Nonparametric Statistical Methods

Author: Myles Hollander
Publisher: John Wiley & Sons
ISBN: 1118553292
Format: PDF, ePub, Mobi
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Praise for the Second Edition “This book should be an essential part of the personal library of every practicing statistician.”—Technometrics Thoroughly revised and updated, the new edition of Nonparametric Statistical Methods includes additional modern topics and procedures, more practical data sets, and new problems from real-life situations. The book continues to emphasize the importance of nonparametric methods as a significant branch of modern statistics and equips readers with the conceptual and technical skills necessary to select and apply the appropriate procedures for any given situation. Written by leading statisticians, Nonparametric Statistical Methods, Third Edition provides readers with crucial nonparametric techniques in a variety of settings, emphasizing the assumptions underlying the methods. The book provides an extensive array of examples that clearly illustrate how to use nonparametric approaches for handling one- or two-sample location and dispersion problems, dichotomous data, and one-way and two-way layout problems. In addition, the Third Edition features: The use of the freely available R software to aid in computation and simulation, including many new R programs written explicitly for this new edition New chapters that address density estimation, wavelets, smoothing, ranked set sampling, and Bayesian nonparametrics Problems that illustrate examples from agricultural science, astronomy, biology, criminology, education, engineering, environmental science, geology, home economics, medicine, oceanography, physics, psychology, sociology, and space science Nonparametric Statistical Methods, Third Edition is an excellent reference for applied statisticians and practitioners who seek a review of nonparametric methods and their relevant applications. The book is also an ideal textbook for upper-undergraduate and first-year graduate courses in applied nonparametric statistics.

Nonparametric Statistical Methods Solutions Manual

Author: Myles Hollander
Publisher: Wiley-Interscience
ISBN: 9780471329862
Format: PDF
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The importance of nonparametric methods in modern statistics has grown dramatically since their inception in the mid-1930s. Requiring few or no assumptions about the populations from which data are obtained, they have emerged as the preferred methodology among statisticians and researchers performing data analysis. Today, these highly efficient techniques are being applied to an ever-widening variety of experimental designs in the social, behavioral, biological, and physical sciences. This long-awaited Second Edition of Myles Hollander and Douglas A. Wolfe's successful Nonparametric Statistical Methods meets the needs of a new generation of users, with completely up-to-date coverage of this important statistical area. Like its highly acclaimed predecessor, the revised edition, along with its companion ftp site, aims to equip students with the conceptual and technical skills necessary to select and apply the appropriate procedures for a given situation. An extensive array of examples drawn from actual experiments illustrates clearly how to use nonparametric approaches to handle one- or two-sample location and dispersion problems, dichotomous data, and one-way and two-way layout problems. Rewritten and updated, this Second Edition now includes new or expanded coverage of: * Nonparametric regression methods. * The bootstrap. * Contingency tables and the odds ratio. * Life distributions and survival analysis. * Nonparametric methods for experimental designs. * More procedures, real-world data sets, and problems. * Illustrated examples using Minitab and StatXact. An ideal text for an upper-level undergraduate or first-year graduate course, this text is also an invaluable source for professionals who want to keep abreast of the latest developments within this dynamic branch of modern statistics. An Instructor's Manual presenting detailed solutions to all the problems in the book is available upon request from the Wiley editorial department.

Nonparametric statistical methods

Author: Myles Hollander
Publisher: John Wiley & Sons
ISBN:
Format: PDF, ePub
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An Instructor's Manual presenting detailed solutions to all the problems in the book is available upon request from the Wiley editorial department.

Practical Nonparametric Statistics

Author: W. J. Conover
Publisher:
ISBN: 9780471168515
Format: PDF, ePub, Docs
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Probability theory; Statistical inference; Some tests based on the binomial distribution; Contingency tables; The use of ranks; Statistics of the kolmogorov-smirnov type; Some miscellaneous tests.

Nonparametric Statistics with Applications to Science and Engineering

Author: Paul H. Kvam
Publisher: John Wiley & Sons
ISBN: 9780470168691
Format: PDF, ePub, Docs
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A thorough and definitive book that fully addresses traditional and modern-day topics of nonparametric statistics This book presents a practical approach to nonparametric statistical analysis and provides comprehensive coverage of both established and newly developed methods. With the use of MATLAB, the authors present information on theorems and rank tests in an applied fashion, with an emphasis on modern methods in regression and curve fitting, bootstrap confidence intervals, splines, wavelets, empirical likelihood, and goodness-of-fit testing. Nonparametric Statistics with Applications to Science and Engineering begins with succinct coverage of basic results for order statistics, methods of categorical data analysis, nonparametric regression, and curve fitting methods. The authors then focus on nonparametric procedures that are becoming more relevant to engineering researchers and practitioners. The important fundamental materials needed to effectively learn and apply the discussed methods are also provided throughout the book. Complete with exercise sets, chapter reviews, and a related Web site that features downloadable MATLAB applications, this book is an essential textbook for graduate courses in engineering and the physical sciences and also serves as a valuable reference for researchers who seek a more comprehensive understanding of modern nonparametric statistical methods.

Statistical Methods for Survival Data Analysis

Author: Elisa T. Lee
Publisher: John Wiley & Sons
ISBN: 1118593057
Format: PDF, ePub, Mobi
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Praise for the Third Edition “. . . an easy-to read introduction to survival analysis which covers the major concepts and techniques of the subject.” —Statistics in Medical Research Updated and expanded to reflect the latest developments, Statistical Methods for Survival Data Analysis, Fourth Edition continues to deliver a comprehensive introduction to the most commonly-used methods for analyzing survival data. Authored by a uniquely well-qualified author team, the Fourth Edition is a critically acclaimed guide to statistical methods with applications in clinical trials, epidemiology, areas of business, and the social sciences. The book features many real-world examples to illustrate applications within these various fields, although special consideration is given to the study of survival data in biomedical sciences. Emphasizing the latest research and providing the most up-to-date information regarding software applications in the field, Statistical Methods for Survival Data Analysis, Fourth Edition also includes: Marginal and random effect models for analyzing correlated censored or uncensored data Multiple types of two-sample and K-sample comparison analysis Updated treatment of parametric methods for regression model fitting with a new focus on accelerated failure time models Expanded coverage of the Cox proportional hazards model Exercises at the end of each chapter to deepen knowledge of the presented material Statistical Methods for Survival Data Analysis is an ideal text for upper-undergraduate and graduate-level courses on survival data analysis. The book is also an excellent resource for biomedical investigators, statisticians, and epidemiologists, as well as researchers in every field in which the analysis of survival data plays a role.

Nonparametric Finance

Author: Jussi Klemelä
Publisher: John Wiley & Sons
ISBN: 1119409101
Format: PDF, ePub, Mobi
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Preface xxiii 1 Introduction 1 1.1 Statistical Finance 2 1.2 Risk Management 3 1.3 Portfolio Management 5 1.4 Pricing of Securities 6 Part I Statistical Finance 11 2 Financial Instruments 13 2.1 Stocks 13 2.2 Fixed Income Instruments 19 2.3 Derivatives 23 2.4 Data Sets 27 3 Univariate Data Analysis 33 3.1 Univariate Statistics 34 3.2 Univariate Graphical Tools 42 3.3 Univariate ParametricModels 55 3.4 Tail Modeling 61 3.5 Asymptotic Distributions 83 3.6 Univariate Stylized Facts 91 4 Multivariate Data Analysis 95 4.1 Measures of Dependence 95 4.2 Multivariate Graphical Tools 103 4.3 Multivariate ParametricModels 107 4.4 Copulas 111 5 Time Series Analysis 121 5.1 Stationarity and Autocorrelation 122 5.2 Model Free Estimation 128 5.3 Univariate Time Series Models 135 5.4 Multivariate Time Series Models 157 5.5 Time Series Stylized Facts 160 6 Prediction 163 6.1 Methods of Prediction 164 6.2 Forecast Evaluation 170 6.3 Predictive Variables 175 6.4 Asset Return Prediction 182 Part II Risk Management 193 7 Volatility Prediction 195 7.1 Applications of Volatility Prediction 197 7.2 Performance Measures for Volatility Predictors 199 7.3 Conditional Heteroskedasticity Models 200 7.4 Moving Average Methods 205 7.5 State Space Predictors 211 8 Quantiles and Value-at-Risk 219 8.1 Definitions of Quantiles 220 8.2 Applications of Quantiles 223 8.3 Performance Measures for Quantile Estimators 227 8.4 Nonparametric Estimators of Quantiles 233 8.5 Volatility Based Quantile Estimation 240 8.6 Excess Distributions in Quantile Estimation 258 8.7 Extreme ValueTheory in Quantile Estimation 288 8.8 Expected Shortfall 292 Part III Portfolio Management 297 9 Some Basic Concepts of Portfolio Theory 299 9.1 Portfolios and Their Returns 300 9.2 Comparison of Return andWealth Distributions 312 9.3 Multiperiod Portfolio Selection 326 10 Performance Measurement 337 10.1 The Sharpe Ratio 338 10.2 Certainty Equivalent 346 10.3 Drawdown 347 10.4 Alpha and Conditional Alpha 348 10.5 Graphical Tools of Performance Measurement 356 11 Markowitz Portfolios 367 11.1 Variance Penalized Expected Return 369 11.2 Minimizing Variance under a Sufficient Expected Return 372 11.3 Markowitz Bullets 375 11.4 Further Topics in Markowitz Portfolio Selection 381 11.5 Examples of Markowitz Portfolio Selection 383 12 Dynamic Portfolio Selection 385 12.1 Prediction in Dynamic Portfolio Selection 387 12.2 Backtesting Trading Strategies 393 12.3 One Risky Asset 394 12.4 Two Risky Assets 405 Part IV Pricing of Securities 419 13 Principles of Asset Pricing 421 13.1 Introduction to Asset Pricing 422 13.2 Fundamental Theorems of Asset Pricing 430 13.3 Evaluation of Pricing and Hedging Methods 456 14 Pricing by Arbitrage 459 14.1 Futures and the Put-Call Parity 460 14.2 Pricing in Binary Models 466 14.3 Black-Scholes Pricing 485 14.4 Black-Scholes Hedging 505 14.5 Black-Scholes Hedging and Volatility Estimation 515 15 Pricing in IncompleteModels 521 15.1 Quadratic Hedging and Pricing 522 15.2 Utility Maximization 523 15.3 Absolutely Continuous Changes of Measures 530 15.4 GARCH Market Models 534 15.5 Nonparametric Pricing Using Historical Simulation 545 15.6 Estimation of the Risk-Neutral Density 551 15.7 Quantile Hedging 555 16 Quadratic and Local Quadratic Hedging 557 16.1 Quadratic Hedging 558 16.2 Local Quadratic Hedging 583 16.3 Implementations of Local Quadratic Hedging 595 17 Option Strategies 615 17.1 Option Strategies 616 17.2 Profitability of Option Strategies 625 18 Interest Rate Derivatives 649 18.1 Basic Concepts of Interest Rate Derivatives 650 18.2 Interest Rate Forwards 659 18.3 Interest Rate Options 666 18.4 Modeling Interest Rate Markets 669 References 673 Index 681

Robust Nonparametric Statistical Methods Second Edition

Author: Thomas P. Hettmansperger
Publisher: CRC Press
ISBN: 1439809097
Format: PDF, Docs
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Presenting an extensive set of tools and methods for data analysis, Robust Nonparametric Statistical Methods, Second Edition covers univariate tests and estimates with extensions to linear models, multivariate models, times series models, experimental designs, and mixed models. It follows the approach of the first edition by developing rank-based methods from the unifying theme of geometry. This edition, however, includes more models and methods and significantly extends the possible analyses based on ranks. New to the Second Edition A new section on rank procedures for nonlinear models A new chapter on models with dependent error structure, covering rank methods for mixed models, general estimating equations, and time series New material on the development of computationally efficient affine invariant/equivariant sign methods based on transform-retransform techniques in multivariate models Taking a comprehensive, unified approach to statistical analysis, the book continues to describe one- and two-sample problems, the basic development of rank methods in the linear model, and fixed effects experimental designs. It also explores models with dependent error structure and multivariate models. The authors illustrate the implementation of the methods using many real-world examples and R. More information about the data sets and R packages can be found at www.crcpress.com

Nonparametric Hypothesis Testing

Author: Stefano Bonnini
Publisher: John Wiley & Sons
ISBN: 1118763483
Format: PDF
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A novel presentation of rank and permutation tests, with accessible guidance to applications in R Nonparametric testing problems are frequently encountered in many scientific disciplines, such as engineering, medicine and the social sciences. This book summarizes traditional rank techniques and more recent developments in permutation testing as robust tools for dealing with complex data with low sample size. Key Features: Examines the most widely used methodologies of nonparametric testing. Includes extensive software codes in R featuring worked examples, and uses real case studies from both experimental and observational studies. Presents and discusses solutions to the most important and frequently encountered real problems in different fields. Features a supporting website (www.wiley.com/go/hypothesis_testing) containing all of the data sets examined in the book along with ready to use R software codes. Nonparametric Hypothesis Testing combines an up to date overview with useful practical guidance to applications in R, and will be a valuable resource for practitioners and researchers working in a wide range of scientific fields including engineering, biostatistics, psychology and medicine.