Associated Sequences, Demimartingales and Nonparametric Inference

This book gives a comprehensive review of results for associated sequences and demimartingales developed so far, with special emphasis on demimartingales and related processes.   One of the basic aims of theory of probability and statistics is to build stochastic models which explain the phenomenon...

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Autor principal: Prakasa Rao, B.L.S
Formato: Livre numérique
Lenguaje:Anglais
Publicado: Basel : Springer Basel [20..].
Cham : Springer Nature
Edición:1st ed. 2012.
Colección:Probability and Its Applications
Acceso en línea:Accès sur la plateforme de l'éditeur
Accès sur la plateforme Istex
Accès Université d'Orléans
Accès INSA CVL
Nota: Archives Springer e-books (Licence nationale)
Archives Springer e-books (Licence nationale)
Autres localisations: Voir dans le Sudoc
Edition sous un autre format:• Associated sequences, demimartingales and nonparametric inference, B.L.S. Prakasa Rao, Basel, Birkhaüser, 2012, 1 vol. (XI-272 p.), Probability and its applications, 978-3-03-480239-0
• Associated Sequences, Demimartingales and Nonparametric Inference, Texte imprimé, 9783034807463
• Associated sequences, demimartingales and nonparametric inference, B.L.S. Prakasa Rao, Basel, Birkhaüser, 2012, 1 vol. (XI-272 p.), Probability and its applications, 978-3-03-480239-0
• Associated Sequences, Demimartingales and Nonparametric Inference, Texte imprimé, 9783034802413
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505 1 |a Preface Associated Random Variables and Related Concepts Demimartingales N-Demimartingales Conditional Demimartingales Multidimensionally Indexed Demimartingales and Continuous Parameter Demimartingales Limit Theorems for Associated Random Variables Nonparametric Estimation for Associated Sequences Nonparametric Tests for Associated Sequences Nonparametric Tests for Change in Marginal Density Function for Associated Sequences References Index 
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520 |a This book gives a comprehensive review of results for associated sequences and demimartingales developed so far, with special emphasis on demimartingales and related processes.   One of the basic aims of theory of probability and statistics is to build stochastic models which explain the phenomenon under investigation and explore the dependence among various covariates which influence this phenomenon. Classic examples are the concepts of Markov dependence or of mixing for random processes. Esary, Proschan and Walkup introduced the concept of association for random variables, and Newman and Wright studied properties of processes termed as demimartingales. It can be shown that the partial sums of mean zero associated random variables form a demimartingale.   Probabilistic properties of associated sequences, demimartingales and related processes are discussed in the first six chapters. Applications of some of these results to problems in nonparametric statistical inference for such processes are investigated in the last three chapters.   This book will appeal to graduate students and researchers interested in probabilistic aspects of various types of stochastic processes and their applications in reliability theory, statistical mechanics, percolation theory and other areas 
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