Statistical Inference

<div dir="ltr" style="text-align: left;" trbidi="on"><h2 style="text-align: left;">Paper I : Statistical Inference</h2><h3 style="text-align: left;">UNIT – I</h3><div style="text-align: left;">Point estimation. Characteristics of a good estimator: Unbiasedness, consistency, sufficiency</div>and efficiency. Method of maximum likelihood and properties of maximum likelihood<br />estimators (without proof). Method of minimum Chi-square. Method of Least squares and<br />method of moments for estimation of parameters. Problems and examples.<br /><h3 style="text-align: left;">UNIT – II</h3>Sufficient Statistics, Cramer-Rao inequality and its use in finding MVU estimators. Statistical<br />Hypothesis (simple and composite). Testing of hypothesis. Type I and Type II errors,<br />significance level, p-values, power of a test. Definitions of Most Powerful (MP), Uniformly<br />Most Powerful (UMP) and Uniformly Most Powerful Unbiased (UMPU) tests.<br /><h3 style="text-align: left;">UNIT – III</h3>Neyman-Pearson’s lemma and its applications for finding most powerful tests for simple<br />hypothesis against simple alternative. Tests based on t, F and χ2 distributions.<br /><h3 style="text-align: left;">UNIT-IV</h3>Likelihood ratio tests and their reduction to standard tests. Large sample tests, variance –<br />stabilizing transformations. Interval estimation, Pivotal quantity and its use in finding<br />confidence intervals, concept of best confidence intervals.<br /><br /></div>

Paper I : Statistical Inference UNIT – I Point estimation. Characteristics of a good estimator: Unbiasedness, consistency, sufficiency and e... Read More »