Bayesian nonparametrics for causal inference and missing data, Daniels, Michael J.

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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Цена: 75030.00 T
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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: Larry Wasserman
Название: All of Nonparametric Statistics
ISBN: 1441920447 ISBN-13(EAN): 9781441920447
Издательство: Springer
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Цена: 102480.00 T
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Описание: It covers a wide range of topics including the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets.

Автор: Bhattacharya
Название: Nonparametric Inference on Manifolds
ISBN: 1107484316 ISBN-13(EAN): 9781107484313
Издательство: Cambridge Academ
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Цена: 40130.00 T
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Описание: Ideal for statisticians, this book will also interest probabilists, mathematicians, computer scientists, and morphometricians with mathematical training. It presents a systematic introduction to a general nonparametric theory of statistics on manifolds, with emphasis on manifolds of shapes. The theory has important applications in medical diagnostics, image analysis and machine vision.

Автор: B.L.S. Prakasa Rao
Название: Associated Sequences, Demimartingales and Nonparametric Inference
ISBN: 3034807465 ISBN-13(EAN): 9783034807463
Издательство: Springer
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Цена: 83850.00 T
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Описание: This book gives a comprehensive review of results for associated sequences and demimartingales developed so far, with special emphasis on demimartingales and related processes. Probabilistic properties of associated sequences, demimartingales and related processes are discussed in the first six chapters.

Автор: Wasserman
Название: All of Nonparametric Statistics
ISBN: 0387251456 ISBN-13(EAN): 9780387251455
Издательство: Springer
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Цена: 139750.00 T
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Описание: It covers a wide range of topics including the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets.

Автор: Phadia
Название: Prior Processes and Their Applications
ISBN: 3319327887 ISBN-13(EAN): 9783319327884
Издательство: Springer
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Цена: 102480.00 T
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Описание: This book presents a systematic and comprehensive treatment of various prior processes that have been developed over the past four decades for dealing with Bayesian approach to solving selected nonparametric inference problems. This revised edition has been substantially expanded to reflect the current interest in this area. After an overview of different prior processes, it examines the now pre-eminent Dirichlet process and its variants including hierarchical processes, then addresses new processes such as dependent Dirichlet, local Dirichlet, time-varying and spatial processes, all of which exploit the countable mixture representation of the Dirichlet process. It subsequently discusses various neutral to right type processes, including gamma and extended gamma, beta and beta-Stacy processes, and then describes the Chinese Restaurant, Indian Buffet and infinite gamma-Poisson processes, which prove to be very useful in areas such as machine learning, information retrieval and featural modeling. Tailfree and Polya tree and their extensions form a separate chapter, while the last two chapters present the Bayesian solutions to certain estimation problems pertaining to the distribution function and its functional based on complete data as well as right censored data. Because of the conjugacy property of some of these processes, most solutions are presented in closed form. However, the current interest in modeling and treating large-scale and complex data also poses a problem – the posterior distribution, which is essential to Bayesian analysis, is invariably not in a closed form, making it necessary to resort to simulation. Accordingly, the book also introduces several computational procedures, such as the Gibbs sampler, Blocked Gibbs sampler and slice sampling, highlighting essential steps of algorithms while discussing specific models. In addition, it features crucial steps of proofs and derivations, explains the relationships between different processes and provides further clarifications to promote a deeper understanding. Lastly, it includes a comprehensive list of references, equipping readers to explore further on their own.

Автор: E.G. Phadia.
Название: Prior Processes and Their Applications
ISBN: 3642392792 ISBN-13(EAN): 9783642392795
Издательство: Springer
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Цена: 83850.00 T
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Описание: This book presents a systematic and comprehensive treatment of various prior processes that have been developed over the last four decades in order to deal with the Bayesian approach to solving some nonparametric inference problems.

Автор: Ghosal, Subhashis.
Название: Fundamentals of Nonparametric Bayesian Inference
ISBN: 0521878268 ISBN-13(EAN): 9780521878265
Издательство: Cambridge Academ
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Цена: 86590.00 T
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Описание: Written by top researchers, this self-contained text is the authoritative account of Bayesian nonparametrics, a nearly universal framework for inference in statistics and machine learning, with practical use in all areas of science, including economics and biostatistics. Appendices with prerequisites and numerous exercises support its use for graduate courses.

Автор: Gibbons, Jean Dickinson , Chakraborti, Subhabrata
Название: Nonparametric Statistical Inference, Sixth Edition
ISBN: 1138087440 ISBN-13(EAN): 9781138087446
Издательство: Taylor&Francis
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Цена: 117390.00 T
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Описание: 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.

Автор: Ghosh, Subir
Название: Asymptotics, Nonparametrics, and Time Series
ISBN: 0824700511 ISBN-13(EAN): 9780824700515
Издательство: Taylor&Francis
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Цена: 433840.00 T
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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
Наличие на складе: Невозможна поставка.
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: Muller, P., Quintana, F.A., Jara, A., Hanson, T.
Название: Bayesian Nonparametric Data Analysis
ISBN: 3319189670 ISBN-13(EAN): 9783319189673
Издательство: Springer
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Цена: 79190.00 T
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Описание: This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones.