Statistical Inference for Markov Processes PDF Download
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Author: Patrick Billingsley
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 100
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Author: Patrick Billingsley
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 100
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Author: Romain Azais
Publisher: John Wiley & Sons
ISBN: 1119544033
Category : Mathematics
Languages : en
Pages : 300
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Book Description
Piecewise-deterministic Markov processes form a class of stochastic models with a sizeable scope of applications: biology, insurance, neuroscience, networks, finance... Such processes are defined by a deterministic motion punctuated by random jumps at random times, and offer simple yet challenging models to study. Nevertheless, the issue of statistical estimation of the parameters ruling the jump mechanism is far from trivial. Responding to new developments in the field as well as to current research interests and needs, Statistical inference for piecewise-deterministic Markov processes offers a detailed and comprehensive survey of state-of-the-art results. It covers a wide range of general processes as well as applied models. The present book also dwells on statistics in the context of Markov chains, since piecewise-deterministic Markov processes are characterized by an embedded Markov chain corresponding to the position of the process right after the jumps.
Author: Patrick Billingsley
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Walter F. Johnson
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Ishwar V. Basawa
Publisher: Academic Press
ISBN:
Category : Mathematics
Languages : en
Pages : 464
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Book Description
Introductory examples of stochastic models; Special models; General theory; Further approaches.
Author: N.U. Prabhu
Publisher: CRC Press
ISBN: 1000104532
Category : Mathematics
Languages : en
Pages : 289
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Book Description
Covering both theory and applications, this collection of eleven contributed papers surveys the role of probabilistic models and statistical techniques in image analysis and processing, develops likelihood methods for inference about parameters that determine the drift and the jump mechanism of a di
Author: Patrick Billingsley
Publisher:
ISBN:
Category :
Languages : en
Pages : 75
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Author: M. B. Rajarshi
Publisher: Springer Science & Business Media
ISBN: 8132207637
Category : Mathematics
Languages : en
Pages : 121
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Book Description
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.
Author: Ishwar V. Basawa
Publisher: Elsevier
ISBN: 1483296148
Category : Mathematics
Languages : en
Pages : 455
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Book Description
Stats Inference Stochasic Process
Author: Said Mohamed Rujbani
Publisher:
ISBN:
Category : Markov processes
Languages : en
Pages : 226
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Book Description
