Titlebook: Statistical Inference for Discrete Time Stochastic Processes; M. B. Rajarshi Book 2013 The Author(s) 2013 Bootstrap.Estimating Functions.N

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CAN Estimators from Dependent Observations,or mixing sequences, as well as central limit theorems for sums of dependent random variables have been discussed. We then discuss weak convergence of empirical processes obtained from stationary observations. These results have been applied to generate consistent and asymptotically normal estimator

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Markov Chains and Their Extensions,odels, such as Hidden Markov chain, Markov chains based on polytomous regression, and Raftery’s Mixture Transition Density model. These models have less number of parameters as compared to a higher order finite Markov chain. Lastly, we discuss methods of estimation in grouped data from finite Markov

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Non-Gaussian ARMA Models,ribution of these models is non-Gaussian. Such models can be broadly described as extensions of Gaussian ARMA models, which have been very widely discussed in the time series literature. These non-Gaussian AR models share two important properties with a linear AR(1) model: (i) the conditional expect

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Estimating Functions,e given the past observations have been specified. These constitute semi-parametric methods for stochastic models. We begin with Conditional Least Squares estimation. Then, we discuss estimating functions in some details. The basic set of estimating functions can be conditionally uncorrelated or cor

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M. B. Rajarshins, especially but not only in developing countries, intensify. Therefore, there is an increasing need to search for integrated solutions to make development more sustainable. The United Nations has acknowledged the problem and approved the “2030 Agenda for Sustainable Development”. On 1st January 2