Probability and statistics Books
Taylor & Francis Ltd Statistical Inference Based on Divergence Measures
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£43.69
Taylor & Francis Ltd Handbook of Stochastic Analysis and Applications
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£43.69
Taylor & Francis Ltd Multivariate Quality Control
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£43.69
Taylor & Francis Ltd White Noise Distribution Theory
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£43.69
Taylor & Francis Ltd Truncated and Censored Samples
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£43.69
Taylor & Francis Ltd Point Processes and Their Statistical Inference
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£45.99
Taylor & Francis Ltd A Primer in Probability
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£45.99
Taylor & Francis Ltd Robust Regression
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£43.69
Taylor & Francis Ltd Interface between Regulation and Statistics in Drug Development
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£43.69
Taylor & Francis Ltd Analyzing Spatial Models of Choice and Judgment
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£45.59
Taylor & Francis Ltd AIBased Metaheuristics for Information Security and Digital Media
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£77.99
Taylor & Francis Ltd Interpreting Statistics for Beginners
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£35.99
Taylor & Francis Ltd Optimal Decision Making in Operations Research
Book SynopsisThe book provides insights in the decision-making for implementing strategies in various spheres of real-world issues. It integrates optimal policies in various decisionmaking problems and serves as a reference for researchers and industrial practitioners. Furthermore, the book provides sound knowledge of modelling of real-world problems and solution procedure using the various optimisation and statistical techniques for making optimal decisions. The book is meant for teachers, students, researchers and industrialists who are working in the field of materials science, especially operations research and applied statistics. Table of Contents1. A New Version of the Generalized Rayleigh Distribution with Copula, Properties, Applications and Different Methods of Estimation 2. Expanding the Burr X Model: Properties, Copula, Real Data Modeling and Different Methods of Estimation 3. Transmuted Burr Type X Model with Applications to Life Time Data 4. Monitoring Patients Blood Level through Enhanced Control Chart 5. Goodness of Fit in Parametric and Non-parametric Econometric Models 6. Stochastic Models for Cancer Progression and its Optimal Programming for Control with Chemotherapy 7. A New Unrelated Question Model with Two Questions Per Card 8. Hybrid of Simple Model and a New Unrelated Question Model for Two Sensitive Characteristics 9. Hybrid of Crossed Model and a New Unrelated Question Model for Two Sensitive Characteristics 10. Modified Regression Type Estimator by Ingeniously Utilizing Probabilities for more Efficient Results in Randomized Response Sampling 11. Ratio and Regression Type Estimators for a New Measure of Coefficient of Dispersion Relative to the Empirical Mode 12. Class of Exponential Ratio Type Estimator for Population Mean in Adaptive Cluster Sampling 13. An Inventory Model for Substitutable Deteriorating Products under Fuzzy and Cloud Fuzzy Demand Rate 14. Co-ordinated Selling Price and Replenishment Policies for Duopoly Retailers under Quadratic Demand and Deteriorating Nature of Items15. Quadratic Programming Approach for the Optimal Multi-objective Transportation Problem 16. Analyzing Multi-Objective Fixed-Charge Solid Transportation Problem under Rough and Fuzzy-Rough Environments 17. Overall Shale Gas Water Management: A Neutrosophic Optimization Approach 18. Memory Effect on an EOQ Model with Price Dependant Demand and Deterioration 19. Optimality Conditions of an Unconstrained Imprecise Optimization Problem via Interval Order Relation 20. Power Comparison of Different Goodness of Fit Tests for Beta Generalized Weibull Distribution 21. On the Transmuted Modified Lindley Distribution: Theory and Applications to Lifetime Data 22. Adjusted Bias and Risk for Estimating Treatment Effect after Selection with an Application in Idiopathic Osteoporosis 23. Validity Judgement of an EOQ Model using Phi-coefficient 24. Uncertain Chance-Constrained Multi-Objective Geometric Programming Problem 25. Optimal Decision Making for the Prediction of Diabetic Retinopathy in Type 2 Diabetes Mellitus Patients
£49.39
Taylor & Francis Ltd Multidimensional Stationary Time Series
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£43.69
Taylor & Francis Ltd Interpreting Statistics for Beginners
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£128.25
Taylor & Francis Ltd Trace Environmental Quantitative Analysis
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£73.14
Taylor & Francis Ltd Applied Linear Regression for Longitudinal Data
Book SynopsisThis book introduces best practices in longitudinal data analysis at intermediate level, with a minimum number of formulas without sacrificing depths. It meets the need to understand statistical concepts of longitudinal data analysis by visualizing important techniques instead of using abstract mathematical formulas. Different solutions such as multiple imputation are explained conceptually and consequences of missing observations are clarified using visualization techniques. Key features include the following: Provides datasets and examples online Gives state-of-the-art methods of dealing with missing observations in a non-technical way with a special focus on sensitivity analysis Conceptualises the analysis of comparative (experimental and observational) studies It is the ideal companion for researchers and students in epidemiological, health, and social and behavioral sciences working with longitudinal studies without a mathematical backgrouTrade Review"Overall, the book is well written. It is clear and allows the reader understanding the main concepts behind models for longitudinal data analysis, with few effort from a technical viewpoint. The examples used to illustrate the methods covered in the textbook are numerous and also rather easy to follow. This helps the reader learn how to proceed with a full longitudinal data analysis."Maria Francesca Marino, University of Florence, Italy, The American Statistician, February 2024.Table of Contents1. Scientific Framework of Data Analysis 2. Revisiting and Shortcomings of Standard Linear Regression Models 3. An Introduction to the Analysis of Longitudinal Data 4. Model Building for Longitudinal Data Analysis 5. Analysis of a Pre/Post Measurement Design 6. Analysis of Longitudinal Life-Event Studies 7. Analysis of Longitudinal Experimental Studies
£87.39
Taylor & Francis Ltd Machine Learning
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£52.24
Taylor & Francis Ltd Engineering Design and Mathematical Modelling
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£43.69
Taylor & Francis Ltd Statistics and Machine Learning Methods for EHR Data
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£45.99
Taylor & Francis Ltd Applied Statistics in Social Sciences
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£104.50
Taylor & Francis Ltd Outstanding User Interfaces with Shiny
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£142.50
Taylor & Francis Ltd Dynamic Time Series Models using RINLA
Book SynopsisDynamic Time Series Models using R-INLA: An Applied Perspective is the outcome of a joint effort to systematically describe the use of R-INLA for analysing time series and showcasing the code and description by several examples. This book introduces the underpinnings of R-INLA and the tools needed for modelling different types of time series using an approximate Bayesian framework.The book is an ideal reference for statisticians and scientists who work with time series data. It provides an excellent resource for teaching a course on Bayesian analysis using state space models for time series.Key Features: Introduction and overview of R-INLA for time series analysis. Gaussian and non-Gaussian state space models for time series. State space models for time series with exogenous predictors. Hierarchical models for a potentially large set of time series. Dynamic modelling of stochastic volatility and spatio-Trade Review"This book will interest current R-users with a background in time series analyses who would like to expand their knowledge regarding INLA and its application with R-INLA package. This book also provides illustrative examples which can contribute to the understanding of the applications of these methods. This book can also benefit academic researchers who would like to apply these types of approaches in their fields." Sébastien Bailly, French National Center for Medical Research (INSERM), France, ISCB, May 2023 Table of ContentsPreface. 1. Bayesian Analysis. 2. A Review of INLA. 3. Modeling Univariate Time Series. 4. More Topics on DLMs with R-INLA. 5. Modeling Time Series with Exogenous Predictors. 6. Structural Time Series Decomposition using R-INLA. 7. Hierarchical DLM. 8. INLA for Multivariate Dynamic Models. 9. Modeling Binary Time Series. 10. Modeling Count Time Series. 11. Modeling Stochastic Volatility. 12. Comparison of R-INLA to Other Bayesian Alternatives. 13. Resources for the User.
£80.74
Taylor & Francis Ltd Introduction to Statistical Methods for Financial Models
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£45.99
Taylor & Francis Ltd Javascript for R
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£47.49
Taylor & Francis Ltd Javascript for R
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£118.75
Taylor & Francis Renminbi Exchange Rate Forecasting Routledge Advances in Risk Management
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£128.25
Taylor & Francis Ltd An Introduction to IoT Analytics
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£44.99
Taylor & Francis Ltd ProjectBased R Companion to Introductory Statistics A ProjectBased Approach Using R
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£128.25
Taylor & Francis Ltd An Introduction to IoT Analytics
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£109.25
Taylor & Francis Ltd Explanatory Model Analysis
Book SynopsisExplanatory Model Analysis Explore, Explain and Examine Predictive Models is a set of methods and tools designed to build better predictive models and to monitor their behaviour in a changing environment. Today, the true bottleneck in predictive modelling is neither the lack of data, nor the lack of computational power, nor inadequate algorithms, nor the lack of flexible models. It is the lack of tools for model exploration (extraction of relationships learned by the model), model explanation (understanding the key factors influencing model decisions) and model examination (identification of model weaknesses and evaluation of model''s performance). This book presents a collection of model agnostic methods that may be used for any black-box model together with real-world applications to classification and regression problems.Trade Review"The structure is well-conceived, with chapters consisting in five sections: intuition, method, example, pros and cons, and code snippets. I sense a teacher’s long experience behind these choices.The chapters contain good mathematical detail on the techniques discussed, but the theory is well balanced with examples and code.The visualizations are great. Often, the gist of a particular technique, and it’s practical, interpretive value, can be gleaned from the visualizations threading through the chapter, along with captions. The authors did a really nice job with this.The rationale for the book is well-described.The discussion of techniques seems both comprehensive (given my sense of the field) and helpfully specific, both at the instance and the dataset levels."-Jeff Webb, University of Utah"The authors are doing a very good job in addressing the potential readers, by providing a clean presentation and practical guidance on diagnostic graphical tools…Having an ‘intuition section’ at the beginning of each chapter is very useful."-Riccardo De Bin, University of Oslo"The book provides a unified presentation of model exploration, visualization, comparison and diagnostics of different machine learning algorithms…This book would be found useful by both students as well as practitioners who analyze their own data. Books including real data examples in R and in Python are needed in this area. (It) will serve as a reference, especially for analyses done with dalex or archivist R package (and )can serve as a textbook of data science courses in many fields including computer science, social sciences, economics and other."-Patricia Martinkova, Institute of Computer Science of the Czech Academy of Sciences"There are books that focus on prediction models, for example the element of statistical learning and an introduction to statistical learning but these are not focused on the evaluation of predictive models which is the main focus on the proposed book and its main advantage. As predictive models become very popular in the last years, such a book that focus on the evaluation of the models and model diagnostics can be very popular."-Ziv Shkedy, Data Science Institute, Hasselt University, Belgium'The book is clearly and consistently structured and well–written. The graphics are explained conceptually and mathematically. There are chapter sections on the pros and cons of what is proposed, where the authors are generally properly cautious and recommend a mixture of approaches.'- Antony Unwin, International Statistical Review, 2021 Volume 89, Issue 3"The structure is well-conceived, with chapters consisting in five sections: intuition, method, example, pros and cons, and code snippets. I sense a teacher’s long experience behind these choices. The chapters contain good mathematical detail on the techniques discussed, but the theory is well balanced with examples and code. The visualizations are great. Often, the gist of a particular technique, and it’s practical, interpretive value, can be gleaned from the visualizations threading through the chapter, along with captions. The authors did a really nice job with this. The rationale for the book is well-described. The discussion of techniques seems both comprehensive (given my sense of the field) and helpfully specific, both at the instance and the dataset levels." -Jeff Webb, University of Utah"The authors are doing a very good job in addressing the potential readers, by providing a clean presentation and practical guidance on diagnostic graphical tools…Having an ‘intuition section’ at the beginning of each chapter is very useful." -Riccardo De Bin, University of Oslo"The book provides a unified presentation of model exploration, visualization, comparison and diagnostics of different machine learning algorithms…This book would be found useful by both students as well as practitioners who analyze their own data. Books including real data examples in R and in Python are needed in this area. (It) will serve as a reference, especially for analyses done with dalex or archivist R package (and )can serve as a textbook of data science courses in many fields including computer science, social sciences, economics and other." -Patricia Martinkova, Institute of Computer Science of the Czech Academy of Sciences"There are books that focus on prediction models, for example the element of statistical learning and an introduction to statistical learning but these are not focused on the evaluation of predictive models which is the main focus on the proposed book and its main advantage. As predictive models become very popular in the last years, such a book that focus on the evaluation of the models and model diagnostics can be very popular." -Ziv Shkedy, Data Science Institute, Hasselt University, Belgium"We need to explore the models and learn about their behaviour. This book presents, explains, and summarises the techniques for doing so. Moreover, it provides code in R and Python for doing so. The methods have many similarities with those of sensitivity analysis developed within the Sensitivity Analysis of Model Output (SAMO) community. ... [M]any doctoral students, professional statisticians and researchers should ensure that they have access to it and know how to use its methods when dealing with highly complex functions in their data and model analysis." -Simon French, in the Journal of the Royal Statistics Society, Series A, June 2022"The book presents a valuable collection of methods for models’ exploration and diagnostics for various machine learning algorithms. It can be useful in the data and computer science courses for students and instructors, as well as for researchers and practitioners who need to analyze and interpret their statistical and machine learning models both of glass-box and blackbox kind. The book also serves as a great primary for applications of the R and Python software and their packages/libraries, so it is valuable in solving various problems of statistical prediction in various fields."-Stan Lipovetsky, in Technometrics, July 2022Table of ContentsI. Introduction 1. Introduction. 2. Model Development. 3. Do-it-yourself. 4. Datasets and models. II. Instance Level. 5. Introduction to Instance-level Exploration. 6. Break-down Plots for Additive Attributions. 7. Break-down Plots for Interactions. 8. Shapley Additive Explanations (SHAP) for Average Attributions. 9. Local Interpretable Model-agnostic Explanations (LIME). 10. Ceteris-paribus Profiles. 11. Ceteris-paribus Oscillations. 12. Local-diagnostics Plots. 13. Summary of Instance-level Exploration. III. Dataset Level. 14. Introduction to Dataset-level Exploration. 15. Model-performance Measures. 16. Variable-importance Measures. 17. Partial-dependence Profiles. 18. Local-dependence and Accumulated-dependence Profiles. 19. Residual Diagnostics Plots. 20. Summary of Model-level Exploration. IV. Use-cases. 21. FIFA 19.
£45.99
Taylor & Francis Ltd Renminbi Exchange Rate Forecasting
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£37.99
Taylor & Francis Ltd Random Matrices and NonCommutative Probability
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£142.50
Taylor & Francis Ltd Feynman Path Integrals in Quantum Mechanics and Statistical Physics
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£43.69
Taylor & Francis Candlestick Forecasting for Investments
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£128.25
Taylor & Francis Ltd Candlestick Forecasting for Investments
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£39.99
Taylor & Francis Ltd Random Matrices and NonCommutative Probability
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£54.14
Taylor & Francis Ltd Fundamentals of Causal Inference
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£76.42
Taylor & Francis Ltd Optimization of IntegerFractional Order Chaotic Systems by Metaheuristics and their Electronic Realization
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£51.99
Taylor & Francis Ltd Data Analytics Computational Statistics and Operations Research for Engineers
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£128.25
Taylor & Francis Ltd Advanced Survival Models
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£43.99
Taylor & Francis Ltd The Sequential Quadratic Hamiltonian Method
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£147.25
Taylor & Francis Ltd Data Mining and Exploration
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£128.25
Taylor & Francis Time Series Analysis of Discourse
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£21.99
Taylor & Francis Ltd Design Analysis of Clinical Trials for Economic Evaluation Reimbursement
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£43.69
Taylor & Francis Ltd Missing Data Analysis in Practice
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£45.99
Taylor & Francis Ltd Nonparametric Statistics on Manifolds and Their Applications to Object Data Analysis
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£43.69
Taylor & Francis Ltd Data Analysis with Competing Risks and Intermediate States
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£45.99