COURSE UNIT TITLE

: ADVANCED STATISTICAL INFERENCE - II

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
STA 6002 ADVANCED STATISTICAL INFERENCE - II ELECTIVE 3 0 0 9

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR BURCU HÜDAVERDI

Offered to

Statistics
Statistics
STATISTICS

Course Objective

To have knowledge about advanced theory of statistical inference.

Learning Outcomes of the Course Unit

1   Understanding the Large Sample theory
2   Applying the convergence rules
3   Using the functionals to obtain the statistics
4   Obtaining the parameter estimation
5   Evaluating the estimators
6   Learning how to construct and how to evaluate statistical tests

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Large Sample Theory
2 Convergence of Sequences of random variables, convergence in probability, convergence in distribution, weak convergence, almost sure convergence
3 Large sample rules and expansions
4 Types of Functionals
5 Types of Statistics by using Functionals
6 Parameter Estimation and Method of Substitution, Method of Moment
7 Minimal Distance and Maximum Likelihood Estimation
8 Evaluating Estimators, Sufficient, Completeness
9 Evaluating Estimators, Consistency, RCLB
10 Hypothesis testing, Test of Simple Hypothesis, Methods of Finding Tests, LRT, SQLRT
11 Methods of Evaluating Test, Error probabilities, Power functions, construction most powerful test
12 Consistency of LRT, asymptotic power of LRT, Problems
13 Asymptotic distributions of some tests, Problems
14 General Review

Recomended or Required Reading

Textbook:
George Casella and Roger L. Berger, Statistical Inference, 2nd edition,2002, Duxbury
Anirban Das Gupta, Asymptotic Theory of statistics and Probability, 2008 Springer
E.L. Lehman and George Casella, Theory of Point Estimation, 2nd edition, 1998, Springer

Planned Learning Activities and Teaching Methods

Lecture and Homeworks

Assessment Methods

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


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

Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of homework assignment, midterm exam and final exam.

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. 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: burcu.hudaverdi @deu.edu.tr
Tel: 0232 301 85 97

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 16 4 64
Preparation for final exam 1 45 45
Preparing assignments 3 25 75
Final 1 2 2
TOTAL WORKLOAD (hours) 228

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

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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