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Nonparametric Statistical Methods
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Myles Hollander, Douglas A. Wolfe
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 ...
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Nonparametric Statistics for the Behavioral Sciences
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Sidney Siegel
This is the revision of the classic text in the field, adding two new chapters and thoroughly updating all others. The original structure is retained, and the book continues to serve as a combined text/reference.
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Semiparametric Regression
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David Ruppert, M P Wand, R J Carroll
Semiparametric regression is concerned with the flexible incorporation of non-linear functional relationships in regression analyses. Any application area that benefits from regression analysis can also benefit from semiparametric regression. Assuming only a basic familiarity with ordinary parametric regression, this user-friendly book explains ...
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Practical nonparametric statistics
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W J Conover
A self-contained introduction to the theory and methods of non-parametric statistics. Presents a review of probability theory and statistical inference and covers tests based on binomial and multinomial distributions and methods based on ranks and empirical distributions. Includes a thorough collection of statistics tables, hundreds of problems ...
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Practical Nonparametric STATS 3e
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W J Conover
This highly-regarded text serves as a quick reference book which offers clear, concise instructions on how and when to use the most popular nonparametric procedures. This edition features some procedures that have withstood the test of time and are now used by many practitioners, such as the Fisher Exact Test for two-by-two contingency tables, the ...
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Introduction to Modern Nonparametric Statistics
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James J. Higgins
Guided by problems that frequently arise in actual practice, James Higginsa book presents a wide array of nonparametric methods of data analysis that researchers will find useful. It discusses a variety of nonparametric methods and, wherever possible, stresses the connection between methods. For instance, rank tests are introduced as special cases ...
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Applied Nonparametric Statistics
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Wayne W. Daniel
This book covers the most commonly used nonparametric statistical techniques by emphasizing applications rather than theory. Exercises and examples are drawn from various disciplines including agriculture, biology, sociology, education, psychology, medicine, business, geology, and anthropology. The applications of techniques are presented in a ...
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Applied Nonparametric Statistical Methods
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Peter Sprent
This new edition follows the basic easy-to-digest pattern that was so well received by users of the earlier editions. The authors substantially update and expand "Applied Nonparametric Statistical Methods" to reflect changing attitudes towards applied statistics, new developments, and the impact of more widely available and better statistical ...
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Nonparametric Statistics with Applications to Science and Engineering
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Paul H Kvam, Brani Vidakovic
This book presents modern nonparametric statistics from a practical point of view. It is primarily intended for use with engineers and scientists. While the book covers the necessary theorems and methods of rank tests in an applied fashion, the novelty lies in its emphasis on modern nonparametric methods in regression and curve fitting, bootstrap ...
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Nonparametrics: Statistical Methods Based on Ranks
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by
E L Lehmann
Rank tests form a class of statistical procedures that have the advantage of great simplicity combined with surprising power. Since their development in the 1940s and 1950s, they have taken their place as strong competitors of the more classical normal theory methods. Rank tests apply only to relatively simple solutions, such as one-, two-, and s ...
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All of Nonparametric Statistics
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Larry Wasserman
The goal of this text is to provide the reader with a single book where they can find a brief account of many, modern topics in nonparametric inference. The book is aimed at Master's level or Ph.D. level students in statistics, computer science, and engineering. It is also suitable for researchers who want to get up to speed quickly on modern ...
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Nonparametric Statistics: An Introduction
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Jean Dickinson Gibbons
Through the use of actual research investigations that have appeared in recent social science journals, Gibbons shows the reader the specific methodology and logical rationale for many of the best-known and most frequently used nonparametric methods that are applicable to most small and large sample sizes. The methods are organized according to ...
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Progressive Censoring: Theory, Methods, and Applications
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Rita Aggarwala, N Balakrishnan
This new volume offers a throughout guide to the theory and methods of progressive censoring for practitioners and professionals in applied statistics, quality control, life testing and reliability testing. In many industrial experiments involving lifetimes of machines or units, experiments have to be terminated early due to a variety of ...
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Robust Nonparametric Statistical Methods
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Thomas P. Hettmansperger, J. W. McKean
Traditional statistical procedures are widely used because they offer the user a unified methodology with which to attack a multitude of problems, from simple location problems to highly complex experimental designs. These procedures are based on least squares fitting, but can be easily impaired by outlying observations. Indeed one outlying ...
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Unified Methods for Censored Longitudinal Data and Causality
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Mark J Van Der Laan, Mark J Van Der Laan, James Robins
During the last decades, there has been an explosion in computation and information technology. This development comes with an expansion of complex observational studies and clinical trials in a variety of fields such as medicine, biology, epidemiology, sociology, and economics among many others, which involve collection of large amounts of data ...
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Nonparametric Curve Estimation: Methods, Theory and Applications
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by
Sam Efromovich
Appropriate for a one-semester course, this self-contained book is an introduction to nonparametric curve estimation theory. It may be used for teaching graduate students in statistics (in this case an intermediate statistical inference, on the level of the book by G. Casella and R. Berger (1990) "Statistical Inference", Brooks/Cole, is the ...
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Parametric and Nonparametric Inference from Record-Breaking Data
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Sneh Gulati, James J Dugundji, William J Padgett
This book provides a comprehensive look at statistical inference from record-breaking data in both parametric and nonparametric settings, including Bayesian inference. A unique feature is that it treats the area of nonparametric function estimation from such data in detail, gathering results on this topic to date in one accessible volume. Previous ...
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Nonparametric Geostatistics
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Stephen Henley
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Exploring the Limits of Bootstrap
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Raoul Lepage (Editor), Lynne Billard
The bootstrap method is a nonparametric technique used to infer the distribution of a statistic derived from a sample. This introduction to the subject explores its applications to problems that place unusual demands on the method.
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Multivariate Permutation Tests: With Applications in Biostatistics
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Fortunato Pesarin
Complex multivariate problems are frequently encountered in many scientific disciplines and it can be very difficult to obtain meaningful results. Permutation and nonparametric combination methods provide flexible solutions to complex problems by reducing the problem down to a set of simpler sub-problems. The author presents a novel but well ...
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Bayesian Nonparametrics
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J K Ghosh, Paul R Rosenbaum, R V Ramamoorthi
Bayesian nonparametrics has grown tremendously in the last three decades, especially in the last few years. This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. While the book is of special interest to Bayesians, it will also appeal to statisticians in general because Bayesian nonparametrics ...
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Applied Nonparametric Regression
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Härdle
Applied Nonparametric Regression is the first book to bring together in one place the techniques for regression curve smoothing involving more than one variable. The computer and the development of interactive graphics programs have made curve estimation possible. This volume focuses on the applications and practical problems of two central ...
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A SAS Companion for Nonparametric Statistics
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Scott J Richter, James J Higgins
Can serve as a SAS lab manual for students enrolled in a course covering nonparametric methods where SAS is used for computing and is also a reference for researchers currently using SAS, and wishing to learn about the capabilities of SAS for performing nonparametric analyses and the syntax for implementing the procedures.
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Nonparametric Smoothing and Lack-Of-Fit Tests
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by
Jeffrey D. Hart
A fundamental problem in statistical analysis is checking how well a particular probability model fits a set of observed data. In many settings, nonparametric smoothing methods provide a convenient and powerful means of testing model fit. Nonparametric Smoothing and Lack-of-Fit Tests explores the use of smoothing methods in testing the fit of ...
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Bibliography of Nonparametric Statistics
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by
I Richard Savage
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