Statistical Inference for Ergodic Diffusion Processes

Statistical Inference for Ergodic Diffusion Processes
Author: Yury A. Kutoyants
Publisher: Springer Science & Business Media
Total Pages: 493
Release: 2013-03-09
Genre: Mathematics
ISBN: 144713866X

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The first book in inference for stochastic processes from a statistical, rather than a probabilistic, perspective. It provides a systematic exposition of theoretical results from over ten years of mathematical literature and presents, for the first time in book form, many new techniques and approaches.


Statistical Inference for Ergodic Diffusion Processes
Language: en
Pages: 493
Authors: Yury A. Kutoyants
Categories: Mathematics
Type: BOOK - Published: 2013-03-09 - Publisher: Springer Science & Business Media

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The first book in inference for stochastic processes from a statistical, rather than a probabilistic, perspective. It provides a systematic exposition of theore
Statistical Inferences for Stochasic Processes
Language: en
Pages: 464
Authors: Ishwar V. Basawa
Categories: Mathematics
Type: BOOK - Published: 1980-01-28 - Publisher: Academic Press

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Introductory examples of stochastic models; Special models; General theory; Further approaches.
Asymptotic Theory of Statistical Inference for Time Series
Language: en
Pages: 671
Authors: Masanobu Taniguchi
Categories: Mathematics
Type: BOOK - Published: 2012-12-06 - Publisher: Springer Science & Business Media

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The primary aim of this book is to provide modern statistical techniques and theory for stochastic processes. The stochastic processes mentioned here are not re
Statistical Inferences for Stochasic Processes
Language: en
Pages: 455
Authors: Ishwar V. Basawa
Categories: Mathematics
Type: BOOK - Published: 2014-06-28 - Publisher: Elsevier

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Stats Inference Stochasic Process
Statistical Methods for Stochastic Differential Equations
Language: en
Pages: 509
Authors: Mathieu Kessler
Categories: Mathematics
Type: BOOK - Published: 2012-05-17 - Publisher: CRC Press

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The seventh volume in the SemStat series, Statistical Methods for Stochastic Differential Equations presents current research trends and recent developments in