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Statistical Inference (1)

Course Description: Sampling Distributions – Central Limit Theorem - Point Estimation - Properties of Estimators (Unbiasedness, Mean Squared Error, Consistency, Sufficiency, Minimum Variance Sufficiency) - Exponential Family - Minimum Variance Unbiased Estimator (MVUE), Cramér-Rao Inequality, Fisher Information - Rao-Blackwell Theorem, Sufficiency and Completeness - Lehmann-Scheffé Theorem - Estimation Methods: Method of Moments, Maximum Likelihood Estimators and their Asymptotic Properties - Bayesian Approach: Use of Prior Distributions, Bayes Estimators using Squared Loss Functions - Invariance Methods: Location and Scale Invariant Estimators – Interval Estimation (Single Population Case): Confidence Interval Estimators - Pivotal Methods - Credible Intervals.
Credit hours: 3
Prerequisites: STAT 215
Objectives of the course :

This course aims to introduce some important statistical methods and concepts regarding statistical hypotheses and parameter estimation, as well as confidence intervals associated with these parameters. In addition, it will cover some important theories and properties related to probability theory.

Course outputs :

Acquiring knowledge by learning some basic methods and concepts related to mathematical statistics and its applications. Improving the ability to use and apply statistical methods in areas that require it. Gaining cognitive skills through thinking and solving some life problems. Enhancing the ability to pursue advanced studies when needed in the field of statistics in general, mathematical statistics, as well as probability theory and its applications in particular.

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  • Last updated: 2026-05-13

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