket, the latest and the most popular format of the game. The book highlights the performance quantification of cricketers (batsmen, bowlers, all-rounders, and wicket keepers), determining the market valuation of cricketers based on their on-field performances and the effect of age on the performance of the cricketers. It also provides a comprehensive overview of the different aspects of the game where quantitative techniques are beneficial, and highlights the use of statistical and data mining tools in analysing sports-related data and objective decision-making in sports. The book appeals to a wide readership, including postgraduate students of statistics/mathematics, data analysts, sports management bodies. It also offers data miners, such as researchers in statistics, mathematics, operations research, and computer science ideas for projects.Table of ContentsChapter 1. Cricket, Statistics and Data Mining.- Chapter 2. Franchisee Cricket and Cricketer’s Auction.- Chapter 3. Quantifying Performance of Cricketers.- Chapter 4. Fielding Performance Measure: Issues, Concern and Solution.- Chapter 5. Performance Based Market Valuation of Players.- Chapter 6. Impact of Age on Performance of Cricketers.- Chapter 7. Performing Under Difficulty: The Magical Pressure Index.- Chapter 8. Decision Making Approaches to Optimum Team Selection.

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Book SynopsisThis book provides a contemporary treatment of quantitative economics, with a focus on data science. The book introduces the reader to R and RStudio, and uses expert Hadley Wickham’s tidyverse package for different parts of the data analysis workflow. After a gentle introduction to R code, the reader’s R skills are gradually honed, with the help of “your turn” exercises. At the heart of data science is data, and the book equips the reader to import and wrangle data, (including network data). Very early on, the reader will begin using the popular ggplot2 package for visualizing data, even making basic maps. The use of R in understanding functions, simulating difference equations, and carrying out matrix operations is also covered. The book uses Monte Carlo simulation to understand probability and statistical inference, and the bootstrap is introduced. Causal inference is illuminated using simulation, data graphs, and R code for applications with real economic examples, covering experiments, matching, regression discontinuity, difference-in-difference, and instrumental variables. The interplay of growth related data and models is presented, before the book introduces the reader to time series data analysis with graphs, simulation, and examples. Lastly, two computationally intensive methods—generalized additive models and random forests (an important and versatile machine learning method)—are introduced intuitively with applications. The book will be of great interest to economists—students, teachers, and researchers alike—who want to learn R. It will help economics students gain an intuitive appreciation of applied economics and enjoy engaging with the material actively, while also equipping them with key data science skills. Table of ContentsCh 1 Introduction.- Ch 2 R and RStudio.- Ch 3 Getting data into R.- Ch 4 Wrangling and graphing data.- Ch 5 Functions.- Ch 6 Matrices.- Ch 7 Probability and statistical inference.- Ch 8 Causal inference.- Ch 9 Solow model and basic facts of growth.- Ch 10 Causal inference for growth.- Ch 11 Graphing and simulating basic time series.- Ch 12 Simple examples: forecasting and causal inference.- Ch 13 Generalized additive models.- Ch 14 Tree models.

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Book SynopsisThis book explores a range of prospective avenues, models, and operational and strategic approaches to Lean Six Sigma (LSS), a contemporary Continuous Improvement (CI) practice for achieving a quality-based competitive edge in organisations. Lean Six Sigma project case studies from banking organizations help to illustrate the operational dimensions of LSS, while the case-specific and cross-case analyses presented here demonstrate its strategic value. While the case data used to arrive at the findings come from the Banking firms, it allows generalizability beyond the Banking and Financial Services sector. The book contends that LSS is not merely a CI practice, but a higher-order organizational capability, more precisely a dynamic capability, that allows firms to gain a competitive edge based on quality. Addressing the interests of practitioners and researchers alike, the book strikes a balance between theory and practice. For practitioners, it offers guidance on using LSS to gain a competitive advantage, and on evidence-based practice in quality management and operational excellence. For researchers, it presents a wealth of literature and expands the body of knowledge on quality management. Accordingly, the book is of immense value to both practitioners and researchers, helping the former unlock the value of LSS as both an operational and strategic resource, and highlighting potential research directions and applications for the latter.“This book provides a deep understanding of Lean Six Sigma applications. It inspires by transferring the principles of the concept into uncommon areas of operations and management behind the usual quality and project management. While reading the book I got hit by a great idea of applying Lean Six Sigma in my digital business as well. My impression at the end of the book was that sky is the limit for the right employment of Lean Six Sigma, especially while viewing it from a dynamic capabilities’ lens. Readers of this book will surely receive insights for improving their business processes both operationally and strategically. Although the book is focused on banking, it is actually suitable for a really wide audience. This is a brilliant piece of research as a book that will serve as a guide for transformation by the prism of Lean Six Sigma.” - Professor. Dr. ZornitsaYordanova, Chief Assistant Professor of Innovation Management, University of National and World Economy, Sofia, Bulgaria “Lean Six Sigma needs to be understood from a systems perspective and there exists a huge knowledge gap in this area of finding holistic solutions to business problems. This book is a very welcome work that addresses this call. It integrates quality management resources and dynamic capabilities view towards practice. Banking and Financial Services was aptly chosen as it has the most direct applicability for social enterprises. Anyone interested in creating more impact with less will surely benefit from reading the book” -Alex Abraham, Chief Executive Officer, Lean Success Partners, Winnipeg, Manitoba, Canada “The book is a refreshing booster to the world of Quality Management especially in the context of Banking and Financial Services. Concepts and terms like “Rapidness of Lean & robustness of Six Sigma to solve operational problems” “Hybrid methodology” resonate very well with what we do in the industry today. Another interesting fact about the book is applying “Dynamic Capabilities approach” to Quality Management, that sets a fresh Quality Oven and ensures this book is definitely a good investment of authors’ intellect.Best part – Even if a reader is new to the world of Quality,this book will be appropriate and resonating. For Researchers and Practitioners, both being leaders orfresh entrants, this book stands out to be a must-read, as it demonstrates the success of the Lean Six Sigma methodology via case studies and practical applications.” -Udit Salvan, Director, Global Transformation & Engineering Network,An American Multinational Financial Services Corporation, New York, USATable of ContentsCHAPTER 1 INTRODUCTION 1.1 The need for the hour 1.2 An introduction to Quality Management 1.3 From Lean and Six Sigma to Lean Six Sigma 1.4 Focus on this book 1.5 Scope and methods used 1.6 Summary of Chapter 1 CHAPTER 2 BACKGROUND OF KEY CONCEPTS 2.1 A Brief Overview of Service Quality 2.2 Competitive Advantage in Quality 2.3 Continuous Improvement 2.4 Organizational Learning in The Context of CI 2.5 Lean Services 2.6 Six Sigma in Services 2.7 A Brief Overview of Lean Six Sigma 2.8 Summary of Chapter 2 CHAPTER 3 AN OVERVIEW OF BANKING SECTOR 3.1 Introduction to Banking3.2 Types of Banking and associated practices3.3 Quality Management in Banking3.4 Process Improvement in Banking vs other sectors 3.5 Summary of Chapter 3 CHAPTER 4 A MORPHOLOGICAL ANALYSIS OF RESEARCH LITERATURE ON LEAN SIX SIGMA FOR SERVICES 4.1 An Overview of Lean Six Sigma in Services 4.2 Data from Literature and their Classification 4.2.1 Fundamental Classification 4.2.2 Methodological Classification 4.2.3 Chronological Classification 4.2.4 Sector-Wise Classification 4.3 Morphological Analysis of the Literature 4.3.1 Dimension 1: Organizational Context of Application 4.3.2 Dimension 2: Desired Outcomes 4.3.3 Dimension 3: Implementation Systems 4.3.4 Dimension 4: LSS Tools and Techniques 4.3.5 Dimension 5: Integration with Other Philosophies 4.3.6 Dimension 6: Evaluation Methods 4.4 Discussion 4.5 Summary of Chapter 4 CHAPTER 5 LEAN SIX SIGMA PROJECTS IN BANKING FIRMS – Analysis of Implementation Cases 5.1 An Overview of Research Literature on LSS in Banking 5.2 Evidence from Practitioners’ Literature 5.3 Real Time Applications of LSS in Banking 5.3.1 Approach 5.3.2 LSS Project Management Method Used 5.4 Case Studies 5.4.1 Case A: Optimization of Employee Utilization 5.4.2 Case B: Accuracy Improvement in Payments Processing 5.5 Lessons Learned and Managerial Implications 5.5.1 LSS Project Management is a sub-Set of LSS Deployment 5.5.2 LSS As a Systems Approach for Process Improvement 5.5.3 Identification of Correct LSS Candidates 5.5.4 Management of Stakeholders in LSS Projects 5.5.5 Change Leadership for LSS Projects 5.6 Summary of Chapter 5 CHAPTER 6 LEAN SIX SIGMA AS A DYNAMIC CAPABILITY – An Analysis of Banking Firms 6.2 Approach 6.2.1 Case Selection 6.2.2 Site Visits and Preliminary Interviews 6.2.3 Additional Interviews and Cross-Case Analysis 6.3 Findings 6.3.1 Compelling need for purposive creation of LSS Capability 6.3.2 LSS as a Vital Component of the Capabilities Network 6.3.3 Path Dependency and Emergence 6.3.4 LSS Enables Organizational Learning 6.3.5 Technical and Evolutionary Fitness of LSS 6.3.6 LSS exhibits VRIN Characteristics 6.3.7 LSS exhibits Agility towards Environmental Dynamism 6.4 Summary of the Conceptual Model 6.4.1 Motivational Drivers 6.4.2 Input Factors 6.4.3 Activity Components 6.4.4 Influencing Factors 6.4.5 Outcomes 6.5 Discussion 6.6 Summary of Chapter 6 CHAPTER 7 SUMMARY AND CONCLUSIONS 7.1 Overall Summary 7.2 Summary of Findings and Affirmations 7.3 Implications 7.3.1 Practical Implications for Managers/Practitioners 7.3.2 Theoretical Implications 7.4 Limitations and Directions for Future 7.5 Conclusions

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Book SynopsisThis book addresses sample size and power in the context of research, offering valuable insights for graduate and doctoral students as well as researchers in any discipline where data is generated to investigate research questions. It explains how to enhance the authenticity of research by estimating the sample size and reporting the power of the tests used. Further, it discusses the issue of sample size determination in survey studies as well as in hypothesis testing experiments so that readers can grasp the concept of statistical errors, minimum detectable difference, effect size, one-tail and two-tail tests and the power of the test. The book also highlights the importance of fixing these boundary conditions in enhancing the authenticity of research findings and improving the chances of research papers being accepted by respected journals. Further, it explores the significance of sample size by showing the power achieved in selected doctoral studies. Procedure has been discussed to fix power in the hypothesis testing experiment. One should usually have power at least 0.8 in the study because having power less than this will have the issue of practical significance of findings. If the power in any study is less than 0.5 then it would be better to test the hypothesis by tossing a coin instead of organizing the experiment. It also discusses determining sample size and power using the freeware G*Power software, based on twenty-one examples using different analyses, like t-test, parametric and non-parametric correlations, multivariate regression, logistic regression, independent and repeated measures ANOVA, mixed design, MANOVA and chi-square.Table of ContentsPreface Acknowledgements 1 Introduction to Sample Size Determination Introduction Issue of Power due to inappropriate sample size Some case studies Flow Diagram of Determining sample size and power Summary 2 Understanding Statistical Inference Introduction Estimating Parameters Estimating Population Mean Confidence Coefficient Confidence Interval Factors Affecting Confidence Interval Estimating Population Proportion Hypothesis Testing Type I and Type II Errors Power of the Test Relationship between Type I and Type II Errors One Tailed and Two Tailed Tests Procedure in Hypothesis Testing Experiment Effect Size Summary 3 Understanding Concepts in Estimating Sample Size in Survey Studies Introduction Determining Sample Size in Estimating Population Mean Factors Affecting Sample Size Sample Size Determination for Estimating Mean when Population SD Known: Illustration 3.1 Sample Size Determination for Estimating Mean when Population SD Unknown: Illustration 3.2 Sample Size Determination for Estimating Mean when Population SD Unknown: Illustration 3.3 Sample Size Determination for Estimating Mean when Population SD Unknown: Illustration 3.4 Determining Sample Size in Estimating Population Proportion Sample Size Determination for Estimating Proportion: Illustration 3.5 Sample Size Determination for Estimating Proportion: Illustration 3.6 Sample Size Determination for Estimating Proportion: Illustration 3.7 Sample Size Determination for Estimating Proportion: Illustration 3.8 Determining Sample Size in Estimating Difference Between Two Population Means Summary 4 Understanding Concepts in Estimating Sample Size in Hypothesis Testing Experiment Introduction Sample Size on the Basis of Power One Sample Testing of Mean Determining Sample Size Estimation of Minimum Sample Size to Test H0 : µ=37 : Illustration 4.1 Minimum Detectable Difference Estimation of Minimum Detectable Difference for Testing H0: µ=37: Illustration 4.2 Estimation of Power in One Sample t Test Estimation of Power in Testing H0: µ=37: Illustration 4.3 Testing Difference Between Two Means Determining Sample Size in Two Sample t Test Estimation of Sample Size for Two Sample t Test for Mean : Illustration 4.4 Estimation of Power in Two Sample t Test Estimation of Power in Two Sample t Test for Mean : Illustration 4.5 Summary 5 Use of G*Power Software Introduction Procedure of Installing G*Power 3.1 Summary 6 Determining Sample Size in Experimental Studies Introduction One Sample Testing Testing Difference of Sample Mean from Population Mean Sample Size and Power Determination: Illustration 6.1 Testing Difference of Sample Proportion from Population Proportion Sample Size Determination: Illustration 6.2 Two Sample Testing Comparing Group Means in Two Independent Samples Sample Size and Power Determination: Illustration 6.3 Comparing Paired Group Means Sample Size Determination: Illustration 6.4 Comparing two Group Means Using Mann Whitney Test Sample Size Determination: Illustration 6.5 Comparing Paired Group Means Using Wilcoxon Signed Rank Test Sample Size Determination: Illustration 6.6 Comparing Two Proportions Sample Size Determination: Illustration 6.7 Correlation Coefficient: Testing Significance Case I: Testing Whether Sample Correlation Differs From 0 Sample Size Determination: Illustration 6.14 Case II: Testing Whether Sample Correlation Differs from a Known Value Sample Size Determination: Illustration 6.15 Correlation Coefficients: Testing Significant Difference Between Two Independent Correlations Sample Size Determination: Illustration 6.16 Bi-Serial Correlation: Testing Significance Sample Size Determination: Illustration 6.17 Goodness of Fit: Testing With Chi-Square Sample Size Determination in Goodness of Fit: Illustration 6.18 Linear Multiple Regression Model Sample Size Determination in Linear Multiple Regression: Illustration 6.19 Logistic Regression Sample Size Determination for Continuous Predictors: Illustration 6.20 Sample Size Determination for a Dichotomous Predictor: Illustration 6.21 Summary 7 Determining Sample Size in General Linear Models Introduction Analysis of Variance One–Way Analysis of Variance Sample Size Determination: Illustration 6.8 Two–Way Analysis of Variance Sample Size Determination for Main and Interaction Effect: Illustration 6.9 Repeated Measures ANOVA Between Factors Sample Size Determination: Illustration 6.10 Repeated Measures ANOVA Within Factors Sample Size Determination: Illustration 6.11 Repeated ANOVA Within-Between Interaction Manova Experiment: for Testing the Significance of Global Effect Sample Size Determination: Illustration 6.12 Manova Experiment: Testing Significance of Interaction Effect Sample Size Determination: Illustration 6.13 Summary Appendix Bibliography

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Book SynopsisThis book introduces readers to various signal processing models that have been used in analyzing periodic data, and discusses the statistical and computational methods involved. Signal processing can broadly be considered to be the recovery of information from physical observations. The received signals are usually disturbed by thermal, electrical, atmospheric or intentional interferences, and due to their random nature, statistical techniques play an important role in their analysis. Statistics is also used in the formulation of appropriate models to describe the behavior of systems, the development of appropriate techniques for estimation of model parameters and the assessment of the model performances. Analyzing different real-world data sets to illustrate how different models can be used in practice, and highlighting open problems for future research, the book is a valuable resource for senior undergraduate and graduate students specializing in mathematics or statistics.Table of ContentsIntroduction.- Preliminaries.- Methods of Estimation - Iterative.- Methods of Estimation - Non-iterative.- Asymptotic Results (of Sinusoidal model).- Order estimation.- Fundamental Frequency Model and its generalization.- Data Analysis.- Two dimensional and multidimensional models.- Chirp Signal Model.- Random Amplitudes.- Related Models.- Appendices.

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Book SynopsisGiven the rapid pace of development in economics and finance, a concise and up-to-date introduction to mathematical methods has become a prerequisite for all graduate students, even those not specializing in quantitative finance. This book offers an introductory text on mathematical methods for graduate students of economics and finance–and leading to the more advanced subject of quantum mathematics. The content is divided into five major sections: mathematical methods are covered in the first four sections, and can be taught in one semester. The book begins by focusing on the core subjects of linear algebra and calculus, before moving on to the more advanced topics of probability theory and stochastic calculus. Detailed derivations of the Black-Scholes and Merton equations are provided – in order to clarify the mathematical underpinnings of stochastic calculus. Each chapter of the first four sections includes a problem set, chiefly drawn from economics and finance. In turn, section five addresses quantum mathematics. The mathematical topics covered in the first four sections are sufficient for the study of quantum mathematics; Black-Scholes option theory and Merton’s theory of corporate debt are among topics analyzed using quantum mathematics.Table of ContentsPART I : INTRODUCTION 1 Introduction 1.1 Introduction 1.2 Elementary Algebra 1.2.1 Quadratic polynomial 1.3 Finite Series 1.4 Infinite Series 1.4.1 Cauchy convergence 1.5 Problems 2 Functions 2.1 Introduction 2.2 Exponential function 2.3 Demand and supply function 2.4 Option theory payoff 2.5 Interest rates; bonds 2.6 Problems PART II : LINEAR ALGEBRA 3 Simultaneous linear equations 3.1 Introduction 3.2 Two commodities 3.3 Vectors 3.4 Basis vectors 3.4.1 Scalar product 3.5 Linear transformations; matrices 3.6 EN: N-dimensional linear vector space 3.7 Linear transformations of EN 3.8 Problems 4 Matrices 4.1 Introduction 4.2 Matrix multiplication 4.3 Properties of N × N matrices 4.4 System of linear equations 4.5 Determinant: 2 × 2 case 4.6 Inverse of a 2 × 2 matrix 4.7 Outer product; transpose 4.7.1 Transpose 4.8 Eigenvalues and eigenvectors 4.8.1 Spectral decomposition 4.9 Problems 5 Square matrices 5.1 Determinant: 3 × 3 case 5.2 Properties of determinants 5.3 N × N determinant 5.3.1 Inverse of a N × N matrix 5.4 Leontief input-output model 5.4.1 Hawkins-Simon condition 5.5 Symmetric matrices 5.6 Symmetric matrix: diagonalization 5.6.1 Functions of a symmetric matrix 5.7 Hermitian matrices 5.8 Diagonalizable matrices 5.8.1 Non-symmetric matrix 5.9 Change of Basis states 5.9.1 Symmetric matrix: change of basis 5.9.2 Hermitian matrix: change of basis 5.10 Problems PART III : CALCULUS 6 Integration 6.1 Introduction 6.2 Sums leading to integrals 6.3 Definite and indefinite integrals 6.4 Applications in economics 6.5 Multiple Integrals 6.5.1 Change of variables 6.6 Gaussian integration 6.6.1 N-dimensional Gaussian integration 6.7 Problems 7 Differentiation 7.1 Introduction 7.2 Inverse of Integration 7.3 Rules of differentiation 7.4 Integration by parts 7.5 Taylor expansion 7.6 Minimum and maximum 7.6.1 Maximizing profit 7.7 Integration; change of variable 7.8 Partial derivatives 7.8.1 Chain rule; Jacobian 7.8.2 Polar coordinates; Gaussian integration 7.9 Hessian matrix: critical points 7.10 Constrained optimization: Lagrange multiplier 7.10.1 Interpretation of λc 7.11 Line integral; Exact and inexact differentials 7.12 Problems 8 Functional analysis 8.1 Dirac bracket and vector notation 8.2 Continuous basis states 8.3 Dirac delta function 8.4 Basis states for function space 8.5 Operators on function space 8.6 Gaussian kernel 8.7 Fourier Transform 8.8 Taylor expansion 8.9 Gaussian functional integration 8.10 Problems 9 Ordinary Differential Equations 9.1 Introduction 9.2 Separable differential equations 9.3 Linear differential equations 9.4 Bernoulli differential equation 9.5 Homegeneous differential equation 9.6 Second order linear differential equations 9.6.1 Single eigenvalue 9.7 Ricatti differential equation 9.8 Inhomogeneous second order differential equations 9.8.1 Green’s function 9.9 System of linear differential equations 9.10 Strum-Louisville theorem; special functions 9.11 Problems PART IV : PROBABILITY THEORY 10 Random variables 10.1 Introduction: Risk 10.1.1 Example 10.2 Key ideas of probability 10.3 Discrete random variables 10.3.1 Bernoulli random variable 10.3.2 Binomial random variable 10.3.3 Poisson random variable 10.4 Continuous random variables 10.4.1 Uniform random variable 10.4.2 Exponential random variable 10.4.3 Normal (Gaussian) random variable 10.5 Problems 11 Probability distribution functions 11.0.1 Cumulative density 11.1 Axioms of probability theory 11.2 Joint probability density 11.3 Independent random variables 11.3.1 Law of large numbers 11.4 Correlated random variables 11.5 Marginal probability density 11.6 Conditional expectation value 11.6.1 Discrete random variable 11.6.2 Continuous random variables 11.7 Problems 12 Stochastic processes & Option pricing 12.1 Gaussian white noise 12.1.1 Integrals of White Noise 12.2 Ito Calculus 12.3 Lognormal Stock Price 12.4 Black-Scholes Equation; Hedged Portfolio 12.4.1 Assumptions in the Derivation of Black-Scholes 12.5 Risk-Neutral Martingale Solution of the Black-Scholes Equation 12.6 Black-Scholes-Schrodinger equation 12.7 Linear Langevin Equation 12.7.1 Random Paths 12.8 Problems 13 Appendix 13.1 Introduction 13.2 Integers 13.3 Real numbers 13.4 Cantor’s Diagonal Argument 13.5 Higher Order Infinities 13.6 Mathematical Logic

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Book SynopsisThe book presents the fundamental concepts from asymptotic statistical inference theory, elaborating on some basic large sample optimality properties of estimators and some test procedures. The most desirable property of consistency of an estimator and its large sample distribution, with suitable normalization, are discussed, the focus being on the consistent and asymptotically normal (CAN) estimators. It is shown that for the probability models belonging to an exponential family and a Cramer family, the maximum likelihood estimators of the indexing parameters are CAN. The book describes some large sample test procedures, in particular, the most frequently used likelihood ratio test procedure. Various applications of the likelihood ratio test procedure are addressed, when the underlying probability model is a multinomial distribution. These include tests for the goodness of fit and tests for contingency tables. The book also discusses a score test and Wald’s test, their relationship with the likelihood ratio test and Karl Pearson’s chi-square test. An important finding is that, while testing any hypothesis about the parameters of a multinomial distribution, a score test statistic and Karl Pearson’s chi-square test statistic are identical. Numerous illustrative examples of differing difficulty level are incorporated to clarify the concepts. For better assimilation of the notions, various exercises are included in each chapter. Solutions to almost all the exercises are given in the last chapter, to motivate students towards solving these exercises and to enable digestion of the underlying concepts. The concepts from asymptotic inference are crucial in modern statistics, but are difficult to grasp in view of their abstract nature. To overcome this difficulty, keeping up with the recent trend of using R software for statistical computations, the book uses it extensively, for illustrating the concepts, verifying the properties of estimators and carrying out various test procedures. The last section of the chapters presents R codes to reveal and visually demonstrate the hidden aspects of different concepts and procedures. Augmenting the theory with R software is a novel and a unique feature of the book. The book is designed primarily to serve as a text book for a one semester introductory course in asymptotic statistical inference, in a post-graduate program, such as Statistics, Bio-statistics or Econometrics. It will also provide sufficient background information for studying inference in stochastic processes. The book will cater to the need of a concise but clear and student-friendly book introducing, conceptually and computationally, basics of asymptotic inference.Trade Review“Every chapter is filled with detailed examples contemplating a large variety of statistical distributions. Each chapter finishes with the formulation of a list of conceptual exercises and another of computational exercises. … Each chapter includes a section with a series of R software procedures designed to illustrate the statistical concepts discussed and establish asymptotic properties, revealing and visually demonstrating some otherwise hidden aspects.” (Annibal Parracho Sant’Anna, zbMATH 1473.62005, 2021)Table of ContentsChapter 1: Introduction.- Chapter 2: Consistency of an Estimator.- Chapter 3: Consistent and Asymptotically Normal Estimators.- Chapter 4: CAN Estimators in Exponential and Cramer Families.- Chapter 5: Large Sample Test Procedures.- Chapter 6: Goodness of Fit Test and Tests for Contingency Tables.- Chapter 7: Solutions to Conceptual Exercises.

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Book SynopsisThis book presents the state of the art in extreme value theory, with a collection of articles related to a seminal paper on the bivariate extreme value distribution written by Professor Masaaki Sibuya in 1960, demonstrating various developments of the original idea over the last half-century. Written by active researchers, the unique combination of articles allows readers to gain a sense of the excellence of the field, ranging from theory to practice, and the tradition of theoretical developments motivated by practically important issues such as tsunamis and financial crises. The contributions discuss a range of topics, including the parameter estimation of the generalized beta distribution, resampling with the empirical beta copula, and regression analysis on imbalanced binary data, as well as the semiparametric estimation of the upper bound of extrema, the long-term analysis of extreme precipitation over Japanese river basins, and various rules of thumb in hydrology.Table of ContentsChap. 1 Estimation of generalized beta distributions (Authors: Sibuya and Mano).- Chap. 2 On Some Resampling Procedures with the Empirical Beta Copula (Kiriliouk, Segers and Tsukahara).- Chap. 3 Regression Analysis for Imbalanced Binary Data: Multi-Dimensional Case (Sei).- Chap. 4 An Analysis of Extremes: Semiparametric Efficiency in Regression (Ozeki and Doksum).- Chap. 5 Future Change in Relationships among Extreme Precipitation Statistics Using “d4PDF” (Tanaka).- Chap. 6 History and Perspectives of Hydrological Frequency Analysis in Japan (Takara).

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Book SynopsisThis book contains new aspects of model diagnostics in time series analysis, including variable selection problems and higher-order asymptotics of tests. This is the first book to cover systematic approaches and widely applicable results for nonstandard models including infinite variance processes. The book begins by introducing a unified view of a portmanteau-type test based on a likelihood ratio test, useful to test general parametric hypotheses inherent in statistical models. The conditions for the limit distribution of portmanteau-type tests to be asymptotically pivotal are given under general settings, and very clear implications for the relationships between the parameter of interest and the nuisance parameter are elucidated in terms of Fisher-information matrices. A robust testing procedure against heavy-tailed time series models is also constructed in the context of variable selection problems. The setting is very reasonable in the context of financial data analysis and econometrics, and the result is applicable to causality tests of heavy-tailed time series models. In the last two sections, Bartlett-type adjustments for a class of test statistics are discussed when the parameter of interest is on the boundary of the parameter space. A nonlinear adjustment procedure is proposed for a broad range of test statistics including the likelihood ratio, Wald and score statistics.Table of ContentsChapter 1. Elements of Stochastic Processes.- Chapter 2. Systematic approach for portmanteau tests in view of Whittle likelihood ratio.- Chapter 3. A new look at portmanteau test.- Chapter 4. Adjustments for a class of tests under nonstandard conditions.- Chapter 5. Adjustments for variance component tests in ANOVA models.- Chapter 6. Robust causality test of infinite variance processes.

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Book SynopsisThis book provides a comprehensive overview of recent novel coronavirus (SARS-CoV-2) infection and discusses developments in the field of nanoparticle/inorganic/organic materials development for antiviral application, therapeutic applications, PPE kit formulations and inclusion of simulated data. The contents focus on measures to keep the infections in check, materials aspects for detection and monitoring, AI modeling for prediction of spread of the virus, among others. This book will be a useful reference for researchers, scientists and policy makers alike.Table of ContentsDeciphering the recent advancements in diagnostic, therapeutic and vaccine candidates against COVID-19.- Personal Protective Equipments (PPE) As Corona Virus Shielding Material.- Exploration of inorganic materials with antiviral properties.- Perspective of organic based antimicrobial coating materials: Implication towards COVID-19.- Material aspects for detection and monitoring.- Recent developments focusing disinfectant systems for effective inactivation of Coronaviruses.- Study on the ANN Forecasting of Epidemical Diseases Study on the ANN Forecasting of Epidemical Diseases.- Simulation Studies Related to Covid-19 Pandemic.- A Detailed Scientometric Review of Coronavirus Research.

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Book SynopsisThis book gathers selected papers presented at the International Conference on Sentimental Analysis and Deep Learning (ICSADL 2021), jointly organized by Tribhuvan University, Nepal; Prince of Songkla University, Thailand; and Ejesra during June, 18–19, 2021. The volume discusses state-of-the-art research works on incorporating artificial intelligence models like deep learning techniques for intelligent sentiment analysis applications. Emotions and sentiments are emerging as the most important human factors to understand the prominent user-generated semantics and perceptions from the humongous volume of user-generated data. In this scenario, sentiment analysis emerges as a significant breakthrough technology, which can automatically analyze the human emotions in the data-driven applications. Sentiment analysis gains the ability to sense the existing voluminous unstructured data and delivers a real-time analysis to efficiently automate the business processes. Meanwhile, deep learning emerges as the revolutionary paradigm with its extensive data-driven representation learning architectures. This book discusses all theoretical aspects of sentimental analysis, deep learning and related topics.Table of ContentsAnalysis of Healthcare Industry Using Machine Learning Approach: A Case Study in Bengaluru Region.- Dynamic Document Localization for Ecient Mining.- SentiSeries: A Trilogy of Customer Reviews, Sentiment Analysis and Time Series.- Video Summarization using Fully Convolutional Residual Dense Network.- An Efficient Deep Learning Approach for Detecting Pneumonia Using the Convolutional Neural Network.- QMCDS: Quantum Memory for Cloud Data Storage.- A Study towards Bangla Fake News Detection using Machine Learning and Deep Learning.- A Deep Learning Approach to Analyze the Propagation of Pandemic in America.- Graph Convolution Based Joint Learning of Rumour with Content, User Credibility, Propagation Context and Cognitive as well as Emotion Signals.- Deep Learning based Real Time Object Classification and Recognition using Supervised Learning Approach.

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Book SynopsisThis volume includes selected technical papers presented at the Forum “Math-for-Industry” 2018. The papers written by eminent researchers and academics working in the area of industrial mathematics from the viewpoint of financial mathematics, machine learning, neural networks, inverse problems, stochastic modelling, etc., discuss how the ingenuity of science, technology, engineering and mathematics are and will be expected to be utilized. This volume focuses on the role that mathematics-for-industry can play in interdisciplinary research to develop new methods. The contents are useful for researchers both in academia and industry working in interdisciplinary sectors.Table of ContentsA Brief Review of Some Swarming Models using Stochastic Differential Equations.- Copula-based estimation of Value at Risk for the portfolio problem.- An Overview of Exact Solution Methods for Guaranteed Minimum Death Benefit Options in Variable Annuities.- Determinantal reinforcement learning with techniques to avoid poor local optima.- Surface Denoising based on Normal Filtering in a Robust Statistics Framework.- Mathematical Modeling and Inverse Problem Approaches for Functional.- Clothing Design based on Thermal Mechanism.- Unique continuation on a sphere for Helmholtz equation and its numerical treatments.- Notes on Backward Stochastic Differential Equations for Computing XVA.

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Book SynopsisThis book features selected papers from the International Conference on Communication and Applied Technologies (ICOMTA 2021), jointly organized by Universidad del Rosario (Bogotá, Colombia); the University of Vigo (Galicia, Spain); the University of Santiago de Compostela-Equipo de Investigaciones Políticas (Galicia, Spain); the University of A Coruña (Galicia, Spain); and the Information and Technology Management Association (ITMA), during September 2021. It covers recent advances in the field of digital communication and processes digital social media, software, big data, data mining, and intelligent systems.Table of Contents

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Book SynopsisThis book includes high-quality papers presented at 15th International Conference on Information Technology and Applications (ICITA 2021), held in Dubai, UAE during 13 – 14 November 2021. The book presents original research work of academics and industry professionals to exchange their knowledge of the state-of-the-art research and development in information technology and applications. The topics covered in the book are cloud computing, business process engineering, machine learning, evolutionary computing, big data analytics, internet of things and cyber-physical systems, information and knowledge management, computer vision and image processing, computer graphics and games programming, mobile computing, ontology engineering, software and systems modelling, human computer interaction, online learning / e-learning, computer networks, and web engineering.Table of ContentsA convolutional neural network for artifacts detection in EEG data.- Real-World Protein Particle Network Reconstruction Based on Advanced Hybrid Features.- Improving Coronavirus (COVID-19) Diagnosis using Deep Transfer Learning.- Beard and Hair Detection, Segmentation and Changing Color Using Mask R-CNN.- Transfer Learning Method with Deep Residual Network for Covid-19 Diagnosis Using Chest Radiographs images.- Deep Learning Approach for COVID-19 Diagnosis Using X-Ray Images.- A hybrid approach for classifying Parkinson’s Disease from brain MRI.- Analysis of Generative Adversarial Networks for Data Driven Inverse Airfoil Design.- Deep Oversampling Technique for 4-level Acne Classification in Imbalanced Data.- Decentralisation of FinTech Business Models.- Dynamic Offloading in Fog Computing: A Survey.- A Vietnamese Festival Preservation Application.- Knowledge Models For Software Testing In Robotics.- Performance Enhancement of SAC-OCDMA System Using an Identity Row Shifting Matrix Code.- Malware Prediction using using LSTM Networks.- Personalised Combination of Multi-source Data for User Profiling.- The teaching of technical design in technical courses in Computer Graphics at Federal Institutes of Education, Science and Technology.

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Book SynopsisThis book collects select contributions presented at the International Conference on Importance of Statistics in Global Emerging (ISGES 2020) held at the Department of Mathematics and Statistics, University of Pune, Maharashtra, India, from 2–4 January 2020. It discusses recent developments in several areas of statistics with applications of a wide range of key topics, including small area estimation techniques, Bayesian models for small areas, ranked set sampling, fuzzy supply chain, probabilistic supply chain models, dynamic Gaussian process models, grey relational analysis and multi-item inventory models, and more. The possible use of other models, including generalized Lindley shared frailty models, Benktander Gibrat risk model, decision-consistent randomization method for SMART designs and different reliability models are also discussed. This book includes detailed worked examples and case studies that illustrate the applications of recently developed statistical methods, making it a valuable resource for applied statisticians, students, research project leaders and practitioners from various marginal disciplines and interdisciplinary research. Table of ContentsX. Chen and B. Nandram, Bayesian Order-Restricted Inference of Multinomial Counts from Small Areas.- L. Chen and B. Nandram, A Hierarchical Bayesian Beta-Binomial Model for Sub-Areas.- P. Anjoy and H. Chandra, Hierarchical Bayes Prediction of Survey-Weighted Small Area Proportions.- E. Zamanzade1 and M. Mahdizadeh, Efficiency of Ranked Set Sampling Design in Goodness of Fit Tests for Cauchy Distribution.- M. R. Bhosale1, R. Latpate and S. Gitte, Fuzzy Supply Chain Newsboy Problem Under Lognormal Distributed Demand for Bakery Products.- S. Kurade, R. Latpate and D. Hanagal, Probabilistic Supply Chain Models with Partial Backlogging for Deteriorating Items.- P. Ranjan and M. Harshvardhan, The Evolution of Dynamic Gaussian Process Model with Applications to Malaria Vaccine Coverage Prediction.- K. Uniyal1, G. Chandra, R. U. Khan and Y. P. Singh, Grey Relational Analysis for the Selection of Potential Isolates of Alternaria Alternata of Poplar.- Nidhi D. Raykundaliya and Dharmesh P. Raykundaliya.- Decision making for Multi-items Inventory Models.- A. Pandey, David D. Hanagal, S. Tyagi and P. Gupta, Modeling Australian Twin Data using Generalized Lindley Shared Frailty Models, K. Jain and H. S. Kapoor, Ultimate Ruin Probability for Benktander Gibrat Risk Model, K. K. Mahajan, S. Arora and A. Gaur, Test of Homogeneity of Scale Parameters Based on Function of Sample Quasi Ranges.- T. Dai and Sanjay Shete, A Bayesian Response-Adaptive, Covariate-Balanced and Q-learning-Decision-consistent Randomization Method for SMART Designs.- J. Sedransk, An introduction to Bayesian Inference for Finite Population Characteristics.- S. C. Malik, Reliability Measures of Repairable Systems with Arrival Time of Server.- P. V. Pandit and S. Joshi, Stress-Strength Reliability estimation for Multi-component system Based on Upper Record Values under New Weibull-Pareto Distribution.- V. S. Vaidyanathan and H. Bakouch, Record Values and Associated Inference on Muth Distribution.- Shalabh, Statistical Linear Calibration in Data with Measurement Errors.

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Book SynopsisThis book gathers the proceedings of the Seventh International Conference on Computational Science and Technology (ICCST 2021), held in Labuan, Malaysia, on 28–29 August 2021. The respective contributions offer practitioners and researchers a range of new computational techniques and solutions, identify emerging issues, and outline future research directions, while also showing them how to apply the latest large-scale, high-performance computational methods. Table of ContentsChapter 1: Optimizing the Financial Efficiency of Logistics Companies with Data Envelopment Analysis ModelLee Pei Fun, Lam Weng Siew and Lam Weng HoeChapter 2: T Research Output to Industry Use: A Readiness Study for Topic Modelling with Sentiment AnalysisJih Soong Tan and Wai Chong ChiaChapter 3: Human Motion Recognition Based on Kinect Sensor and Leap Motion ControllerChun Yung Wong, Giap Weng Ng and Ag Asri Ag IbrahimChapter 4: A Review on the Hyperparameters Used in Machine Learning Approaches for Classifying Paddy Rice FieldLerryson Jomin and Rayner AlfredChapter 5: Cloud Database in Mobile Augmented RealityNurul Farahin Hikmatullah Darwisal, Ng Giap Weng and Angeline Lee Ling SingChapter 6: A Brief Comparative of Indoor Positioning SystemMohd Aidil Safwan, Giap Weng Ng and Siti Hasnah TanalolChapter 7: Application of Speech Augmented Reality for Sabah Tourism IndustryRianty Saimon, Giap Weng Ng and Siti Hasnah TanalolChapter 8: Analysis on the Financial Performance of Companies with TOPSIS Model Lam Weng Siew, Lam Weng Hoe and Lee Pei FunChapter 9: Evaluation of the Financial Distress Level of Construction Companies in Malaysia using Z-score ModelLiew Kah Fai, Lam Weng Siew and Lam Weng HoeChapter 10: Mathematical Model for Scheduling Problems: A Compatibility Test on University Course Timetabling ProblemNur Aidya Hanum Aizam, Zuliyana Farahi Ismail, Candy Loh Siew YenChapter 11: Vision-based Dynamic Hand Gesture Recognition Techniques and Applications: A ReviewNurfazlin Muhamad Feizal Franslin and Giap Weng NgChapter 12: A Review on Statistical and Machine Learning Approaches to Forecasting the Occurrence of Covid-19 Positive CasesVelentine Jaulip and Rayner Alfred

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Book SynopsisThis book offers a unique new look at the familiar quantification theory from the point of view of mathematical symmetry and spatial symmetry. Symmetry exists in many aspects of our life—for instance, in the arts and biology as an ingredient of beauty and equilibrium, and more importantly, for data analysis as an indispensable representation of functional optimality. This unique focus on symmetry clarifies the objectives of quantification theory and the demarcation of quantification space, something that has never caught the attention of researchers.Mathematical symmetry is well known, as can be inferred from Hirschfeld’s simultaneous linear regressions, but spatial symmetry has not been discussed before, except for what one may infer from Nishisato’s dual scaling. The focus on symmetry here clarifies the demarcation of quantification analysis and makes it easier to understand such a perennial problem as that of joint graphical display in quantification theory. The new framework will help advance the frontier of further developments of quantification theory. Many numerical examples are included to clarify the details of quantification theory, with a focus on symmetry as its operational principle. In this way, the book is useful not only for graduate students but also for researchers in diverse areas of data analysis.Table of ContentsOptimality and Symmetry.- Examples of Quantification.- Constraints on Quantification.- Quantification Procedures.- Mathematical Symmetry.- Data Format and Information.- Space Theory and Symmetry.

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Book SynopsisThis book discusses recent theoretical developments in agglomerative hierarchical clustering. The general understanding of agglomerative hierarchical clustering is that its theory was completed long ago and there is no room for further methodological studies, at least in its fundamental structure. This book has been planned counter to that view: it will show that there are possibilities for further theoretical studies and they will be not only for methodological interests but also for usefulness in real applications. When compared with traditional textbooks, the present book has several notable features. First, standard linkage methods and agglomerative procedure are described by a general algorithm in which dendrogram output is expressed by a recursive subprogram. That subprogram describes an abstract tree structure, which is used for a two-stage linkage method for a greater number of objects. A fundamental theorem for single linkage using a fuzzy graph is proved, which uncovers several theoretical features of single linkage. Other theoretical properties such as dendrogram reversals are discussed. New methods using positive-definite kernels are considered, and some properties of the Ward method using kernels are studied. Overall, theoretical features are discussed, but the results are useful as well for application-oriented users of agglomerative clustering. Table of ContentsIntroduction.- Linkage Methods and Algorithms.- Theory of the Single Linkage Method.- Positive-Definite Kernels in Agglomerative Hierarchical Clustering.- Some Other Topics in Agglomerative Hierarchical Clustering.- Miscellanea.

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Book SynopsisThis is the first book of two volumes covering the basics of statistical methods and analysis. Significant topics include concepts of research and data analysis, descriptive statistics, probability and distributions, correlation and regression, and statistical inference. The book includes useful examples and exercises as well as relevant case studies for proper implementation of the discussed tools. This book will be a valuable text for undergraduate students of statistics, management, economics, and psychology, wanting to gain basic understanding of statistics and the usage of its various concepts. Table of Contents1. Concepts in Research and Data Analysis.- 2. Descriptive Statistics.- 3. Probability and Distributions.- 4. Correlation and Regression.- 5. Statistical Inference.

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Book SynopsisThe book presents high-quality, peer-reviewed papers from the FICR International Conference on Rising Threats in Expert Applications and Solutions 2022 organized by IIS (Deemed to be University), Jaipur, Rajasthan, India, during January 7–8, 2022. The volume is a collection of innovative ideas from researchers, scientists, academicians, industry professionals, and students. The book covers a variety of topics, such as expert applications and artificial intelligence/machine learning; advance web technologies such as IoT, big data, cloud computing in expert applications; information and cyber security threats and solutions, multimedia applications in forensics, security and intelligence; advancements in app development; management practices for expert applications; and social and ethical aspects in expert applications through applied sciences.Table of ContentsA Novel Approach for Detecting Online Malware Detection Using Performance Metrics Using LSTMRNN and BRNN Based Recurrent Neural Network in Cloud Environment.- Analytical Study of Augmented Reality Simulation System Application on Paintings.- A New Model SATV for Sentiment Analysis of Hinglish Sentences.- Characterization of Molecular Dynamic Trajectory using K-means clustering.- Data Warehouse Modernization using Document-Oriented ETL Framework for Real Time Analytics.- Opinion Mining for Breast Cancer Disease Using Apriori and K-Modes Clustering Algorithm.- Deep learning approach based on ADASYN for detection of Web attacks in the CICIDS2017 dataset.- FUNDUS and OCT Image Classification using Deep Learning Techniques.- A Secure Framework for the Deployment of Microservices using Cloud Container Technology.- Time Series Analytics – A fuel to Data Science in forecasting Analytics for time stamped data.- Sparse Autoencoder and Deep learning based Framework for Multi-label Classification of Chronic Diseases.- A Comparative study of different machine learning based feature extraction techniques in bacterial image classification.- Cloud removal and satellite image reconstruction using Deep Learning based Image inpainting approaches.- A Novel Cipher Technique using Substitution and Transposition Methods.- Edge Computing: State of Art with Current challenges and future opportunities.- Deep Learning and Machine Learning Approaches for the Classification of Personality Traits: Review.- Two Level Priority Task Scheduling Algorithm For Real Time IoT Based Storage Condition Assessment System.- Intrusion Detection of Internet of Things Botnet Attacks using Data Mining Technique.- Code Tamper-proofing using Return Oriented Programming in IoT Devices.- Conversion of Punjabi Sign Language using Animation.- Deep Learning based Classification of Microscopic Fungal Images.- Application of Artificial Neural Network and Multiple Linear Regression in Predicting Consumer Attitude towards Use of Mobile Wallet.- Securing Unauthorized Access to Cloud Data Storage.- A Survey on Water Usage Optimization using Artificial Intelligence in Agricultural Sector.- The Role of Artificial Neural Network in Word Sense Disambiguation (WSD) - A Survey.- Covid-19 Challenges for digital education and critical analysis.- Cloud Computing: Concepts and Techniques with Service Provider Analysis.- Analyzing Frequency Rhythms in Posed and Spontaneous Emotions Using Time- Intensity of EEG Signals.- Improving Classification Performance Using The Semi-Pivoted QR approximation algorithm.- Comparative Study on Video Based Face Recognition Methods.- An Innovative Framework based Algorithmic Approach for Object Detection.- Smart Bus Concept of COVID-19 Prevention using Face-Bio Keys.- Data Integration Solutions - A Key to the Cloud Migration.- A Proposed Structure, primarily based totally on Cloud Computing for Enhancing Electronic Commerce Applications.- Deep Learning and Internet of Things (IoT) Based Irrigation System for Cultivation of Paddy Crop.- Review of Machine Learning Techniques for Analysis of Medical Data Sets.- Incorporating contextual information in prediction based word embedding models.- Distributed Cloud Data Center for Efficient Services in Logistics Management.- Plants Disease Detection using Image Processing using Tensor flow: A boon to agriculture.- Data Visualisation using Self-Organising Maps.- SQL Injection Attacks, Detection Techniques on Web Application Databases.- Deep Learning Based Brain Tumor Segmentation: Recent Updates.- Mobile Application for Smart Agriculture in India: "An Analytical Approach".- Offline handwritten character recognition of Gujarati characters using convolutional neural network.- Forecasting Solar Energy on Time Frame: A Review.- Framework for Faster Index Search.- Image Classification by Optimized Convolution Neural Networks.- Benefits of Mining Fashion Data.- Current state of Engineering Education in India: Student Teacher scenario in online mode.- Estimating Blockchain using Time-series forecasting ARIMA.- Analyzing Online Reviews of Customers using Machine Learning Techniques.- Assessing between-group differences in implementation of Lean Six Sigma management conception.- Boosting Blockchain Mechanism using Cryptographic Algorithm in WSN.- Virtual Try-On using Augmented Reality.- Fractures Classification and Detection Using DIP And ML.- Sentiment Analysis in Social Media Website.- Artificial Intelligence based Techniques for Underwater Image Restoration and Enhancement.-A rapid computing technology on profound computing era with quantum computing.- Covid-19: Comparison of Time series forecasting models and Hybrid ARIMA- ANN.- YOLOV5 and Morphological Hat Transform Based Traffic Sign Recognition.- Optimizer Based Performance Study of Block Wise Fine-Tuning AlexNet.- Comparative study on Image Feature Extraction Techniques.- Cyber Security Threat and it's Global Perspective.- Detecting Cheating in Online Assessments using Face Recognition and Head Pose Estimation.- Opinion System of Real-Time Emotions from Human Interaction incoporated in Class using Deep Learning.- A Study on Automatic Detection of Alzheimer’s disease using Multimodalities.- IoT enabled smart tourism (IoTeST): tourism service dimension.- An Innovative Approach towards Designing Efficient K-Nearest Neighbour Algorithm.- Latest trends in Multi-Modality Medical Image Fusion: A Generic Review.- Comparison Study of Classification Techniques for Predicting Performance of Students using Weka Environment.- Multimodal Autism Detection in Children through Facial and Speech Emotion.- A Contemporary Learning on Smart Devices for Smart Metropolis.- Malpractice Detection in Online Assessment using Eye Gaze Tracking and Object Detection.- Big Data f5 Load Balancer with ChatBots Framework.- Keylogger awareness and use in Cyber Forensics.

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Book SynopsisAs a comprehensive textbook in survey sampling, this book discusses the inadequacies of classic, designed-based inferential procedures and provides alternative approaches in the form of model formulations, model-design-based procedures of analysis, inference and interpretation. The book focuses on a wide range of topics which included Bayesian and Empirical Bayesian approaches, complex procedures of stratification, clustering, sampling in multi stages and phases, linear and non-linear estimation of parameters, small area estimation by spatial and chronological modelling, network and adaptive sampling methods and more. The book includes detailed case studies and exercises, making it valuable for students of statistics, specifically survey sampling. Table of Contents1. Meaning and purpose of Survey Sampling.- 2. Inference in Survey Sampling.- 3. Sampling with Varying Probabilities.- 4. Fixing the size of an Equal Probability Sample.- 5. Adjusting Unit-Nonresponse by weighting & Tackling Item-Nonresponse by Imputation.- 6. Randomized Response & Indirect Survey Techniques.- 7. Super-Population Modeling. Model-Assisted Approach.Asymptotics.- 8. Prediction Approach: Robustness, Bayesian Methods, Empirical Bayes.- 9. Small Area Estimation & Developing Small Domain Statistics.- 10. Estimation of non-linear Parametric functions.- 11. Permanent random numbers, Poisson Sampling and Collocated Sampling.- 12. Network and Adaptive Sampling.- 13. Fixing size of a sample in Complex Strategies.- 14. Inadequate and Multiple Frame Data and Conditional Inference.- 15. Study of Analytical Surveys.- An Epilogue.- References.

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Book SynopsisThis textbook explains the fundamentals of Markov Chain Monte Carlo (MCMC) without assuming advanced knowledge of mathematics and programming. MCMC is a powerful technique that can be used to integrate complicated functions or to handle complicated probability distributions. MCMC is frequently used in diverse fields where statistical methods are important – e.g. Bayesian statistics, quantum physics, machine learning, computer science, computational biology, and mathematical economics. This book aims to equip readers with a sound understanding of MCMC and enable them to write simulation codes by themselves. The content consists of six chapters. Following Chap. 2, which introduces readers to the Monte Carlo algorithm and highlights the advantages of MCMC, Chap. 3 presents the general aspects of MCMC. Chap. 4 illustrates the essence of MCMC through the simple example of the Metropolis algorithm. In turn, Chap. 5 explains the HMC algorithm, Gibbs sampling algorithm and Metropolis-Hastings algorithm, discussing their pros, cons and pitfalls. Lastly, Chap. 6 presents several applications of MCMC. Including a wealth of examples and exercises with solutions, as well as sample codes and further math topics in the Appendix, this book offers a valuable asset for students and beginners in various fields. Table of ContentsChapter 1: Introduction.- Chapter 2: What is the Monte Carlo method?.- Chapter 3: General Aspects of Markov Chain Monte Carlo.- Chapter 4: Metropolis Algorithm.- Chapter 5: Other Useful Algorithms.- Chapter 6: Applications of Markov Chain Monte Carlo.

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Book SynopsisThis book provides expository derivations for moments of a family of pseudo distributions, which is an extended family of distributions including the pseudo normal (PN) distributions recently proposed by the author. The PN includes the skew normal (SN) derived by A. Azzalini and the closed skew normal (CSN) obtained by A. Domínguez-Molina, G. González-Farías, and A. K. Gupta as special cases. It is known that the CSN includes the SN and other various distributions as special cases, which shows that the PN has a wider variety of distributions. The SN and CSN have symmetric and skewed asymmetric distributions. However, symmetric distributions are restricted to normal ones. On the other hand, symmetric distributions in the PN can be non-normal as well as normal. In this book, for the non-normal symmetric distributions, the term “kurtic normal (KN)” is used, where the coined word “kurtic” indicates “mesokurtic, leptokurtic, or platykurtic” used in statistics. The variety of the PN was made possible using stripe (tigerish) and sectional truncation in univariate and multivariate distributions, respectively. The proofs of the moments and associated results are not omitted and are often given in more than one method with their didactic explanations. Table of ContentsThe Sectionally Truncated Normal Distribution.- Normal Moments Under Stripe Truncation and the Real-Valued Poisson Distribution.- The Basic Parabolic Cylinder Distribution and its Multivariate Extension.- The Pseudo-Normal (PN) Distribution.- The Kurtic-Normal (KN) Distribution.- The Normal-Normal (NN) Distribution.- The Decompositions of the PN and NN Distributed Variables.- The Truncated Pseudo-Normal (TPN) and Truncated Normal-Normal (TNN) Distributions.- The Student t- and Pseudo-t (PT) Distributions: Various Expressions of Mixtures.- Multivariate Measures of Skewness and Kurtosis.

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Book SynopsisThis book provides empirical applications of macroeconometric methods through discussions on key issues in the Indian economy. It deals with issues of topical relevance in the arena of macroeconomics. The aim is to apply time series and financial econometric methods to macroeconomic issues of an emerging economy such as India. The data sources are given in each chapter, and students and researchers may replicate the analyses.The book is divided into three parts—Part I: Macroeconomic Modelling and Policy; Part II: Forecasting the Indian Economy and Part III: Business Cycles and Global Crises. It provides a holistic understanding of the techniques with each chapter delving into a relevant issue analysed using appropriate methods—Chapter 1: Introduction; Chapter 2: Macroeconomic Modelling and Bayesian Methods; Chapter 3: Monetary Policy Framework in India; Chapter 4: Determinants of Yields on Government Securities in India; Chapter 5: Monetar y Transmission in the Indian Economy; Chapter 6: India’s Bilateral Export Growth and Exchange Rate Volatility: A Panel GMM Approach; Chapter 7: Aggregate and Sectoral Productivity Growth in the Indian Economy: Analysis and Determinants; Chapter 8: Forecasting the INR/USD Exchange Rate: A BVAR Framework; Chapter 9: Forecasting India’s Inflation in a Data-Rich Environment: A FAVAR Study; Chapter 10: A Structural Macroeconometric Model for India; Chapter 11: International Synchronization of Growth Rate Cycles: An Analysis in Frequency Domain; Chapter 12: Inter-Linkages Between Asian and U.S. Stock Market Returns: A Multivariate GARCH Analysis; Chapter 13: The Increasing Synchronization of International Recessions.Since the selection of issues is from macroeconomic aspects of the Indian economy, the book has wide applications and is useful for students and researchers of fields such as applied econometrics, time series econometrics, financial econometrics, forecasting methods and macroeconomics.Table of ContentsChapter 1: IntroductionPart I: Macroeconomic Modelling and Policy Chapter 2: Macroeconomic Modelling and Bayesian MethodsChapter 3: Monetary Policy Framework in India Chapter 4: Determinants of Yields on Government Securities in India Chapter 5: Monetary Transmission in the Indian Economy Chapter 6: India’s Bi-lateral Export Growth and Exchange Rate Volatility: A Panel GMM Approach Chapter 7: Aggregate and Sectoral Productivity in the Indian Economy: Analysis and Determinants Part II: Forecasting the Indian Economy Chapter 7: Forecasting the INR/USD Exchange Rate: A BVAR Framework Chapter 8: Forecasting India’s Inflation in a Data Rich Environment: A FAVAR Study Chapter 9: A Structural Macroeconometric Model for India Part III: Business Cycles and Global Crises Chapter 10: International Synchronization of Growth Rate Cycles: An Analysis in Frequency Domain Chapter 11: Inter-linkages between Asian and U.S. Stock Market Returns: A Multivariate GARCH Analysis Chapter 12: The Increasing Synchronization of International Recessions

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Book SynopsisThis textbook has been developed from the lecture notes for a one-semester course on stochastic modelling. It reviews the basics of probability theory and then covers the following topics: Markov chains, Markov decision processes, jump Markov processes, elements of queueing theory, basic renewal theory, elements of time series and simulation. Rigorous proofs are often replaced with sketches of arguments — with indications as to why a particular result holds, and also how it is connected with other results — and illustrated by examples. Wherever possible, the book includes references to more specialised texts containing both proofs and more advanced material related to the topics covered.Table of ContentsBasics of Probability Theory; Markov Chains; Markov Decision Processes; The Exponential Distribution and Poisson Process; Jump Markov Processes; Elements of Queueing Theory; Elements of Renewal Theory; Elements of Time Series; Elements of Simulation.

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Book SynopsisThis textbook has been developed from the lecture notes for a one-semester course on stochastic modelling. It reviews the basics of probability theory and then covers the following topics: Markov chains, Markov decision processes, jump Markov processes, elements of queueing theory, basic renewal theory, elements of time series and simulation. Rigorous proofs are often replaced with sketches of arguments — with indications as to why a particular result holds, and also how it is connected with other results — and illustrated by examples. Wherever possible, the book includes references to more specialised texts containing both proofs and more advanced material related to the topics covered.Table of ContentsBasics of Probability Theory; Markov Chains; Markov Decision Processes; The Exponential Distribution and Poisson Process; Jump Markov Processes; Elements of Queueing Theory; Elements of Renewal Theory; Elements of Time Series; Elements of Simulation.

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Book SynopsisThis textbook systematically presents fundamental methods of statistical analysis: from probability and statistical distributions, through basic concepts of statistical inference, to a collection of methods of analysis useful for scientific research. It is rich in tables, diagrams, and examples, in addition to theoretical justification of the methods of analysis introduced. Each chapter has a section entitled “Exercises and Problems” to accompany the text. There are altogether about 300 exercises and problems, answers to the selected problems are given. A section entitled “Proof of the Results in This Chapter” in each chapter provides interested readers with material for further study.Trade Review"It offers a sound presentation of concepts at either the undergraduate or first-year graduate level, including a moderate amount of calculus ... A resulting strength of the text is that students are presented with a deeper understanding of analytic methods."Journal of the American Statistical Association"The main merit of this book is that the author gives for every model the corresponding method of hypothesis testing: whether the given assumption holds. The abundance of numerical examples, diagrams, tables helps the reader to analyze the problems arisen not only from biology, but from social sciences, too ... Due to the interesting applications the book can be recommended also to lecturers of theoretical statistics to demonstrate the force of the theory."Acta Scientiarum MathematicarumTable of ContentsDescriptive Statistics; Probability; Random Variables; Probability Distributions; Statistical Inference -- Interval Estimation; Hypothesis Testing -- Fundamental Concepts; Testing Hypotheses Concerning Population Means and Population Proportions; The Chi-Square Test; Linear Regression; Correlation; Multiple Regression and Correlation; One-Way Analysis of Variance; Two-Way Analysis of Variance -- Fixed Effects Model; Two-Way Analysis of Variance Random-Effects Model and Mixed Model; Design of Experiment; Analysis of Covariance; Non-Parametric Statistics.

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Book SynopsisIn this book, an integrated introduction to statistical inference is provided from a frequentist likelihood-based viewpoint. Classical results are presented together with recent developments, largely built upon ideas due to R.A. Fisher. The term “neo-Fisherian” highlights this.After a unified review of background material (statistical models, likelihood, data and model reduction, first-order asymptotics) and inference in the presence of nuisance parameters (including pseudo-likelihoods), a self-contained introduction is given to exponential families, exponential dispersion models, generalized linear models, and group families. Finally, basic results of higher-order asymptotics are introduced (index notation, asymptotic expansions for statistics and distributions, and major applications to likelihood inference).The emphasis is more on general concepts and methods than on regularity conditions. Many examples are given for specific statistical models. Each chapter is supplemented with problems and bibliographic notes. This volume can serve as a textbook in intermediate-level undergraduate and postgraduate courses in statistical inference.

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Book SynopsisThis book teaches modern Markov chain Monte Carlo (MC) simulation techniques step by step. The material should be accessible to advanced undergraduate students and is suitable for a course. It ranges from elementary statistics concepts (the theory behind MC simulations), through conventional Metropolis and heat bath algorithms, autocorrelations and the analysis of the performance of MC algorithms, to advanced topics including the multicanonical approach, cluster algorithms and parallel computing. Therefore, it is also of interest to researchers in the field. The book relates the theory directly to Web-based computer code. This allows readers to get quickly started with their own simulations and to verify many numerical examples easily. The present code is in Fortran 77, for which compilers are freely available. The principles taught are important for users of other programming languages, like C or C++.Table of ContentsSampling, Statistics and Computer Code; Error Analysis for Independent Random Variables; Markov Chain Monte Carlo; Error Analysis for Markov Chain Data; Advanced Monte Carlo; Parallel Computing; Conclusions, History and Outlook.

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Book SynopsisStatistical distributions are fundamental to Statistical Science and are a prime indispensable tool for its applications. This monograph is the first to examine an important but somewhat neglected field — univariate continuous distribution on a bounded domain, excluding the beta distribution. It provides an elementary but thorough discussion of “novel” contributions developed in recent years, such as the two-sided power, generalized trapezoidal and generalized Topp and Leone distributions, among others. It discusses a general framework for constructing two-sided distributions and some of its properties. It contains a comprehensive chapter on the triangular distribution as well as a chapter on earlier extensions not emphasized in existing literature. Special attention is given to estimation, in particular, non-standard maximum likelihood procedures. The applications are drawn mainly from the econometric and engineering domains.Table of ContentsThe Triangular Distribution; Some Early Extensions of the Triangular Distribution; The Standard Two-Sided Power Distribution; The Two-Sided Power Distribution; The Generalized Trapezoidal Distribution; Uneven Two-Sided Power Distributions; The Reflected Generalized Topp and Leone Distribution; A Generalized Framework for Two-Sided Distributions.

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Book SynopsisStein's startling technique for deriving probability approximations first appeared about 30 years ago. Since then, much has been done to refine and develop the method, but it is still a highly active field of research, with many outstanding problems, both theoretical and in applications. This volume, the proceedings of a workshop held in honour of Charles Stein in Singapore, August 2003, contains contributions from many of the mathematicians at the forefront of this effort. It provides a cross-section of the work currently being undertaken, with many pointers to future directions. The papers in the collection include applications to the study of random binary search trees, Brownian motion on manifolds, Monte-Carlo integration, Edgeworth expansions, regenerative phenomena, the geometry of random point sets, and random matrices.Table of ContentsZero Biasing in One and Higher Dimensions, and Applications (L Goldstein & G Reinert); Poisson Limit Theorems for the Appearances of Attributes (O Chryssaphinou et al.); Normal Approximation in Geometric Probability (M D Penrose & J E Yukich); Stein's Method, Edgeworth's Expansions and a Formula of Barbour (V Rotar); Stein's Method for Compound Poisson Approximation (A Xia); The Independence Number for Minimal Spanning Trees (S Lee & Z Su); Strong Memoryless Times (T Erhardsson); Multivariate Poisson-Binomial Approximation (A D Barbour); A BerryEsseen Bound for the t-Statistic (Q-M Shao); An Application of Stein's Method to Maxima in Hypercubes (Z D Bai et al.); Expected Length of an MST (J A Fill & J M Steele); Spectra of Random Matrices with Martingale Structure (F G�tze & A Tikhomirov); Characterization of Brownian Motion on Manifolds (E P Hsu); On Some Randomized Quadrature Rules (W-L Loh); Matrix Correlation Statistics (A D Barbour & L Chen); Random Binary Search Trees (L Devroye).

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Book SynopsisA common theme in probability theory is the approximation of complicated probability distributions by simpler ones, the central limit theorem being a classical example. Stein's method is a tool which makes this possible in a wide variety of situations. Traditional approaches, for example using Fourier analysis, become awkward to carry through in situations in which dependence plays an important part, whereas Stein's method can often still be applied to great effect. In addition, the method delivers estimates for the error in the approximation, and not just a proof of convergence. Nor is there in principle any restriction on the distribution to be approximated; it can equally well be normal, or Poisson, or that of the whole path of a random process, though the techniques have so far been worked out in much more detail for the classical approximation theorems.This volume of lecture notes provides a detailed introduction to the theory and application of Stein's method, in a form suitable for graduate students who want to acquaint themselves with the method. It includes chapters treating normal, Poisson and compound Poisson approximation, approximation by Poisson processes, and approximation by an arbitrary distribution, written by experts in the different fields. The lectures take the reader from the very basics of Stein's method to the limits of current knowledge.Trade Review"All the lectures have been given by eminent experts working in the area of Stein's method and provide details and unsolved problems along with a list of very useful references. These lectures will be very valuable for both experts and researchers wishing to explore the powerful approximation method proposed by Charles Stein."Mathematical ReviewsTable of ContentsStein's Method for Normal Approximation (L Chen & Q-M Shao); Stein's Method for Poisson and Compound Poisson Approximation (T Erhardsson); Stein's Method and Poisson Process Approximation (A Xia); Three General Approaches to Stein's Method (G Reinert).

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