Nonparametric Goodness of Fit Testing Under Gaussian Models

Nonparametric Goodness of Fit Testing Under Gaussian Models Author Yuri Ingster
ISBN-10 9780387215808
Year 2012-11-12
Pages 457
Language en
Publisher Springer Science & Business Media
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This book presents the modern theory of nonparametric goodness-of-fit testing. It fills the gap in modern nonparametric statistical theory by discussing hypothesis testing and addresses mathematical statisticians who are interesting in the theory of non-parametric statistical inference. It will be of interest to specialists who are dealing with applied non-parametric statistical problems relevant in signal detection and transmission and in technical and medical diagnostics among others.

Nonlinear Estimation and Classification

Nonlinear Estimation and Classification Author David D. Denison
ISBN-10 9780387215792
Year 2013-11-11
Pages 477
Language en
Publisher Springer Science & Business Media
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Researchers in many disciplines face the formidable task of analyzing massive amounts of high-dimensional and highly-structured data. This is due in part to recent advances in data collection and computing technologies. As a result, fundamental statistical research is being undertaken in a variety of different fields. Driven by the complexity of these new problems, and fueled by the explosion of available computer power, highly adaptive, non-linear procedures are now essential components of modern "data analysis," a term that we liberally interpret to include speech and pattern recognition, classification, data compression and signal processing. The development of new, flexible methods combines advances from many sources, including approximation theory, numerical analysis, machine learning, signal processing and statistics. The proposed workshop intends to bring together eminent experts from these fields in order to exchange ideas and forge directions for the future.

Parametric and Nonparametric Inference from Record Breaking Data

Parametric and Nonparametric Inference from Record Breaking Data Author Sneh Gulati
ISBN-10 9780387215495
Year 2013-03-14
Pages 117
Language en
Publisher Springer Science & Business Media
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By providing a comprehensive look at statistical inference from record-breaking data in both parametric and nonparametric settings, this book treats the area of nonparametric function estimation from such data in detail. Its main purpose is to fill this void on general inference from record values. Statisticians, mathematicians, and engineers will find the book useful as a research reference. It can also serve as part of a graduate-level statistics or mathematics course.

Block Designs A Randomization Approach

Block Designs  A Randomization Approach Author Tadeusz Calinski
ISBN-10 9781441992468
Year 2012-12-06
Pages 357
Language en
Publisher Springer Science & Business Media
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The book is composed of two volumes, each consisting of five chapters. In Vol ume I, following some statistical motivation based on a randomization model, a general theory of the analysis of experiments in block designs has been de veloped. In the present Volume II, the primary aim is to present methods of that satisfy the statistical requirements described in constructing block designs Volume I, particularly those considered in Chapters 3 and 4, and also to give some catalogues of plans of the designs. Thus, the constructional aspects are of predominant interest in Volume II, with a general consideration given in Chapter 6. The main design investigations are systematized by separating the material into two contents, depending on whether the designs provide unit efficiency fac tors for some contrasts of treatment parameters (Chapter 7) or not (Chapter 8). This distinction in classifying block designs may be essential from a prac tical point of view. In general, classification of block designs, whether proper or not, is based here on efficiency balance (EB) in the sense of the new termi nology proposed in Section 4. 4 (see, in particular, Definition 4. 4. 2). Most of the attention is given to connected proper designs because of their statistical advantages as described in Volume I, particularly in Chapter 3. When all con trasts are of equal importance, either the class of (v - 1; 0; O)-EB designs, i. e.

Topics in Stochastic Analysis and Nonparametric Estimation

Topics in Stochastic Analysis and Nonparametric Estimation Author Pao-Liu Chow
ISBN-10 9780387751115
Year 2010-07-19
Pages 214
Language en
Publisher Springer Science & Business Media
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To honor Rafail Z. Khasminskii, on his seventy-fifth birthday, for his contributions to stochastic processes and nonparametric estimation theory an IMA participating institution conference entitled "Conference on Asymptotic Analysis in Stochastic Processes, Nonparametric Estimation, and Related Problems" was held. This volume commemorates this special event. Dedicated to Professor Khasminskii, it consists of nine papers on various topics in probability and statistics.

Testing Statistical Hypotheses

Testing Statistical Hypotheses Author Erich L. Lehmann
ISBN-10 9780387276052
Year 2006-03-30
Pages 786
Language en
Publisher Springer Science & Business Media
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The third edition of Testing Statistical Hypotheses updates and expands upon the classic graduate text, emphasizing optimality theory for hypothesis testing and confidence sets. The principal additions include a rigorous treatment of large sample optimality, together with the requisite tools. In addition, an introduction to the theory of resampling methods such as the bootstrap is developed. The sections on multiple testing and goodness of fit testing are expanded. The text is suitable for Ph.D. students in statistics and includes over 300 new problems out of a total of more than 760.

Statistical Models and Methods for Biomedical and Technical Systems

Statistical Models and Methods for Biomedical and Technical Systems Author Filia Vonta
ISBN-10 0817646191
Year 2008-03-05
Pages 556
Language en
Publisher Springer Science & Business Media
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This book deals with the mathematical aspects of survival analysis and reliability as well as other topics, reflecting recent developments in the following areas: applications in epidemiology; probabilistic and statistical models and methods in reliability; models and methods in survival analysis, longevity, aging, and degradation; accelerated life models; quality of life; new statistical challenges in genomics. The work will be useful to a broad interdisciplinary readership of researchers and practitioners in applied probability and statistics, industrial statistics, biomedicine, biostatistics, and engineering.

Introduction to Nonparametric Estimation

Introduction to Nonparametric Estimation Author Alexandre B. Tsybakov
ISBN-10 9780387790527
Year 2008-10-22
Pages 214
Language en
Publisher Springer Science & Business Media
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Developed from lecture notes and ready to be used for a course on the graduate level, this concise text aims to introduce the fundamental concepts of nonparametric estimation theory while maintaining the exposition suitable for a first approach in the field.

Random Effect and Latent Variable Model Selection

Random Effect and Latent Variable Model Selection Author David Dunson
ISBN-10 0387767215
Year 2010-03-18
Pages 170
Language en
Publisher Springer Science & Business Media
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Random Effect and Latent Variable Model Selection In recent years, there has been a dramatic increase in the collection of multivariate and correlated data in a wide variety of ?elds. For example, it is now standard pr- tice to routinely collect many response variables on each individual in a study. The different variables may correspond to repeated measurements over time, to a battery of surrogates for one or more latent traits, or to multiple types of outcomes having an unknown dependence structure. Hierarchical models that incorporate subje- speci?c parameters are one of the most widely-used tools for analyzing multivariate and correlated data. Such subject-speci?c parameters are commonly referred to as random effects, latent variables or frailties. There are two modeling frameworks that have been particularly widely used as hierarchical generalizations of linear regression models. The ?rst is the linear mixed effects model (Laird and Ware , 1982) and the second is the structural equation model (Bollen , 1989). Linear mixed effects (LME) models extend linear regr- sion to incorporate two components, with the ?rst corresponding to ?xed effects describing the impact of predictors on the mean and the second to random effects characterizing the impact on the covariance. LMEs have also been increasingly used for function estimation. In implementing LME analyses, model selection problems are unavoidable. For example, there may be interest in comparing models with and without a predictor in the ?xed and/or random effects component.

Bayesian Theory and Applications

Bayesian Theory and Applications Author David A. Stephens
ISBN-10 9780199695607
Year 2013-01-24
Pages 702
Language en
Publisher Oxford University Press
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This volume guides the reader along a statistical journey that begins with the basic structure of Bayesian theory, and then provides details on most of the past and present advances in this field.