Statistical Inference for Discrete Time Stochastic Processes

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 marti...

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Bibliographic Details
Main Author: Rajarshi, M. B. (Author)
Corporate Author: SpringerLink (Online service)
Format: Electronic eBook
Language:English
Published: India : Springer India : Imprint: Springer, 2013.
Series:SpringerBriefs in Statistics,
Subjects:
Online Access:Full Text via HEAL-Link
Table of Contents:
  • CAN Estimators from dependent observations
  • Markov chains and their extensions
  • Non-Gaussian ARMA models
  • Estimating Functions
  • Estimation of joint densities and conditional expectation
  • Bootstrap and other resampling procedures
  • Index.