COURSE UNIT TITLE

: ROBUST ESTIMATION METHODS AND HYPOTHESIS TESTING

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
STA 5026 ROBUST ESTIMATION METHODS AND HYPOTHESIS TESTING ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR ABDULLAH FIRAT ÖZDEMIR

Offered to

Statistics (English)
STATISTICS (ENGLISH)
Statistics (English)

Course Objective

In this course, the concept of robustness in statistics and robust statistical inferential methods will be teached in a graduate level.

Learning Outcomes of the Course Unit

1   Practical Reasons for Using Robust Methods
2   A Foundation for Robust Methods
3   Some Measures of Locations and Their Influence Function
4   Estimating Measures of Location and Scale
5   Some Comparisons of the Location Estimators
6   Confidence Intervals in the One-Sample Case
7   Comparing Two Groups
8   One-Way and Higher Designs

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 1-)Practical Reasons for Using Robust Methods
2 2-)Problems with Assuming Normality and homogeneity of variances
3 3-)A Foundation for Robust Methods
4 4-)Basic Tools for Judging Robustness
5 5-)Some Measures of Locations and Their Influence Function
6 6-)Estimating Measures of Location and Scale
7 7-)The Sample Trimmed Mean and M-Estimator of Location
8 8-)Outlier detecting methods
9 9-)Some Comparisons of the Location Estimators
10 10-)Confidence Intervals in the One-Sample Case
11 11-)The Inferences about the Trimmed Mean and M-estimators-Homework2
12 12-)Comparing Two Groups, The Yuen-Welch Test
13 13-)One-Way and Higher Designs, Trimmed Means in a One-Way Design-PRESENTATION
14 14-)Comparing M-Measures of Location-A general review

Recomended or Required Reading

Textbook(s): Rand R.Wilcox Introduction to Robust Estimation and HypothesisTesting , 3rd edition by, Academic Press, 2012
Textbook(s): Robert G. Staudte, Simon J. Sheater Robust Estimation &Testing, by, Wiley Pub. 1990

Planned Learning Activities and Teaching Methods

Lecture format, built around the textbook readings and computer applications with numerous examples chosen to illustrate theoretical concepts. Lots of drill with emphasis on practice. Questions are encouraged and discussion of material stressed.Lecture and problem solving.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 PRS PRESENTATION
4 FIN FINAL EXAM
5 FCG FINAL COURSE GRADE MTE* 0.30 + ASG * 0.20 + PRS * 0.10 + FIN * 0.40
6 RST RESIT
7 FCGR FINAL COURSE GRADE (RESIT) MTE* 0.30 + ASG * 0.20 + PRS * 0.10 + RST * 0.40


Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of exams and homeworks

Language of Instruction

English

Course Policies and Rules

Attendance to at least 70% for the lectures is an essential requirement of this course and is the responsibility of the student. It is necessary that attendance to the lecture and homework delivery must be on time. Any unethical behavior that occurs either in presentations or in exams will be dealt with as outlined in school policy.

Contact Details for the Lecturer(s)

DEU Fen Fakültesi Istatistik Bölümü
e-mail: firat.ozdemir@deu.edu.tr
Tel: 0232 301 85 52

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 14 1 14
Preparation for final exam 1 36 36
Preparation for midterm exam 1 24 24
Preparing assignments 2 20 40
Preparing presentations 1 28 28
Final 1 2 2
Midterm 1 2 2
Quiz etc. 1 2 2
TOTAL WORKLOAD (hours) 190

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.1555
LO.2555
LO.3555
LO.4555
LO.5555
LO.6555
LO.7555
LO.8555