COURSE UNIT TITLE

: INTRODUCTION TO ROBUST ESTIMATION AND INFERENCE

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
IST 4117 INTRODUCTION TO ROBUST ESTIMATION AND INFERENCE ELECTIVE 3 0 0 5

Offered By

Statistics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR ABDULLAH FIRAT ÖZDEMIR

Offered to

Statistics
Statistics(Evening)

Course Objective

Researchers need robust methods when the underlying assumptions of the method they use are not properly satisfied. In this course the concept of robustness in statistics and robust statistical inferential methods will be teached in an undergraduate level.

Learning Outcomes of the Course Unit

1   Use R programming language in a basic level
2   Examine Normal theory assumptions by Monte-Carlo simulation
3   Describe fundamental concepts in robust statistics
4   Calculate robust measures of location and scale
5   Use bootstrap sampling approach
6   Use robust methods for one sample inference
7   Use robust methods for two samples inference
8   Use robust methods for more than two samples inference

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Fundamental concepts in R, loops, built in functions
2 Monte-Carlo simulation with R
3 Fundamental concepts in robust statistics
4 Robust estimators of location
5 Robust estimators of scale
6 Robust outlier detection methods
7 Comparing robust estimators of location and scale by simulation
8 Midterm exam
9 Bootstrap sampling
10 Robust confidence intervals and one sample tests
11 Comparing robust and conventional one sample tests by simulation
12 Robust two samples tests/ Comparing robust and conventional two sample tests by simulation
13 Robust tests for more than two samples
14 Comparing robust and conventional more than two sample tests by simulation

Recomended or Required Reading

Textbook(s): Applying Contemporary Statistical Techniques Rand R. Wilcox, Academic Press 2003

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.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 FIN FINAL EXAM
3 FCG FINAL COURSE GRADE MTE * 0.50 + FIN * 0.50
4 RST RESIT
5 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.50 + RST * 0.50


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of ...

Language of Instruction

Turkish

Course Policies and Rules

Student responsibilities:
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. You can find the undergraduate policy at http://web.deu.edu.tr/fen

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 13 3 39
Preparations before/after weekly lectures 12 1 12
Preparation for midterm exam 1 24 24
Preparation for final exam 1 29 29
Preparing assignments 2 4 8
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 116

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14
LO.15544
LO.25544
LO.35544
LO.45544
LO.55544
LO.65544
LO.75544
LO.85544