Lecture Notes

The lectures notes are available as single files mapped to the lecture sessions below or as a complete document (PDF - 1.45MB).

LEC #	TOPICS	LECTURE NOTES
1	Estimation Theory Introduction	(PDF)
2	Some Probability Distributions	(PDF)
3	Method of Moments	(PDF)
4	Maximum Likelihood Estimators	(PDF)
5	Consistency of MLE Asymptotic Normality of MLE, Fisher Information	(PDF)
6	Rao-Crámer Inequality	(PDF)
7	Efficient Estimators	(PDF)
8	Gamma Distribution Beta Distribution	(PDF)
9	Prior and Posterior Distributions	(PDF)
10	Bayes Estimators Conjugate Prior Distributions	(PDF)
11	Sufficient Statistic	(PDF)
12	Jointly Sufficient Statistics Improving Estimators Using Sufficient Statistics, Rao-Blackwell Theorem	(PDF)
13	Minimal Jointly Sufficient Statistics χ2 Distribution	(PDF)
14	Estimates of Parameters of Normal Distribution	(PDF)
15	Orthogonal Transformation of Standard Normal Sample	(PDF)
16	Fisher and Student Distributions	(PDF)
17	Confidence Intervals for Parameters of Normal Distribution	(PDF)
18	Testing Hypotheses Testing Simple Hypotheses Bayes Decision Rules	(PDF)
19	Most Powerful Test for Two Simple Hypotheses	(PDF)
20	Randomized Most Powerful Test Composite Hypotheses. Uniformly Most Powerful Test	(PDF)
21	Monotone Likelihood Ratio One Sided Hypotheses	(PDF)
22	One Sided Hypotheses (cont.)	(PDF)
23	Pearson’s Theorem	(PDF)
24	Goodness-of-Fit Test Goodness-of-Fit Test for Continuous Distribution	(PDF)
25	Goodness-of-Fit Test for Composite Hypotheses	(PDF)
26	Test of Independence	(PDF)
27	Test of Homogeneity	(PDF)
28	Kolmogorov-Smirnov Test	(PDF)
29	Simple Linear Regression Method of Least Squares Simple Linear Regression	(PDF)
30	Joint Distribution of the Estimates	(PDF)
31	Statistical Inference in Simple Linear Regression	(PDF)
32	Classification Problem	(PDF)