Posted on 2019-02-22, by nokia241186.
Statistical Inference for Discrete Time Stochastic Processes By M. B. Rajarshi (auth.)
2013 | 113 Pages | ISBN: 8132207629 | PDF | 2 MB
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.
(Buy premium account for maximum speed and resuming ability)
- Ebooks list page : 39373
- 2019-01-31Statistical Inference for Discrete Time Stochastic Processes
- 2017-12-18[PDF] Statistical Inference for Discrete Time Stochastic Processes (SpringerBriefs in Statistics) - Removed
- 2018-12-13Performance Analysis and Synthesis for Discrete-Time Stochastic Systems with Network-Enhanced Comple...
- 2018-08-16Statistical Inference for Piecewise-deterministic Markov Processes
- 2018-08-14Statistical Inference for Piecewise-deterministic Markov Processes
- 2012-03-21Statistical Inference for Fractional Diffusion Processes
- 2012-03-14Sense and Nonsense of Statistical Inference By Charmont Wang
- 2011-11-09Discrete-time Dynamic Models
- 2011-11-01Asymptotic Theory of Statistical Inference for Time Series (Springer Series in Statistics)
- 2011-08-01Asymptotic Theory of Statistical Inference for Time Series (Springer Series in Statistics)
- 2011-07-27Asymptotic Theory of Statistical Inference for Time Series (repost)
- 2011-05-30Statistical Inference for Fractional Diffusion Processes (Wiley Series in Probability and Statistics)
- 2010-12-09Asymptotic Theory of Statistical Inference for Time Series (Repost)
- 2009-03-06Asymptotic Theory of Statistical Inference for Time Series
- 2019-03-05Stochastic Multi-Stage Optimization At the Crossroads between Discrete Time Stochastic Control an... - Removed
- 2019-02-16Statistical inference for data science: A companion to the Coursera Statistical Inference Course
- 2019-01-24Formal Methods for Discrete-Time Dynamical Systems (Studies in Systems, Decision and Control) - Removed
- 2019-01-15Discrete-time Stochastic Systems (2nd edition) - Removed
- 2019-01-11Formal Methods for Discrete-Time Dynamical Systems (Studies in Systems, Decision and Control)
- Download links and password may be in the description section, read description carefully!
- Do a search to find mirrors if no download links or dead links.