Statistical Inference for Discrete Time Stochastic Processes

Statistical Inference for Discrete Time Stochastic Processes
Author :
Publisher : Springer Science & Business Media
Total Pages : 121
Release :
ISBN-10 : 9788132207627
ISBN-13 : 8132207629
Rating : 4/5 (629 Downloads)

Book Synopsis Statistical Inference for Discrete Time Stochastic Processes by : M. B. Rajarshi

Download or read book Statistical Inference for Discrete Time Stochastic Processes written by M. B. Rajarshi and published by Springer Science & Business Media. This book was released on 2012-10-05 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work is an overview of statistical inference in stationary, discrete time stochastic processes. Results in the last fifteen years, particularly on non-Gaussian sequences and semi-parametric and non-parametric analysis have been reviewed. The first chapter gives a background of results on martingales and strong mixing sequences, which enable us to generate various classes of CAN estimators in the case of dependent observations. Topics discussed include inference in Markov chains and extension of Markov chains such as Raftery's Mixture Transition Density model and Hidden Markov chains and extensions of ARMA models with a Binomial, Poisson, Geometric, Exponential, Gamma, Weibull, Lognormal, Inverse Gaussian and Cauchy as stationary distributions. It further discusses applications of semi-parametric methods of estimation such as conditional least squares and estimating functions in stochastic models. Construction of confidence intervals based on estimating functions is discussed in some detail. Kernel based estimation of joint density and conditional expectation are also discussed. Bootstrap and other resampling procedures for dependent sequences such as Markov chains, Markov sequences, linear auto-regressive moving average sequences, block based bootstrap for stationary sequences and other block based procedures are also discussed in some detail. This work can be useful for researchers interested in knowing developments in inference in discrete time stochastic processes. It can be used as a material for advanced level research students.


Statistical Inference for Discrete Time Stochastic Processes Related Books

Statistical Inference for Discrete Time Stochastic Processes
Language: en
Pages: 121
Authors: M. B. Rajarshi
Categories: Mathematics
Type: BOOK - Published: 2012-10-05 - Publisher: Springer Science & Business Media

GET EBOOK

This work is an overview of statistical inference in stationary, discrete time stochastic processes. Results in the last fifteen years, particularly on non-Gaus
Statistical Inferences for Stochasic Processes
Language: en
Pages: 464
Authors: Ishwar V. Basawa
Categories: Mathematics
Type: BOOK - Published: 1980-01-28 - Publisher: Academic Press

GET EBOOK

Introductory examples of stochastic models; Special models; General theory; Further approaches.
Bayesian Inference for Stochastic Processes
Language: en
Pages: 409
Authors: Lyle D. Broemeling
Categories: Mathematics
Type: BOOK - Published: 2017-12-12 - Publisher: CRC Press

GET EBOOK

This is the first book designed to introduce Bayesian inference procedures for stochastic processes. There are clear advantages to the Bayesian approach (includ
Statistical Inference for Diffusion Type Processes
Language: en
Pages: 0
Authors: B.L.S. Prakasa Rao
Categories: Mathematics
Type: BOOK - Published: 2010-05-24 - Publisher: Wiley

GET EBOOK

Decision making in all spheres of activity involves uncertainty. If rational decisions have to be made, they have to be based on the past observations of the ph
Statistical Analysis of Stochastic Processes in Time
Language: en
Pages: 356
Authors: J. K. Lindsey
Categories: Mathematics
Type: BOOK - Published: 2004-08-02 - Publisher: Cambridge University Press

GET EBOOK

This book was first published in 2004. Many observed phenomena, from the changing health of a patient to values on the stock market, are characterised by quanti