          Description of Individual Course Units
 Course Unit Code Course Unit Title Type of Course Unit Year of Study Semester Number of ECTS Credits İST410 ROBUST STATISTICS Elective 4 8 5
Level of Course Unit
First Cycle
Objectives of the Course
To make students learn the robust statistics concept and to give the students the opportunity to investigate the robust statistical methods in the literature and to compute and compare them by the help of the Matlab software.
Name of Lecturer(s)
Assoc. Prof. Dr. Hakan Savaş SAZAK
Learning Outcomes
 1 To Know the Basic Statistical Concepts 2 To Know the Theoretical Statistical Concepts 3 To Know the Statistical Estimation Methods 4 To be able to derive estimators with various methods under different distributions 5 To Know the Robust Statistics Concepts 6 To Know Various Robust Statistics Methods 7 To Know the Properties of Robust Estimators and Statistics 8 To be able to implement the simulation of various statistical methods on computer 9 To be able to compare the statistical methods under interest with respect to their properties and simulation results and make comments on them 10 To be able to conduct the applications of Robust Statistical Methods
Mode of Delivery
Face to Face
Prerequisites and co-requisities
Recommended Optional Programme Components
Basic Usage Knowledge on the Matlab Software
Course Contents
• Basic Concepts: Statistic, estimator, unbiasedness etc. • Frequently used distributions and their properties • Properties of Estimators: Efficiency, unbiasedness, consistency etc. • The likelihood concept and the Fisher information matrix • Basic Estimation Techniques: Least Squares Estimators, Moment Estimators, Maximum Likelihood Estimators • Properties of Robust Statistical Methods • The concept of Order Statistics and their properties • Robust Estimation Methods: Weighted Least Squares, BLUE, L Estimators, Median, M-Estimators, Modified Maximum Likelihood Estimators • Measures of Robustness: Breakdown Bound and Influence Function • The simulation and comparison of Robust Estimators • Robust Regression
Weekly Detailed Course Contents
 Week Theoretical Practice Laboratory 0 Basic Concepts: Statistic, estimator, unbiasedness etc. 1 Frequently used distributions and their properties 2 Properties of Estimators: Efficiency, unbiasedness, consistency etc. 3 The likelihood concept and the Fisher information matrix 4 Basic Estimation Techniques: Least Squares Estimators, Moment Estimators, Maximum Likelihood Estimators 5 Properties of Robust Statistical Methods 6 The concept of Order Statistics and their properties 7 Midterm Exam 8 Robust Estimation Methods: Weighted Least Squares, BLUE, L Estimators, Median, M-Estimators, Modified Maximum Likelihood Estimators 9 Measures of Robustness: Breakdown Bound and Influence Function 10 Dixon’s outlier model, mixed model, contamination model 11 Obtaining various properties of estimators under different models 12 Introduction of Matlab software and creating specialized programs by using Matlab 13 The simulation and comparison of Robust estimators under different distributions and models 14 Robust Regression 15 Final Exam
Hoaglin, D.C., Moesteller, F. & Tukey, J.W., “Understanding Robust and Exploratory Data Analysis”, 1983, John Wiley & Sons Inc. Martinez, W.L. & Martinez, A.R., “Computational Statistics Handbook with MATLAB”, 2002, Chapman & Hall/CRC
Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"
Assessment Methods and Criteria
 Term (or Year) Learning Activities Quantity Weight Midterm Examination 1 100 SUM 100 End Of Term (or Year) Learning Activities Quantity Weight Final Sınavı 1 100 SUM 100 Term (or Year) Learning Activities 40 End Of Term (or Year) Learning Activities 60 SUM 100
Language of Instruction
English
Work Placement(s) 