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A First Course in Parametric Inference, by B. K. Kale
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Starting with the basic concepts of sufficient statistics, the classical approach based on minimum variance unbiased estimation is presented in detail. A separate chapter is added on simulataneous estimation of several parametric functions where the optimality criteria are defined using variance covariance matrix of the vector of estimators. Large sample theory of estimation based on consistent asymptotic normal estimators is then presented and various methods which lead to such estimators, eg. method of moments and percentiles are presented. There is a separate chapter on maximum likelihood method which is used extensively in many other fields. The book concludes with standard theory of tests of hypotheses and confidence intervals with an emphasis on likelihood estimators. There are many solved examples illustrating the theory and which are applicable in a variety of practical situations. A special feature of the text is the wealth of historical information which provides a perspective of how the ideas developed over the past 200 years starting from Boscovitch-Gauss -Legendre Laplace to K. Pearson, Fisher - Neyman, E. Pearson and C.R. Rao.
- Published on: 1998-02
- Original language: English
- Binding: Paperback
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