| 書目名稱 | Statistical Inference for Discrete Time Stochastic Processes |
| 編輯 | M. B. Rajarshi |
| 視頻video | http://file.papertrans.cn/877/876438/876438.mp4 |
| 概述 | The book deals with classical as well as most recent developments in the area of inference in discrete time stationary stochastic processes.Topics discussed include Markov chains, non-Gaussian sequenc |
| 叢書名稱 | SpringerBriefs in Statistics |
| 圖書封面 |  |
| 描述 | 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 sequ |
| 出版日期 | Book 2013 |
| 關鍵詞 | Bootstrap; Estimating Functions; Non-Gaussian Sequences; Stationary Random Sequences; Statistical Infere |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-81-322-0763-4 |
| isbn_softcover | 978-81-322-0762-7 |
| isbn_ebook | 978-81-322-0763-4Series ISSN 2191-544X Series E-ISSN 2191-5458 |
| issn_series | 2191-544X |
| copyright | The Author(s) 2013 |
|Archiver|手機版|小黑屋|
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