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Stochastic processes - inference theory / Malempati M. Rao.

By: Material type: TextTextSeries: Springer monographs in mathematicsPublication details: Switzerland : Springer, 2014.Edition: 2nd edDescription: xvii, 669 p. ; 24 cmISBN:
  • 9783319121710 (hard cover : alk. paper)
Subject(s): DDC classification:
  • 519.23 23 R215
Contents:
1. Introduction and Preliminaries -- 2. Some Principles of Hypothesis Testing -- 3. Parameter Estimation and Asymptotics -- 4. Inferences for Classes of Processes -- 5. Likelihood Ratios for Processes -- 6. Sampling Methods for Processes -- 7. More on Stochastic Inference -- 8. Prediction and Filtering of Processes -- 9. Nonparametric Estimation for Processes -- Bibliography -- Indexes.
Summary: The present volume gives a substantial account of regression analysis, both for stochastic processes and measures, and includes recent material on Ridge regression with some unexpected applications, for example in econometrics. The first three chapters can be used for a quarter or semester graduate course on inference on stochastic processes. The remaining chapters provide more advanced material on stochastic analysis suitable for graduate seminars and discussions, leading to dissertation or research work. In general, the book will be of interest to researchers in probability theory, mathematical statistics and electrical and information theory.
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 519.23 R215 (Browse shelf(Opens below)) Available 136206
Total holds: 0

Includes bibliographical references and indexes.

1. Introduction and Preliminaries --
2. Some Principles of Hypothesis Testing --
3. Parameter Estimation and Asymptotics --
4. Inferences for Classes of Processes --
5. Likelihood Ratios for Processes --
6. Sampling Methods for Processes --
7. More on Stochastic Inference --
8. Prediction and Filtering of Processes --
9. Nonparametric Estimation for Processes --
Bibliography --
Indexes.

The present volume gives a substantial account of regression analysis, both for stochastic processes and measures, and includes recent material on Ridge regression with some unexpected applications, for example in econometrics. The first three chapters can be used for a quarter or semester graduate course on inference on stochastic processes. The remaining chapters provide more advanced material on stochastic analysis suitable for graduate seminars and discussions, leading to dissertation or research work. In general, the book will be of interest to researchers in probability theory, mathematical statistics and electrical and information theory.

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