Probability and statistics Books

Book SynopsisAudio-Röhrenverstärker erfreuen sich wachsender Beliebtheit im Hochpreissegment. Die Verfügbarkeit moderner Bauelemente, neue Schaltungskonzepte und die Erfahrungen aus dem Transistorverstärkerbau lassen das Interesse an Neu- und Weiterentwicklungen von Röhrenverstärkern steigen. Elektronikentwicklung mit Hilfe des universellen Schaltungssimulationsprogramms SPICE bilden eine vollkommene Einheit. Das Buch, einzigartig in diesem Bereich, will die Entwickler und die zahlreichen Amateure mit SPICE zusammenbringen.Das Buch hat drei Schwerpunkte: die Modellierung von Verstärkerröhren, die Anwendung von SPICE für die Untersuchung von Schaltungskonzepten und die Vorstellung moderner Schaltungstechniken, die mit SPICE analysiert werden können.Table of ContentsSchaltungssimulation mit SPICE.- PC-Implementierungen von SPICE, das kostenfreie graphische LTSpice.- Die grundlegenden Schaltungsanalysen am Beispiel passiver Netzwerke.- Modellierung von Verstärkerröhren.- SPICE-Simulation von Röhrenverstärkerschaltungen.- SPICE-Simulationstechniken für Fortgeschrittene.- Rückkopplungen, Stabilität und Frequenzgangskorrekturen.

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Book SynopsisDie angewandte Mathematik ist die Grundlage sämtlicher Ingenieur- und Naturwissenschaften. Aber auch in vielen praxisorientierten technischen Berufen ist „Mathe“ ein wichtiges Werkzeug und die Voraussetzung für erfolgreiches Arbeiten und gute Resultate. Oft haben die Studierenden an Universitäten, Fachhochschulen sowie Ingenieur- und Technikerschulen große Schwierigkeiten, sich mit der wunderbar und manchmal so verblüffend in sich schlüssigen Materie anzufreunden, denn meistens wird das Thema Mathematik in der Lehre zwar sehr umfassend und allgemeingültig aber auch gleichzeitig staubtrocken und theoretisch rübergebracht. Aber auch für die Mathematik gilt: Man kann den Einstieg in dieses theoretische und abstrakte Thema sehr viel anschaulicher als in den meisten Lehrbüchern darstellen und das Ganze noch mit Humor und Cartoons würzen. Mathe kann auch witzig sein! Mit Humor und einer unkonventionellen Darstellungsweise analog zu „Keine Panik vor Statistik!“, „Keine Panik vor Mechanik!“ und „Keine Panik vor Thermodynamik!“ lässt sich für Viele ein einfacher Zugang zur Mathematik finden und eine Brücke zu den ernsteren und theoretischen Lehrbüchern schlagen...Table of ContentsGrundlagen.- Reelle Zahlen.- Elementare Funktionen.- Komplexe Zahlen.- Vektorrechnung.- Matrizen.- Differentialrechnung.- Integralrechnung.- Reihen.- Funktionen mehrerer Veränderlicher.

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Book SynopsisDie Finanzrisiken in der Assekuranz werden mit Hilfe der modernen Finanztheorie beleuchtet. Dazu wird mit dem Begriff der Duration die Zinssensitivität von Barwerten, Deckungskapital etc. untersucht. Anhand der mathematischen Risikobegriffe werden die modernen risikobasierten Solvenzanforderungen erläutert und ihre Beziehung zur Portfoliotheorie in der Ökonomie gezeigt. Zudem wird die Problematik der neuen risikobasierten Solvenznormen diskutiert. Die Entwicklung der Risiken über die Zeit wird anhand von stochastischen Prozessen erläutert. Daraus werden die bekannten Modelle zur Preisbestimmung von Optionen recht elementar und anschaulich begründet. So können Preis und Wirkung konkreter Absicherungen von Finanzrisiken am Markt aufgezeigt werden. Trade ReviewAus den Rezensionen: “... Das Buch bietet eine solide und grundlegende Einführung in die Welt der Finanzrisiken in der Assekuranz und kann Aktuaren, aber auch Studierenden im Bereich Versicherungsmathematik bzw. Finanzmathematik als Grundlagenlektüre empfohlen werden.” (in:risknet.de, 7. Oktober 2013)Table of ContentsTheorie und Wirklichkeit – Bewertungen - Duration, Konvexität und Dispersion – Zinssensitivität – Zinssensitivität von Leibrenten und Sterblichkeitsannahmen - Solvency II und die Aggregation verschiedener Risiken – Portfoliotheorie – Finanzmarktinstrumente - Stochastische Zinsmodelle – Glossar, Lösungen und Index

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Book SynopsisDas Lehrbuch führt anwendungsorientiert in alle zentralen Teilbereiche des Operations Research bzw. der quantitativen Unternehmensplanung ein: Behandelt werden lineare, ganzzahlige und nichtlineare Optimierungsansätze, Methoden der Projektplanung und Netzplantechnik, stochastische Modelle sowie nichtexakte Lösungsverfahren. Die jeweiligen Modelle und Methoden werden anhand zahlreicher betrieblicher Anwendungsbeispiele aus den Bereichen Produktion, Logistik, Marketing und Finanzwirtschaft illustriert. Entsprechende Übungsaufgaben mit ausführlichen Lösungen tragen zur Vertiefung des Stoffes bei und helfen bei der Vorbereitung auf Klausuren. Das Buch richtet sich an Lehrende und Studierende der Wirtschaftswissenschaften, der Wirtschaftsinformatik, der Ingenieurwissenschaften sowie verwandter Studiengänge, in denen grundlegende Kenntnisse quantitativer Methoden der Unternehmensplanung benötigt werden.Table of ContentsTeil 1: Grundlagen der quantitativen Unternehmensplanung. Grundfragen der Planung.- Quantitative Planung im Unternehmen.- Teil 2: Optimierungsmodelle und Optimierungsmethoden. Lineare Optimierung. Ganzzahlige Optimierung.- Nichtlineare Optimierung.- Teil 3: Projektplanung und Netzplantechnik. Graphentheoretische Grundlagen.- Grundbegriffe und Darstellungsformen für Netzpläne.- Zeitplanung mit Vorgangsknotennetzen.- Zeitplanung mit Vorgangspfeilnetzen.- Optimale Flüsse in Digraphen.- Planung von Projektkosten.- Teil 4: Stochastische Modelle. Homogene Markovketten.- Warteschlangen.- Lagerhaltungsmodelle.- Teil 5: Nichtexakte Lösungsverfahren. Heuristische Verfahren.- Simulation.- Literaturverzeichnis.- Stichwortverzeichnis.

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Book SynopsisStudierende, die ein Informatikstudium aufnehmen, brauchen zu Beginn vor allem eines: mathematische Grundkenntnisse. Dieser Crashkurs soll Studierenden im ersten Semester helfen, möglichst rechtzeitig diese Fähigkeiten zu erwerben. Durch viele motivierende Beispiele, erklärende Bilder und konkrete Darstellungen weckt das Buch Freude an der Mathematik und sichert den erfolgreichen Einstieg.Trade Review"Ein schönes Einsteigerbuch, das gut auf das Studium vorbereitet und trotzdem die Lust an Mathe nicht verleidet." www.media-mania.de, 30.12.2010Table of ContentsSchulstoff.- Schulstoff.- Mengen, Logik und Kombinatorik.- Grundbegriffe.- Logik und Beweismethoden.- Kombinatorik.- Algebra und Zahlentheorie.- Modulare Arithmetik.- Algebraische Strukturen.- Lineare Algebra.- Vektorkalkül.- Matrizenkalkül.- Analysis.- Folgen und Rekursionsgleichungen.- Konvergenz von Zahlenfolgen.- Differenzialrechnung.- Diskrete Stochastik.- Ereignisse und ihre Wahrscheinlichkeiten.- Zufallsvariablen.

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Book SynopsisThe book Practical Multivariate Statistics has been specifically designed for P.G. and Ph.D. students majoring in Pure Statistics, Agricultural Statistics, Biological and Social Sciences, as well as individuals preparing for competitive examinations such as ISS, IES, SSS, A.S.R.B., States PSCs, NET, and IAS. This book is also beneficial for faculty members of the Department of Statistics at Indian Universities. With 33 years of teaching experience under his belt, the author has prepared this book to cater to the needs of U.G., P.G., and Ph.D. students from a variety of disciplines in Agriculture, Agril. Engg., and Agril Statistics. The book is composed of 12 chapters, with the first five focusing on Multivariate Analysis, and the subsequent four delving into Multivariate Regression Analysis. The tenth chapter deals with Multicollinearity, while the eleventh chapter covers Sampling Distributions of Partial & Multiple Correlation. Lastly, the twelfth chapter provides an overview of Elements of Stochastic Processes. Each chapter includes three types of questions: True/False, Fill in the Blanks, and Multiple Choice, along with their corresponding Key Answers.

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Book SynopsisGeomorphology is an area of study that focuses on the outer surface of the Earths crust. This field is crucial for understanding the historical and current geological processes that have shaped the Earths surface, as well as the resources and ecosystems that it supports. Additionally, geomorphology plays a critical role in identifying areas that are prone to natural disasters. The use of remote sensing technology has greatly expanded the scope of geomorphology, as it allows for the easy observation and animation of geological processes.

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Book SynopsisGeomorphology is the study of external landscape/architecture of earth's crust which stands as a testimony not only for the palaeo morphotectonic and morpho-dynamic activities but also the present day geological processes as well, thus making geomorphology significant in understand the hierarchical evolution of the earth and related resources, environment/ecosystems and disaster proneness. With remote sensing deepening its roots in all fields, the scope of geomorphology too has phenomenally widened as fabrication and animation of modern geologic/geomorphic processes are being easily done by remote sensing.Table of Contents1. Pleistocene Earth Movements In Peninsular India- Evidences From Landsat MSS and Thematic Mapper Data 2. Remote Sensing and Pleistocene Tectonics of Southern Indian Peninsula 3. Evidences of Neotectonism Along Coromandel Coast of Tamil Nadu, Using IRS Data. 4. Pleistocene/ Holocene Graben Along Pondicherry -Cumbum Valley, Tamil Nadu, India 5. Neotectonic Controls on The Migration of Sarasvati River of The Great Indian Desert 6. Eyed Drainages Observed in IRS Imagery in Tamil Nadu, India and Their Geological Significance 7. Fluvial Anomalies and Neotectonics of Western Ghats, Tamil Nadu, India 8. Aid of Remote Sensing In Fluvial Geomorphic Mapping 9. Remote Sensing and River Migration In Western India 10. Remote Sensing of River Migration in Tamil Nadu 11. The Phenomenon of River Migration In Northern Tamil Nadu Evidence From Satellite Data, Archaeology and Tamil Literature 12. A Remote Sensing Study of River Deltas of Tamil Nadu 13. Detection of Submarine Delta along Madras Coast, India, Using IRS Imagery 14. Morpho- Tectonic Evolution of East and West Coasts of Indian Peninsula 15. Tectonic and Geomorphic Evolution of West Coast Along Mangalore- Cape Comorin Sector, India 16. Remote Sensing and Certain Challenging Coastal Engineering Geological Problems of Tamil Nadu Coast, India 17. Rapid Land Building Activity Along Vedaranniyam Coast and Its Possible Implications 18. Temporal Changes In Land -Water Distribution Pattern During 1930-1993 along The CoromandelCoast of Tamil Nadu, India, and Its Significance 19. Marine Environment Modeling Using Thematic Mapper Data 20. IRS -1A Imagery Senses Some Rapidly Building Islands along East Coast of Tamil Nadu 21. GIS Based Visualization of Land - Ocean Interactive Phenomenon along Vedaranniyam Coast,Tamil Nadu, India 22. Tectonically Induced Environmental Problems on and off Pondicherry Coast, Tamil Nadu, India.A Vision Through Remote Sensing 23. Landslides and Quaternary Tectonics of South India

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Book SynopsisThe book Statistical Analysis Using R Software provides a comprehensive overview of R software and the codes used for statistical analysis. The development and content of R software are discussed in detail, as well as the concepts of writing codes in R and importing data from other software for use in R. The codes used for statistical analysis are clearly explained and provided in the book. It is a valuable resource for students and researchers seeking to understand the concepts of R and its use for statistical analysis.

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Book SynopsisIl volume fornisce un'introduzione a teoria e applicazioni dei modelli lineari generalizzati. Si presentano modelli di regressione per risposte continue, binarie, categoriali e di conteggio. Si offre anche un'introduzione ai modelli per risposte correlate. Utilizzando il software statistico R, vengono forniti gli strumenti per l'analisi dei dati tramite i diversi modelli parametrici e semiparametrici. Gli esempi con R alla fine di ciascun capitolo rappresentano una guida ad esercitazioni con il computer e richiedono una partecipazione attiva nello svolgere le analisi proposte. Numerosi esercizi concludono ogni capitolo. Il taglio adottato è funzionale ad approfondire in modo integrato aspetti teorici e applicativi. Unico nel suo genere, è rivolto agli studenti di Scienze Statistiche. Table of Contents1. Modelli lineari e lineari generalizzati.- 2. Modelli lineari generalizzati.- 3. Modelli per dati bancari.- 4. Modelli per risposte politomiche.- 5. Modelli per dati di conteggio.- 6. Quasi-verosimiglianza.- Modelli per risposte correlate.- A Dati utilizzati nel testo.- B Distribuzioni di probabilità.- C Eguaglianza tra stime OLS e GLS.- D Il metodo delta.- E Funzioni generatrici.- F Codice R per l’esempio 2.9.- G Equivalenza tra residui di Pearson e di devianza.- H Modelli per la sovradispersione: schema.

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Book SynopsisThe notes are based on lectures on stochastic processes given at Scuola Normale Superiore in 1999 and 2000. Some new material was added and only selected, less standard results were presented. We did not include several applications to statistical mechanics and mathematical finance, covered in the lectures, as we hope to write part two of the notes devoted to applications of stochastic processes in modelling. The main themes of the notes are constructions of stochastic processes. We present different approaches to the existence question proposed by Kolmogorov, Wiener, Ito and Prohorov. Special attention is also paid to Levy processes. The lectures are basically self-contained and rely only on elementary measure theory and functional analysis. They might be used for more advanced courses on stochastic processes.

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Book SynopsisThe lectures concentrate on some old and new relations between quasiderivatives of solutions to Ito stochastic equations and interior smoothness of harmonic functions associated with degenerate elliptic equations. Recent progress in the case of constant coefficients is discussed in full detail.

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Book SynopsisThis volume explores, from a mathematical and a philosophical perspective, the virtuous circle connecting logic and rationality. While logic lends its methods, techniques and ideas to the investigation of rationality, the practical problems which arise in modelling rational behaviour, especially in the social sciences, motivate logicians to develop more refined logical formalisms. This is why non classical logics - a unifying theme of this volume - play a fundamental role in the construction of formal models of rationality.Table of ContentsIntroduction.- 1. Foundations.- Ordered algebras and logic. George Metcalfe, Francesco Paoli and Constantine Tsinakis.- 2. Probability. The social entropy process: Axiomatising the aggregation of probabilistic beliefs. George Wilmers.- Conditional probability in the light of qualitative belief change. David Makinson.- Is there a probability theory of many-valued events? Vincenzo Marra.- 3. Uncertainty.- On Giles style dialogue games and hypersequent systems. Christian G. Fermüller.- Poset representation for free RDP-algebras. Diego Valota.- Uncertainty, indeterminacy and fuzziness: A probabilistic approach. Martina Fedel.- 4. Rationality.- Tractable depth-bounded logics and the problem of logical omniscience. Marcello D’Agostino.- Rational behaviour at trust nodes. Hykel Hosni and Silvia Milano.

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Book SynopsisThe book consists of 12 chapteThe I is related to terminology in experimental design while the II devoted to completely randomized block design and randomized block design for agricultural experiments in the field. The III is devoted to factorial experiments in randomized block design involving two or more factoThe IV deals with partially confounded and fully confounded factorial experiments. The cheaper V deals with split plot design and strip plot design. The VI deals with repetition of experiments over years with sampling in agricultural trials at cultivator's fields, while VII is related to sustainability of crop sequences and treatments. The VIII deals with analysis of trials at cultivators' fields while the IX deals with sampling techniques. X deals with co-relation and regression studies. The XI spells out the agronomic considerations and synthesis of system based results. The last XII deals with methodology and procedure for farming systems research while the schedule for date collection for farming systems characterization and evaluation is given in appendix.Table of Contents1. Terminology in experimental design. 2. Completely randomized design and randomized block design. 3. Factorial experiments. 4. Confounded factorial design. 5. Split-plot and strip-plot design. 6. Repetition of experiments over years. 7. Analysis of sustainability of crop sequences / treatments. 8. Selection of cultivators. analysis of farm experiments. 9. Sampling techniques. 10. Correlation and regression. 11. Agronomic considerations and synthesis of system based results.12. Methodology and procedures for farming systems research.

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Book SynopsisThe book is a reference book useful for undergraduates, postgraduates and research scholars of biological, ecological and medical sciences. The purpose of writing this book is to provide an accessible reference book on statistical techniques whose proper use will help students in withdrawing accurate results and able to interpret them logically. The methods described in this book are, of course, the same as those used in different disciplines, but things are made so user's friendly that even general readers will find this book useful. The s of the book have been organized in such a way that suits the course curriculum of various universities. In this book enough materials are provided to cover statistical techniques with examples which are ecological and biological based.Table of Contents1. Introduction to Statistics 2. Organisation of Data 3. Data Transformation 4. Descriptive Statistics: Measurement of Central Tendency 5. Descriptive Statistics: Measures of Dispersion 6. Descriptive Statistics: Data Distributions 7. Inferential Statistics: Confidence Intervals 8 Inferential Statistics: Parametric Tests of Significance 9 Inferential Statistics: Non-Parametric Tests of Significance 10 Inferential Statistics: Correlation and Regression 11 Presentation of Data 12 Choosing A Statistical Test

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Book Synopsis

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Book SynopsisThis book presents and standardizes statistical models and methods that can be directly applied to both reliability and survival analysis. These two types of analysis are widely used in many fields, including engineering, management, medicine, actuarial science, the environmental sciences, and the life sciences. Though there are a number of books on reliability analysis and a handful on survival analysis, there are virtually no books on both topics and their overlapping concepts. Offering an essential textbook, this book will benefit students, researchers, and practitioners in reliability and survival analysis, reliability engineering, biostatistics, and the biomedical sciences. Table of ContentsChapter 1: Reliability and Survival Analysis: Concepts and Definitions.- Chapter 2: Mean Lifetime and Residual Lifetime.- Chapter 3: Probability Distributions of Lifetimes (Uncensored).- Chapter 4: Censoring Mechanisms.- Chapter 5: Probability Distributions of Lifetimes under Censoring Schemes.- Chapter 6: Nonparametric Methods.- Chapter 7: Parametric Methods.- Chapter 8: Regression Models.- Chapter 9: Generalized Linear Models for Failure Times.- Chapter 10: Components and Systems.- Chapter 11: Reliability Functions and Ageing Properties.- Chapter 12: Models for Production System.- Chapter 13: Stochastic Models.- Chapter 14: Further Topics.- References.- Appendix - A: Statistical Tables.- Appendix - B: Data Sets.- Appendix - C: R-Packages and Programming Codes.- Index.

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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 discusses all major topics on survey sampling and estimation. It covers traditional as well as advanced sampling methods related to the spatial populations. The book presents real-world applications of major sampling methods and illustrates them with the R software. As a large sample size is not cost-efficient, this book introduces a new method by using the domain knowledge of the negative correlation between the variable of interest and the auxiliary variable in order to control the size of a sample. In addition, the book focuses on adaptive cluster sampling, rank-set sampling and their applications in real life. Advance methods discussed in the book have tremendous applications in ecology, environmental science, health science, forestry, bio-sciences, and humanities. This book is targeted as a text for undergraduate and graduate students of statistics, as well as researchers in various disciplines.Table of Contents-1. Introduction.- 2. Simple Random Sampling.- 3. Stratied Random Sampling.- 4. Cluster Sampling.- 5. Double Sampling.- 6. Probability Proportional to Size Sampling.- 7. Systematic Sampling.- 8. Resampling Techniques.- 9. Adaptive Cluster Sampling.- 10. Two-Stage Adaptive Cluster Sampling.- 11. Adaptive Cluster Double Sampling.- 12. Inverse Adaptive Cluster Sampling.- 13. Two Stage Inverse Adaptive Cluster Sampling.- 14. Stratified Inverse Adaptive Cluster Sampling.- 15. Negative Adaptive Cluster Sampling.- 16. Negative Adaptive Cluster Double Sampling.- 17. Two- Stage Negative Adaptive Cluster Sampling.- 18. Balanced and Unbalanced Ranked Set Sampling.- 19. Ranked Set Sampling in Other Parameter Estimation and Non-Parametric Inference.- 20. Important Versions of Ranked Set Sampling.- 21. Sampling Errors.

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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 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 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 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 SynopsisThis book presents new computation schemes for the sensitivity of options using the binomial tree and introduces readers to the discrete Malliavin calculus. It also shows that applications of the discrete Malliavin calculus approach to the binomial tree model offer fundamental tools for computing Greeks.The binomial tree approach is one of the most popular methods in option pricing. Although it is a fairly traditional model for option pricing, it is still widely used in financial institutions since it is tractable and easy to understand. However, the book shows that the tree approach also offers a powerful tool for deriving the Greeks for options. Greeks are quantities that represent the sensitivities of the price of derivative securities with respect to changes in the underlying asset price or parameters. The Malliavin calculus, the stochastic methods of variations, is one of the most popular tools used to derive Greeks. However, it is also very difficult to understand for most students and practitioners because it is based on complex mathematics. To help readers more easily understand the Malliavin calculus, the book introduces the discrete Malliavin calculus, a theory of the functional for the Bernoulli random walk. The discrete Malliavin calculus is significantly easier to understand, because the functional space of the Bernoulli random walk is realized in a finite dimensional space. As such, it makes this valuable tool far more accessible for a broad readership.Table of ContentsIntroduction.- Single-Period Model.- Multiple Time Model.- Application to Finance.- Spectral Binomial Tree.- Short Introduction to Malliavin Calculus in Continuous Time Model.- Discrete Malliavin Greeks.

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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 a highly practical introduction to Bayesian statistical modeling with Stan, which has become the most popular probabilistic programming language.The book is divided into four parts. The first part reviews the theoretical background of modeling and Bayesian inference and presents a modeling workflow that makes modeling more engineering than art. The second part discusses the use of Stan, CmdStanR, and CmdStanPy from the very beginning to basic regression analyses. The third part then introduces a number of probability distributions, nonlinear models, and hierarchical (multilevel) models, which are essential to mastering statistical modeling. It also describes a wide range of frequently used modeling techniques, such as censoring, outliers, missing data, speed-up, and parameter constraints, and discusses how to lead convergence of MCMC. Lastly, the fourth part examines advanced topics for real-world data: longitudinal data analysis, state space models, spatial data analysis, Gaussian processes, Bayesian optimization, dimensionality reduction, model selection, and information criteria, demonstrating that Stan can solve any one of these problems in as little as 30 lines.Using numerous easy-to-understand examples, the book explains key concepts, which continue to be useful when using future versions of Stan and when using other statistical modeling tools. The examples do not require domain knowledge and can be generalized to many fields. The book presents full explanations of code and math formulas, enabling readers to extend models for their own problems. All the code and data are on GitHub.Table of ContentsPrefacePart I: Introduction Chapter 1: Overview of Statistical Modeling and StanChapter 2: Review of Bayesian InferenceChapter 3: Before Starting Statistical ModelingPart II: Introduction of StanChapter 4: Start with Stan, RStan and PyStanChapter 5: Elementary Regression and Model CheckPart III: Essential Components and Techniques for ExpertsChapter 6: Introduction of Distributions from Modeling ViewpointsChapter 7: Issues of RegressionChapter 8: Nonlinear ModelChapter 9: Hierarchical ModelChapter 10: Advanced GrammarsChapter 11: How to Lead ConvergenceChapter 12: Discrete ParametersChapter 13: Usage of MCMC SamplesPart IV: Advanced Topics for Real-world DataChapter 14: Longitudinal Data Analysis with State Space Model Chapter 15: Spatial Data Analysis with Markov Field ModelChapter 16: Survival AnalysisChapter 17: Causal InferenceChapter 18: Model selectionAppendix: Differences between Stan and BUGSReferenceIndex

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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 book includes speeches given during five seminar sessions held in honor of Prof. C. R. Rao, on his 100th year. This book also contains a few write-ups touching on the diverse aspects of this august personality. The chapters pay tribute to Prof. C. R. Rao, the Padma Vibhushan awardee, by discussing his life and contributions to the field of statistics. The book also includes a chapter by the Abel Prize winner Prof. S. R. Varadhan who happened to successfully complete his Ph.D. under the guidance of Prof. C. R. Rao. Table of Contents

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Book SynopsisAn Overview over the Content of This Book.- GB Models and Yield Curves in Traditional Finance, Mathematical Finance and K System.- Pricing Government Bonds and Yield Curves via K Models.- Empirical Effectiveness of the KGB Model as JGB and USGB Pricing Models.- Empirical Effectiveness of K0-Yield Curve.- KCB Model and Term Structure of Default Probabilities (TSDP).- Credit Risk Analyses on Japanese CBs and Default Curves.- Credit Risk Analyses on CB Prices in the US Energy Sector.- Credit Risk Analysis on Euro Government Bonds.- Extended KCB Model, Credit Portfolio and CDS Pricing.

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Book Synopsis1. Introduction.- 2. Properties of Life Distributions.- 3. Univariate Continuous Distributions.- 4. Some Discrete Distributions.- 5. Distributions Derived from the Parent Distribution.- 6. Finite Range Life Distributions.- 7. Ordering among Life Distributions .- 8. Classes of Life Distributions .- 9. Characterizations of Life Distributions .- 10. Distributions of Special Interest .- 11. Using an Appropriate Probability Model.

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Book SynopsisChapter 1. Introduction.- Chapter 2. Itô calculus.- Chapter 3. Malliavin calculus.- Chapter 4. Asymptotic expansion.- Chapter 5. Weak approximation.- Chapter 6. Application: Deep learning-based weak approximation.

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Book SynopsisMeasuring Scales.- Tables and Charts.- Summarizing Data.- Probability.- Discrete Distributions.- Normal Distribution.- Sampling and Sample Size.- Estimation and Statistical Inference.- Health and Disease Measures.- Foreground ToolBox for Hypothesis Testing.- Hypothesis Testing.- Correlation and Regression.- Analysis Of Variance.

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Book Synopsis1. Introduction.- 2. Framework of gamma-divergence.- 2.1. Scale invariance.- 2.2 GM divergence and HM divergence.- 3. Minimum divergence methods for generalized linear models.- 3.1. Bernoulli logistic model.- 3.2. Poisson log-linear model.- 3.3. Poisson point process model.- 4. Minimum divergence methods in machine leaning.- 4.1. Multi-class AdaBoost.- 4.2. Boltzmann machine.- 5. gamma-divergence for real valued functions.- 6. Discussion.

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Book SynopsisThis treatise delves into the latest advancements in stochastic volatility models, highlighting the utilization of Markov chain Monte Carlo simulations for estimating model parameters and forecasting the volatility and quantiles of financial asset returns. The modeling of financial time series volatility constitutes a crucial aspect of finance, as it plays a vital role in predicting return distributions and managing risks. Among the various econometric models available, the stochastic volatility model has been a popular choice, particularly in comparison to other models, such as GARCH models, as it has demonstrated superior performance in previous empirical studies in terms of fit, forecasting volatility, and evaluating tail risk measures such as Value-at-Risk and Expected Shortfall. The book also explores an extension of the basic stochastic volatility model, incorporating a skewed return error distribution and a realized volatility measurement equation. The concept of realized volatility, a newly established estimator of volatility using intraday returns data, is introduced, and a comprehensive description of the resulting realized stochastic volatility model is provided. The text contains a thorough explanation of several efficient sampling algorithms for latent log volatilities, as well as an illustration of parameter estimation and volatility prediction through empirical studies utilizing various asset return data, including the yen/US dollar exchange rate, the Dow Jones Industrial Average, and the Nikkei 225 stock index. This publication is highly recommended for readers with an interest in the latest developments in stochastic volatility models and realized stochastic volatility models, particularly in regards to financial risk management.Table of Contents1 Introduction.- 2 Stochastic Volatility Model.- 3 Asymmetric Stochastic Volatility Model.- 4 Stochastic Volatility Model with Generalized Hyperbolic Skew Student’s t Error.- 5 Realized Stochastic Volatility Model.

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