Statistical Inference M. Rajagopal; P. Dhanavanthan
Material type:![Text](/opac-tmpl/lib/famfamfam/BK.png)
- 9788120346352
- 519.5 RAJ
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CUTN Central Library Sciences | Non-fiction | 519.5 RAJ (Browse shelf(Opens below)) | Available | 34261 | |
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CUTN Central Library Sciences | Non-fiction | 519.5 RAJ (Browse shelf(Opens below)) | Available | 34262 | |
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CUTN Central Library Sciences | Reference | 519.5 RAJ (Browse shelf(Opens below)) | Not For Loan | 34265 |
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518.1 BAS Design methods and analysis of algorithms | 519.2 ROS A first course in probability/ | 519.5 DOB An Introduction to Liner Models/ | 519.5 RAJ Statistical Inference | 519.502 LEV Statistics for management / | 519.502854 KEN Statistical Computing / | 519.535 HAI Multivariate data analysis / |
Preliminaries
Point Estimation—Unbiasedness and Consistency
Sufficiency and Completeness
Minimum Variance Unbiased Estimators
Methods of Estimation
Interval Estimation
Testing Statistical Hypotheses I
Testing Statistical Hypotheses II
Likelihood Ratio Method of Test Construction
Invariance and Equivariance
Bayesian Approach
Nonparametric Methods
Sequential Procedures
Intended as a text for the postgraduate students of statistics, this well-written book gives a complete coverage of Estimation theory and Hypothesis testing, in an easy-to-understand style. It is the outcome of the authors’ teaching experience over the years. The text discusses absolutely continuous distributions and random sample which are the basic concepts on which Statistical Inference is built up, with examples that give a clear idea as to what a random sample is and how to draw one such sample from a distribution in real-life situations. It also discusses maximum-likelihood method of estimation, Neyman’s shortest confidence interval, classical and Bayesian approach. The difference between statistical inference and statistical decision theory is explained with plenty of illustrations that help students obtain the necessary results from the theory of probability and distributions, used in inference.
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