Asymptotics, Nonparametrics, and Time Series, Ghosh, Subir

Автор: Faghih, Nezameddin Bonyadi, Ebrahim Sarreshtehdari, Lida
Название: Quality management and operations research
ISBN: 0367744902 ISBN-13(EAN): 9780367744908
Издательство: Taylor&Francis
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Описание: Offering a step-by-step approach for applying the Nonparametric Method with the Bayesian Approach to model complex relationships occurring in Reliability Engineering, Quality Management, and Operations Research, it also discusses survival and censored data, accelerated lifetime tests (issues in reliability data analysis), and R codes. This book uses the Nonparametric Bayesian approach in the fields of quality management and operations research. It presents a step-by-step approach for understanding and implementing these models, as well as includes R codes which can be used in any dataset. The book helps the readers to use statistical models in studying complex concepts and applying them to Operations Research, Industrial Engineering, Manufacturing Engineering, Computer Science, Quality and Reliability, Maintenance Planning and Operations Management.This book helps researchers, analysts, investigators, designers, producers, industrialists, entrepreneurs, and financial market decision makers, with finding the lifetime model of products, and for crucial decision-making in other markets.

Автор: Kolassa, John E.
Название: An Introduction to nonparametric statistics
ISBN: 0367194848 ISBN-13(EAN): 9780367194840
Издательство: Taylor&Francis
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Описание: This book presents the theory and practice of non-parametric statistics, with an emphasis on motivating principals. The course is a combination of traditional rank based methods and more computationally-intensive topics like density estimation, kernel smoothers in regression, and robustness. The text is aimed at MS students.

Автор: Daniil Ryabko
Название: Asymptotic Nonparametric Statistical Analysis of Stationary Time Series
ISBN: 3030125637 ISBN-13(EAN): 9783030125639
Издательство: Springer
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Цена: 46570.00 T
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Описание: Stationarity is a very general, qualitative assumption, that can be assessed on the basis of application specifics. It is thus a rather attractive assumption to base statistical analysis on, especially for problems for which less general qualitative assumptions, such as independence or finite memory, clearly fail. However, it has long been considered too general to be able to make statistical inference. One of the reasons for this is that rates of convergence, even of frequencies to the mean, are not available under this assumption alone. Recently, it has been shown that, while some natural and simple problems, such as homogeneity, are indeed provably impossible to solve if one only assumes that the data is stationary (or stationary ergodic), many others can be solved with rather simple and intuitive algorithms. The latter include clustering and change point estimation among others. In this volume I summarize these results. The emphasis is on asymptotic consistency, since this the strongest property one can obtain assuming stationarity alone. While for most of the problem for which a solution is found this solution is algorithmically realizable, the main objective in this area of research, the objective which is only partially attained, is to understand what is possible and what is not possible to do for stationary time series. The considered problems include homogeneity testing (the so-called two sample problem), clustering with respect to distribution, clustering with respect to independence, change point estimation, identity testing, and the general problem of composite hypotheses testing. For the latter problem, a topological criterion for the existence of a consistent test is presented. In addition, a number of open problems is presented.

Автор: Gao, Jiti
Название: Nonlinear Time Series
ISBN: 0367389355 ISBN-13(EAN): 9780367389352
Издательство: Taylor&Francis
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Цена: 65320.00 T
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Useful in the theoretical and empirical analysis of nonlinear time series data, semiparametric methods have received extensive attention in the economics and statistics communities over the past twenty years. Recent studies show that semiparametric methods and models may be applied to solve dimensionality reduction problems arising from using fully nonparametric models and methods. Answering the call for an up-to-date overview of the latest developments in the field, Nonlinear Time Series: Semiparametric and Nonparametric Methods focuses on various semiparametric methods in model estimation, specification testing, and selection of time series data.

After a brief introduction, the book examines semiparametric estimation and specification methods and then applies these approaches to a class of nonlinear continuous-time models with real-world data. It also assesses some newly proposed semiparametric estimation procedures for time series data with long-range dependence. Even though the book only deals with climatological and financial data, the estimation and specifications methods discussed can be applied to models with real-world data in many disciplines.

This resource covers key methods in time series analysis and provides the necessary theoretical details. The latest applied finance and financial econometrics results and applications presented in the book enable researchers and graduate students to keep abreast of developments in the field.

Автор: Desu, M.M. , Raghavarao, D.
Название: Nonparametric Statistical Methods For Complete and Censored Data
ISBN: 0367394952 ISBN-13(EAN): 9780367394950
Издательство: Taylor&Francis
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Balancing the "cookbook" approach of some texts with the more mathematical approach of others, Nonparametric Statistical Methods for Complete and Censored Data introduces commonly used non-parametric methods for complete data and extends those methods to right censored data analysis. Whenever possible, the authors derive their methodology from the general theory of statistical inference and introduce the concepts intuitively for students with minimal backgrounds. Derivations and mathematical details are relegated to appendices at the end of each chapter, which allows students to easily proceed through each chapter without becoming bogged down in a lot of mathematics.

In addition to the nonparametric methods for analyzing complete and censored data, the book covers optimal linear rank statistics, clinical equivalence, analysis of block designs, and precedence tests. To make the material more accessible and practical, the authors use SAS programs to illustrate the various methods included.

Автор: Jureckov?, Jana
Название: Methodology in Robust and Nonparametric Statistics
ISBN: 1439840687 ISBN-13(EAN): 9781439840689
Издательство: Taylor&Francis
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Автор: Sam Efromovich
Название: Nonparametric Curve Estimation
ISBN: 1475773013 ISBN-13(EAN): 9781475773019
Издательство: Springer
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Цена: 93160.00 T
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Описание: This book gives a systematic, comprehensive, and unified account of modern nonparametric statistics of density estimation, nonparametric regression, filtering signals, and time series analysis.

Автор: Gao, Jiti
Название: Nonlinear Time Series
ISBN: 1584886137 ISBN-13(EAN): 9781584886136
Издательство: Taylor&Francis
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Название: Asymptotics, Nonparametrics, and Time Series
ISBN: 036739992X ISBN-13(EAN): 9780367399924
Издательство: Taylor&Francis
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Цена: 65320.00 T
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Описание: "Contains over 2500 equations and exhaustively covers not only nonparametrics but also parametric, semiparametric, frequentist, Bayesian, bootstrap, adaptive, univariate, and multivariate statistical methods, as well as practical uses of Markov chain models."

Автор: Groeneboom
Название: Nonparametric Estimation under Shape Constraints
ISBN: 0521864011 ISBN-13(EAN): 9780521864015
Издательство: Cambridge Academ
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Цена: 79200.00 T
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Описание: This book treats the latest developments in the theory of order-restricted inference, with special attention to nonparametric methods and algorithmic aspects. Each chapter ends with a set of exercises of varying difficulty. The theory is illustrated with the analysis of real-life data, which are mostly medical in nature.

Автор: Pranab Kumar Sen
Название: Theory and Applications of Sequential Nonparametrics
ISBN: 0898710510 ISBN-13(EAN): 9780898710519
Издательство: Mare Nostrum (Eurospan)
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Цена: 51830.00 T
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Описание: A study of sequential nonparametric methods emphasizing the unified Martingale approach to the theory, with a detailed explanation of major applications including problems arising in clinical trials, life-testing experimentation, survival analysis, classical sequential analysis and other areas of applied statistics and biostatistics.

Автор: Ghosh J.K., Ramamoorthi R.V.
Название: Bayesian Nonparametrics
ISBN: 0387955372 ISBN-13(EAN): 9780387955377
Издательство: Springer
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Цена: 153720.00 T
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Описание: 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 offers a whole continuous spectrum of robust alternatives to purely parametric and purely nonparametric methods of classical statistics. The book is primarily aimed at graduate students and can be used as the text for a graduate course in Bayesian nonparametrics. Though the emphasis of the book is on nonparametrics, there is a substantial chapter onJayanta Ghosh has been Director and Jawaharlal Nehru Professor at the Indian Statistical Institute and President of the International Statistical Institute. He is currently professor of statistics at Purdue University. He has been editor of Sankhya and served on the editorial boards of several journals including the Annals of Statistics. Apart from Bayesian analysis, his interests include asymptotics, stochastic modeling, high dimensional model selection, reliability and survival analysis and bioinformatics.R.V. Ramamoorthi is professor at the Department of Statistics and Probability at Michigan State University. He has published papers in the areas of sufficiency invariance, comparison of experiments, nonparametric survival analysis and Bayesian analysis. In addition to Bayesian nonparametrics, he is currently interested in Bayesian networks and graphical models. He is on the editorial board of Sankhya.