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An Over-View

  • Ishwar V. Basawa
  • David John Scott
Part of the Lecture Notes in Statistics book series (LNS, volume 17)

Abstract

This is an over-view of some recent developments in asymptotic inference for dependent and not necessarily identically distributed observations. Diverse models of non-ergodic type (see §2 for definitions), and results on efficiency of estimators and tests will be discussed using a unified approach. Our aim in this chapter is to present the main ideas and general asymptotic results in an informal manner. More detailed treatment of specific problems discussed here is given in subsequent chapters.

Keywords

Maximum Likelihood Estimator Limit Distribution Asymptotic Variance Asymptotic Curvature Linear Stochastic Differential Equation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag New York Inc. 1983

Authors and Affiliations

  • Ishwar V. Basawa
    • 1
  • David John Scott
    • 1
  1. 1.Department of Mathematical StatisticsLa Trobe UniversityBundooraAustralia

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