Titlebook: Stochastic Processes - Inference Theory; Malempati M. Rao Book 2014Latest edition Springer International Publishing Switzerland 2014 60Gxx

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Introduction and Preliminaries,s to be tested as well as the associated parameter estimation from observations. Then, how both these questions can be unified into a broad framework of a decision theory is discussed. These ideas will be elaborated later on and then their application to various classes of stochastic processes will

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Parameter Estimation and Asymptotics,parameters. That involves various classes of loss functions, and the study concentrates on certain desirable properties of estimators which are (known) functions of random variables. These include a detailed mathematical analysis of Bayes and maximum likelihood estimation as well as (nonlinear) pred

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Nonparametric Estimation for Processes,tral densities of a class of second order processes. After some necessary preliminaries, we discuss spectral properties of separable, especially harmonizable, processes of second order in Section 1. Then we consider an asymptotically unbiased estimator, and a related function, of the spectral distri

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