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Author : J. W. Snow
Publisher :
ISBN 13 :
Total Pages : 154 pages
Book Rating : 4.:/5 (223 download)
Download or read book Distribution-free Inference about Quantiles written by J. W. Snow and published by . This book was released on 1972 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Jean Dickinson Gibbons
Publisher : CRC Press
ISBN 13 : 1439896127
Total Pages : 652 pages
Book Rating : 4.4/5 (398 download)
Download or read book Nonparametric Statistical Inference written by Jean Dickinson Gibbons and published by CRC Press. This book was released on 2010-07-26 with total page 652 pages. Available in PDF, EPUB and Kindle. Book excerpt: Proven Material for a Course on the Introduction to the Theory and/or on the Applications of Classical Nonparametric Methods Since its first publication in 1971, Nonparametric Statistical Inference has been widely regarded as the source for learning about nonparametric statistics. The fifth edition carries on this tradition while thoroughly revising at least 50 percent of the material. New to the Fifth Edition Updated and revised contents based on recent journal articles in the literature A new section in the chapter on goodness-of-fit tests A new chapter that offers practical guidance on how to choose among the various nonparametric procedures covered Additional problems and examples Improved computer figures This classic, best-selling statistics book continues to cover the most commonly used nonparametric procedures. The authors carefully state the assumptions, develop the theory behind the procedures, and illustrate the techniques using realistic research examples from the social, behavioral, and life sciences. For most procedures, they present the tests of hypotheses, confidence interval estimation, sample size determination, power, and comparisons of other relevant procedures. The text also gives examples of computer applications based on Minitab, SAS, and StatXact and compares these examples with corresponding hand calculations. The appendix includes a collection of tables required for solving the data-oriented problems. Nonparametric Statistical Inference, Fifth Edition provides in-depth yet accessible coverage of the theory and methods of nonparametric statistical inference procedures. It takes a practical approach that draws on scores of examples and problems and minimizes the theorem-proof format. Jean Dickinson Gibbons was recently interviewed regarding her generous pledge to Virginia Tech.
Author : Herbert A. David
Publisher : John Wiley & Sons
ISBN 13 : 0471654019
Total Pages : 482 pages
Book Rating : 4.4/5 (716 download)
Download or read book Order Statistics written by Herbert A. David and published by John Wiley & Sons. This book was released on 2004-03-22 with total page 482 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume provides an up-to-date coverage of the theory and applications of ordered random variables and their functions. Furthermore, it develops the distribution theory of OS systematically. Applications include procedures for the treatment of outliers and other data analysis techniques. Even when chapter and section headings are the same as in OSII, there are appreciable changes, mostly additions, with some obvious deletions. Parts of old Ch. 7, for example, are prime candidates for omission. Appendices are designed to help collate tables, computer algorithms, and software, as well as to compile related monographs on the subject matter. Extensive exercise sets will continue, many of them replaced by newer ones.
Author : N. Unnikrishnan Nair
Publisher : Springer Science & Business Media
ISBN 13 : 0817683615
Total Pages : 411 pages
Book Rating : 4.8/5 (176 download)
Download or read book Quantile-Based Reliability Analysis written by N. Unnikrishnan Nair and published by Springer Science & Business Media. This book was released on 2013-08-24 with total page 411 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a fresh approach to reliability theory, an area that has gained increasing relevance in fields from statistics and engineering to demography and insurance. Its innovative use of quantile functions gives an analysis of lifetime data that is generally simpler, more robust, and more accurate than the traditional methods, and opens the door for further research in a wide variety of fields involving statistical analysis. In addition, the book can be used to good effect in the classroom as a text for advanced undergraduate and graduate courses in Reliability and Statistics.
Author : John E. Kolassa
Publisher : CRC Press
ISBN 13 : 0429514794
Total Pages : 187 pages
Book Rating : 4.4/5 (295 download)
Download or read book An Introduction to Nonparametric Statistics written by John E. Kolassa and published by CRC Press. This book was released on 2020-09-29 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook presents techniques for statistical analysis in the absence of strong assumptions about the distributions generating the data. Rank-based and resampling techniques are heavily represented, but robust techniques are considered as well. These techniques include one-sample testing and estimation, multi-sample testing and estimation, and regression. Attention is payed to the intellectual development of the field, with a thorough review of bibliographical references. Computational tools, in R and SAS, are developed and illustrated via examples. Exercises designed to reinforce examples are included. Important techniques covered include Rank-based techniques, including sign, Kruskal-Wallis, Friedman, Mann-Whitney and Wilcoxon tests, are presented. Tests are inverted to produce estimates and confidence intervals. Multivariate tests are explored. Techniques reflecting the dependence of a response variable on explanatory variables are presented. Density estimation is explored. The bootstrap and jackknife are discussed. This text is intended for a graduate student in applied statistics. The course is best taken after an introductory course in statistical methodology, a course in elementary probability, and a course in regression. Mathematical prerequisites include calculus through multivariate differentiation and integration, and, ideally, a course in matrix algebra.
Author : Karl V. Bury
Publisher : Cambridge University Press
ISBN 13 : 9780521635066
Total Pages : 386 pages
Book Rating : 4.6/5 (35 download)
Download or read book Statistical Distributions in Engineering written by Karl V. Bury and published by Cambridge University Press. This book was released on 1999-01-13 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 1999 book presents single-variable statistical distributions useful in solving practical problems in a wide range of engineering contexts.
Author : Charles M. Beach
Publisher :
ISBN 13 :
Total Pages : 54 pages
Book Rating : 4.3/5 (91 download)
Download or read book Model-free Statistical Inference with Lorenz Curves, Income Shares, and Gini Coefficients written by Charles M. Beach and published by . This book was released on 1979 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Marco Geraci
Publisher : Wiley
ISBN 13 : 9781118856727
Total Pages : 500 pages
Book Rating : 4.8/5 (567 download)
Download or read book Applied Statistics Using Quantiles written by Marco Geraci and published by Wiley. This book was released on 2020-05-18 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book is devoted to quantile-based methods of analysis. It is divided in three parts. Part I introduces general topics in statistics and sets out the goals of statistical analysis and describes the double-faced nature of statistical distributions, namely probability and quantile functions and how the latter can be used to extract information from the data. In particular, chapter 3 (location, scale and shape of probability distributions) describes where such information resides; this is a recurring theme throughout the book and is further developed in Chapters 8 and 14. While inferential procedures based on modelling probability functions have been widely described in a number of statistical textbooks, scientific contributions to the development of quantile-based inference are sparse and lack a comprehensive treatment. The main topics of the book are discussed in parts II and III, which introduce methods and applications for unconditional and conditional quantiles. Each part considers: the distribution-free approach, in which quantile estimation makes no use of parametric probability models; and the model-based approach, in which the quantile function is defined as the inverse of a known distribution function, thus quantile estimation conforms to some statistical model (e.g., Normal, exponential, Pareto). The book emphasises that in a quantile model-based approach the modelling step starts from the quantile function directly (as opposed to modelling the distribution function and deriving the quantiles by inversion).
Author : David Ardia
Publisher : Springer Science & Business Media
ISBN 13 : 3540786570
Total Pages : 206 pages
Book Rating : 4.5/5 (47 download)
Download or read book Financial Risk Management with Bayesian Estimation of GARCH Models written by David Ardia and published by Springer Science & Business Media. This book was released on 2008-05-08 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents in detail methodologies for the Bayesian estimation of sing- regime and regime-switching GARCH models. These models are widespread and essential tools in n ancial econometrics and have, until recently, mainly been estimated using the classical Maximum Likelihood technique. As this study aims to demonstrate, the Bayesian approach o ers an attractive alternative which enables small sample results, robust estimation, model discrimination and probabilistic statements on nonlinear functions of the model parameters. The author is indebted to numerous individuals for help in the preparation of this study. Primarily, I owe a great debt to Prof. Dr. Philippe J. Deschamps who inspired me to study Bayesian econometrics, suggested the subject, guided me under his supervision and encouraged my research. I would also like to thank Prof. Dr. Martin Wallmeier and my colleagues of the Department of Quantitative Economics, in particular Michael Beer, Roberto Cerratti and Gilles Kaltenrieder, for their useful comments and discussions. I am very indebted to my friends Carlos Ord as Criado, Julien A. Straubhaar, J er ^ ome Ph. A. Taillard and Mathieu Vuilleumier, for their support in the elds of economics, mathematics and statistics. Thanks also to my friend Kevin Barnes who helped with my English in this work. Finally, I am greatly indebted to my parents and grandparents for their support and encouragement while I was struggling with the writing of this thesis.
Author : Sermin Gungor
Publisher :
ISBN 13 :
Total Pages : 42 pages
Book Rating : 4.:/5 (13 download)
Download or read book Exact Inference in Predictive Quantile Regressions with an Application to Stock Returns written by Sermin Gungor and published by . This book was released on 2017 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: We develop an exact and distribution-free procedure to test for quantile predictability at several quantile levels jointly, while allowing for an endogenous predictive regressor with any degree of persistence. The approach proceeds by combining together the quantile regression t-statistics from each considered quantile level and uses Monte Carlo resampling techniques to control the overall significance level of the data-dependent combination in finite samples. A simulation study confirms the fact that the proposed inference procedure controls the familywise error rate and achieves good power. We use the new approach to test the ability of many commonly used variables to predict the quantiles of excess stock returns, and shed new light on tail predictability.
Author : Vladimir Vovk
Publisher : Springer
ISBN 13 : 9781441934710
Total Pages : 0 pages
Book Rating : 4.9/5 (347 download)
Download or read book Algorithmic Learning in a Random World written by Vladimir Vovk and published by Springer. This book was released on 2010-10-29 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. Based on these approximations, a new set of machine learning algorithms have been developed that can be used to make predictions and to estimate their confidence and credibility in high-dimensional spaces under the usual assumption that the data are independent and identically distributed (assumption of randomness). Another aim of this unique monograph is to outline some limits of predictions: The approach based on algorithmic theory of randomness allows for the proof of impossibility of prediction in certain situations. The book describes how several important machine learning problems, such as density estimation in high-dimensional spaces, cannot be solved if the only assumption is randomness.
Author : Dimitris N. Politis
Publisher : Springer
ISBN 13 : 3319213474
Total Pages : 256 pages
Book Rating : 4.3/5 (192 download)
Download or read book Model-Free Prediction and Regression written by Dimitris N. Politis and published by Springer. This book was released on 2015-11-13 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Model-Free Prediction Principle expounded upon in this monograph is based on the simple notion of transforming a complex dataset to one that is easier to work with, e.g., i.i.d. or Gaussian. As such, it restores the emphasis on observable quantities, i.e., current and future data, as opposed to unobservable model parameters and estimates thereof, and yields optimal predictors in diverse settings such as regression and time series. Furthermore, the Model-Free Bootstrap takes us beyond point prediction in order to construct frequentist prediction intervals without resort to unrealistic assumptions such as normality. Prediction has been traditionally approached via a model-based paradigm, i.e., (a) fit a model to the data at hand, and (b) use the fitted model to extrapolate/predict future data. Due to both mathematical and computational constraints, 20th century statistical practice focused mostly on parametric models. Fortunately, with the advent of widely accessible powerful computing in the late 1970s, computer-intensive methods such as the bootstrap and cross-validation freed practitioners from the limitations of parametric models, and paved the way towards the `big data' era of the 21st century. Nonetheless, there is a further step one may take, i.e., going beyond even nonparametric models; this is where the Model-Free Prediction Principle is useful. Interestingly, being able to predict a response variable Y associated with a regressor variable X taking on any possible value seems to inadvertently also achieve the main goal of modeling, i.e., trying to describe how Y depends on X. Hence, as prediction can be treated as a by-product of model-fitting, key estimation problems can be addressed as a by-product of being able to perform prediction. In other words, a practitioner can use Model-Free Prediction ideas in order to additionally obtain point estimates and confidence intervals for relevant parameters leading to an alternative, transformation-based approach to statistical inference.
Author : Z. Govindarajulu
Publisher : World Scientific
ISBN 13 : 981270034X
Total Pages : 692 pages
Book Rating : 4.8/5 (127 download)
Download or read book Nonparametric Inference written by Z. Govindarajulu and published by World Scientific. This book was released on 2007 with total page 692 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a solid foundation on nonparametric inference for students taking a graduate course in nonparametric statistics and serves as an easily accessible source for researchers in the area.With the exception of some sections requiring familiarity with measure theory, readers with an advanced calculus background will be comfortable with the material.
Author : Edward Vonesh
Publisher : CRC Press
ISBN 13 : 1482293277
Total Pages : 581 pages
Book Rating : 4.4/5 (822 download)
Download or read book Linear and Nonlinear Models for the Analysis of Repeated Measurements written by Edward Vonesh and published by CRC Press. This book was released on 1996-11-01 with total page 581 pages. Available in PDF, EPUB and Kindle. Book excerpt: Integrates the latest theory, methodology and applications related to the design and analysis of repeated measurement. The text covers a broad range of topics, including the analysis of repeated measures design, general crossover designs, and linear and nonlinear regression models. It also contains a 3.5 IBM compatible disk, with software to implem
Author : J.S. Maritz
Publisher : CRC Press
ISBN 13 : 1000153002
Total Pages : 279 pages
Book Rating : 4.0/5 (1 download)
Download or read book Distribution-Free Statistical Methods, Second Edition written by J.S. Maritz and published by CRC Press. This book was released on 2020-11-26 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: Distribution-free statistical methods enable users to make statistical inferences with minimum assumptions about the population in question. They are widely used, especially in the areas of medical and psychological research. This new edition is aimed at senior undergraduate and graduate level. It also includes a discussion of new techniques that have arisen as a result of improvements in statistical computing. Interest in estimation techniques has particularly grown, and this section of the book has been expanded accordingly. Finally, Distribution-Free Statistical Methods includes more examples with actual data sets appearing in the text.
Author : Murray Aitkin
Publisher : CRC Press
ISBN 13 : 100064457X
Total Pages : 391 pages
Book Rating : 4.0/5 (6 download)
Download or read book Introduction to Statistical Modelling and Inference written by Murray Aitkin and published by CRC Press. This book was released on 2022-09-30 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: The complexity of large-scale data sets (“Big Data”) has stimulated the development of advanced computational methods for analysing them. There are two different kinds of methods to aid this. The model-based method uses probability models and likelihood and Bayesian theory, while the model-free method does not require a probability model, likelihood or Bayesian theory. These two approaches are based on different philosophical principles of probability theory, espoused by the famous statisticians Ronald Fisher and Jerzy Neyman. Introduction to Statistical Modelling and Inference covers simple experimental and survey designs, and probability models up to and including generalised linear (regression) models and some extensions of these, including finite mixtures. A wide range of examples from different application fields are also discussed and analysed. No special software is used, beyond that needed for maximum likelihood analysis of generalised linear models. Students are expected to have a basic mathematical background in algebra, coordinate geometry and calculus. Features • Probability models are developed from the shape of the sample empirical cumulative distribution function (cdf) or a transformation of it. • Bounds for the value of the population cumulative distribution function are obtained from the Beta distribution at each point of the empirical cdf. • Bayes’s theorem is developed from the properties of the screening test for a rare condition. • The multinomial distribution provides an always-true model for any randomly sampled data. • The model-free bootstrap method for finding the precision of a sample estimate has a model-based parallel – the Bayesian bootstrap – based on the always-true multinomial distribution. • The Bayesian posterior distributions of model parameters can be obtained from the maximum likelihood analysis of the model. This book is aimed at students in a wide range of disciplines including Data Science. The book is based on the model-based theory, used widely by scientists in many fields, and compares it, in less detail, with the model-free theory, popular in computer science, machine learning and official survey analysis. The development of the model-based theory is accelerated by recent developments in Bayesian analysis.
Author : Jean Dickinson Gibbons
Publisher : CRC Press
ISBN 13 : 135161617X
Total Pages : 695 pages
Book Rating : 4.3/5 (516 download)
Download or read book Nonparametric Statistical Inference written by Jean Dickinson Gibbons and published by CRC Press. This book was released on 2020-12-21 with total page 695 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for previous editions: "... a classic with a long history." – Statistical Papers "The fact that the first edition of this book was published in 1971 ... [is] testimony to the book’s success over a long period." – ISI Short Book Reviews "... one of the best books available for a theory course on nonparametric statistics. ... very well written and organized ... recommended for teachers and graduate students." – Biometrics "... There is no competitor for this book and its comprehensive development and application of nonparametric methods. Users of one of the earlier editions should certainly consider upgrading to this new edition." – Technometrics "... Useful to students and research workers ... a good textbook for a beginning graduate-level course in nonparametric statistics." – Journal of the American Statistical Association Since its first publication in 1971, Nonparametric Statistical Inference has been widely regarded as the source for learning about nonparametrics. The Sixth Edition carries on this tradition and incorporates computer solutions based on R. Features Covers the most commonly used nonparametric procedures States the assumptions, develops the theory behind the procedures, and illustrates the techniques using realistic examples from the social, behavioral, and life sciences Presents tests of hypotheses, confidence-interval estimation, sample size determination, power, and comparisons of competing procedures Includes an Appendix of user-friendly tables needed for solutions to all data-oriented examples Gives examples of computer applications based on R, MINITAB, STATXACT, and SAS Lists over 100 new references Nonparametric Statistical Inference, Sixth Edition, has been thoroughly revised and rewritten to make it more readable and reader-friendly. All of the R solutions are new and make this book much more useful for applications in modern times. It has been updated throughout and contains 100 new citations, including some of the most recent, to make it more current and useful for researchers.
