COURSE UNIT TITLE

: RESAMPLING METHODS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
STA 5007 RESAMPLING METHODS 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

ASSOCIATE PROFESSOR ENGIN YILDIZTEPE

Offered to

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

Course Objective

This course will introduce resampling methods and their applications. Those methods are computer intensive methods and now getting more and more popular and present reliable alternatives to traditional methods. The course will emphasize the practical side of resampling methods by illustrating the theoretical issues with practical applications to data analysis and statistical modeling. R statistical programming language will be used for illustrating the theoretical issues. R is an integrated software, designed especially for data manipulation, calculation, graphical display and statistical methods.

Learning Outcomes of the Course Unit

1   Using correctly the resampling methods,
2   Understanding the underlying mechanisms justifying their use,
3   Comprehending the permutation tests and the bootstrap methods,
4   Using bootstrap methods to construct confidence intervals,
5   Using of bootstrap hypothesis tests,
6   Applying bootstrap in regression models,
7   Using the resampling methods to solve problems with real data,
8   Building simulations for re-sampling applications.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Programming with R; operators, assignment, objects and data structures, data import/export, control statements and flow control
2 Functions and Functional Programming; build-in functions, writing functions
3 Statistical Data Analysis with R, probability distributions in R, simulation of random variables, Monte Carlo methods for estimation
4 Overview of Resampling; Permutation tests, The Bootstrap
5 The Jacknife Method
6 The Bootstrap Method; The bootstrap estimate of standard error, Homework 1
7 Parametric and nonparametric bootstrap
8 Bootstrap Confidence Intervals; Standard bootstrap CI, Percentile CI, Bootstrap-t CI
9 Bootstrap Confidence Intervals; BC and BCa CI, Other methods for constructing CI, Homework 2
10 Hypothesis Testing; Permutation tests, Bootstrap tests
11 Bootstrapping regression models; bootstrapping pairs
12 Bootstrapping regression models; bootstrapping residuals
13 Related methods; balanced bootstrap, the jacknife-after-bootstrap, cross validation, .632 bootstrap, Homework 3
14 Simulation studies

Recomended or Required Reading

Textbook(s):
Efron B., Tibshirani, R.J., An Introduction to the Bootstrap , Chapman&Hall, 1993.
Davison, A. C. & Hinkley, D. V., Bootstrap Methods and their Application , Cambridge University Press, 1997.
Supplementary Book(s):
Zieffler A.S., Harring, R.H., Long J.D., Comparing Groups Randomization and Bootstrap Methods Using R , Wiley, 2011.
Manly, B.F.J, Randomization, Bootstrap and Monte Carlo Methods in Biology , Chapman&Hall, 2007.
Chernick M.,R., Bootstrap Methods , Wiley, 2008.

Planned Learning Activities and Teaching Methods

Lecture format, built around the textbook readings and computer applications with numerous examples chosen to illustrate theoretical concepts.
Lecture, homework assignments, examples and PC laboratory exercises.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 ASG ASSIGNMENT
2 FIN FINAL EXAM
3 FCG FINAL COURSE GRADE ASG * 0.50 + FIN * 0.50
4 RST RESIT
5 FCGR FINAL COURSE GRADE (RESIT) ASG * 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 exam 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: engin.yildiztepe@deu.edu.tr
Tel: 0232 301 86 04

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparation for final exam 1 48 48
Preparing assignments 3 25 75
Preparations before/after weekly lectures 13 2 26
Final 1 2 2
TOTAL WORKLOAD (hours) 193

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.14554
LO.24554
LO.34554
LO.44554
LO.54554
LO.64554
LO.74554
LO.84554

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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