Nonparametric Goodness Of Fit Testing Under Gaussian Models
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Nonparametric Goodness Of Fit Testing Under Gaussian Models
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Author : Yuri Ingster
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-11-12
Nonparametric Goodness Of Fit Testing Under Gaussian Models written by Yuri Ingster and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-11-12 with Mathematics categories.
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.
Nonparametric Goodness Of Fit Testing Under Gaussian Models
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Author : Yuri Ingster
language : en
Publisher:
Release Date : 2002-10-29
Nonparametric Goodness Of Fit Testing Under Gaussian Models written by Yuri Ingster and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-10-29 with categories.
Parametric And Nonparametric Inference From Record Breaking Data
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Author : Sneh Gulati
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-14
Parametric And Nonparametric Inference From Record Breaking Data written by Sneh Gulati and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-14 with Mathematics categories.
As statisticians, we are constantly trying to make inferences about the underlying population from which data are observed. This includes estimation and prediction about the underlying population parameters from both complete and incomplete data. Recently, methodology for estimation and prediction from incomplete data has been found useful for what is known as "record-breaking data," that is, data generated from setting new records. There has long been a keen interest in observing all kinds of records-in particular, sports records, financial records, flood records, and daily temperature records, to mention a few. The well-known Guinness Book of World Records is full of this kind of record information. As usual, beyond the general interest in knowing the last or current record value, the statistical problem of prediction of the next record based on past records has also been an important area of record research. Probabilistic and statistical models to describe behavior and make predictions from record-breaking data have been developed only within the last fifty or so years, with a relatively large amount of literature appearing on the subject in the last couple of decades. This book, written from a statistician's perspective, is not a compilation of "records," rather, it deals with the statistical issues of inference from a type of incomplete data, record-breaking data, observed as successive record values (maxima or minima) arising from a phenomenon or situation under study. Prediction is just one aspect of statistical inference based on observed record values.
Introduction To Nonparametric Estimation
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Author : Alexandre B. Tsybakov
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-10-22
Introduction To Nonparametric Estimation written by Alexandre B. Tsybakov and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-10-22 with Mathematics categories.
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.
Nonlinear Estimation And Classification
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Author : David D. Denison
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-11
Nonlinear Estimation And Classification written by David D. Denison and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-11 with Mathematics categories.
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.
Block Designs A Randomization Approach
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Author : Tadeusz Calinski
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Block Designs A Randomization Approach written by Tadeusz Calinski and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
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.
Statistical Models And Methods For Reliability And Survival Analysis
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Author : Vincent Couallier
language : en
Publisher: John Wiley & Sons
Release Date : 2013-12-31
Statistical Models And Methods For Reliability And Survival Analysis written by Vincent Couallier and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-31 with Mathematics categories.
Statistical Models and Methods for Reliability and Survival Analysis brings together contributions by specialists in statistical theory as they discuss their applications providing up-to-date developments in methods used in survival analysis, statistical goodness of fit, stochastic processes for system reliability, amongst others. Many of these are related to the work of Professor M. Nikulin in statistics over the past 30 years. The authors gather together various contributions with a broad array of techniques and results, divided into three parts - Statistical Models and Methods, Statistical Models and Methods in Survival Analysis, and Reliability and Maintenance. The book is intended for researchers interested in statistical methodology and models useful in survival analysis, system reliability and statistical testing for censored and non-censored data.
Foundations Of Statistical Inference
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Author : Yoel Haitovsky
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Foundations Of Statistical Inference written by Yoel Haitovsky and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
This volume is a collection of papers presented at a conference held in Shoresh Holiday Resort near Jerusalem, Israel, in December 2000 organized by the Israeli Ministry of Science, Culture and Sport. The theme of the conference was "Foundation of Statistical Inference: Applications in the Medical and Social Sciences and in Industry and the Interface of Computer Sciences". The following is a quotation from the Program and Abstract booklet of the conference. "Over the past several decades, the field of statistics has seen tremendous growth and development in theory and methodology. At the same time, the advent of computers has facilitated the use of modern statistics in all branches of science, making statistics even more interdisciplinary than in the past; statistics, thus, has become strongly rooted in all empirical research in the medical, social, and engineering sciences. The abundance of computer programs and the variety of methods available to users brought to light the critical issues of choosing models and, given a data set, the methods most suitable for its analysis. Mathematical statisticians have devoted a great deal of effort to studying the appropriateness of models for various types of data, and defining the conditions under which a particular method work. " In 1985 an international conference with a similar title* was held in Is rael. It provided a platform for a formal debate between the two main schools of thought in Statistics, the Bayesian, and the Frequentists.
Mathematical Foundations Of Infinite Dimensional Statistical Models
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Author : Evarist Giné
language : en
Publisher: Cambridge University Press
Release Date : 2016
Mathematical Foundations Of Infinite Dimensional Statistical Models written by Evarist Giné and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Business & Economics categories.
This book develops the theory of statistical inference in statistical models with an infinite-dimensional parameter space, including mathematical foundations and key decision-theoretic principles.
Statistical Inference Via Convex Optimization
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Author : Anatoli Juditsky
language : en
Publisher: Princeton University Press
Release Date : 2020-04-07
Statistical Inference Via Convex Optimization written by Anatoli Juditsky and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-07 with Mathematics categories.
This authoritative book draws on the latest research to explore the interplay of high-dimensional statistics with optimization. Through an accessible analysis of fundamental problems of hypothesis testing and signal recovery, Anatoli Juditsky and Arkadi Nemirovski show how convex optimization theory can be used to devise and analyze near-optimal statistical inferences. Statistical Inference via Convex Optimization is an essential resource for optimization specialists who are new to statistics and its applications, and for data scientists who want to improve their optimization methods. Juditsky and Nemirovski provide the first systematic treatment of the statistical techniques that have arisen from advances in the theory of optimization. They focus on four well-known statistical problems—sparse recovery, hypothesis testing, and recovery from indirect observations of both signals and functions of signals—demonstrating how they can be solved more efficiently as convex optimization problems. The emphasis throughout is on achieving the best possible statistical performance. The construction of inference routines and the quantification of their statistical performance are given by efficient computation rather than by analytical derivation typical of more conventional statistical approaches. In addition to being computation-friendly, the methods described in this book enable practitioners to handle numerous situations too difficult for closed analytical form analysis, such as composite hypothesis testing and signal recovery in inverse problems. Statistical Inference via Convex Optimization features exercises with solutions along with extensive appendixes, making it ideal for use as a graduate text.