COURSE UNIT TITLE

: NONPARAMETRIC INFERENCE IN HYDROLOGY

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
CIE 5138 NONPARAMETRIC INFERENCE IN HYDROLOGY 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 GÜLAY ONUŞLUEL GÜL

Offered to

HYDRAULIC ENGINEERING AND WATER RESOURCES
HYDRAULIC ENGINEERING AND WATER RESOURCES
HYDRAULIC ENGINEERING AND WATER RESOURCES

Course Objective

This course aims to provide a comprehensive account of some of the current nonparametric techniques to be employed in hydrological inference. The course contents cover the description of the theory and the application of these methods and an explanation of how nonparametric tests can be effectively used in hydrological analyses. Their use in hydrological modeling and trend detection will be described. A review of hypothesis testing using nonparametric methods will be presented. In addition, a term assignment will be given in the form of an application of a few of the nonparametric methods.

Learning Outcomes of the Course Unit

1   Students will have understood and applied nonparametric inference basic concepts.
2   Students will be able to explain the basic ideas of Bayesian statistics, nonparametric inference and computationally intensive methods.
3   Students will be able to debate on Markov chains and their applications.
4   Students can solve hypothesis testing problems where the conditions for the traditional parametric inferential tools to be applied are not fulfilled.
5   Students will be able to manage some estimation methods as well as basic nonparametric tests.
6   Students will be able to apply nonparametric procedures in data analysis using some statistical software and evaluate the outputs.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

CIE 5113 - Hydrometric Data Evaluation

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction and Fundamentals of Nonparametric Methods
2 Order Statistics
3 Tests Based on Runs
4 Tests of Goodness of Fit
5 Rank-order Statistics
6 One-sample and Paired Sample Tecniques: the Sign Test and Signed-rank Test
7 The General Two Sample Problem
8 Linear Rank Statistics and the General Two-sample Problem
9 Linear Rank Tests for the Scale Problem
10 Linear Rank Tests for the Location Problem
11 Tests of the Equality of k Independent Samples
12 Measures of Association for Bivariate Samples
13 Measures of Association in Multiple Classifications
14 Measures of Association in Multiple Classifications (cont.)

Recomended or Required Reading

Textbook(s): Gibbons, J.D., Chakraborti, S., Nonparametric Statistical Inference, Marcel Dekker, Inc., 1992 (Third Edition).
Hipel, K.W., McLeod, A.I., Time Series Modelling of Water Resources and Environmental Systems. Elsevier, 1994.

Planned Learning Activities and Teaching Methods

Formal presentation of subjects and recitation, homework assignment.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.25 + ASG *0.25 +FIN *0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.25 + ASG *0.25 +RST *0.50


Further Notes About Assessment Methods

None

Assessment Criteria

LO 1-2-3-4-5-6: evaluated through tailor-made questions posed during mid-term and final exams.
LO 1-2-3-4-5-6: evaluated through reports prepared upon homework assignment.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

gulay.onusluel@deu.edu.tr

Office Hours

will be announced at the beginning of semester.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparation for final exam 1 20 20
Preparation for midterm exam 1 20 20
Preparations before/after weekly lectures 13 4 52
Reading 4 5 20
Preparing assignments 1 30 30
Midterm 1 4 4
Final 1 4 4
TOTAL WORKLOAD (hours) 192

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11
LO.133
LO.244
LO.344
LO.45
LO.55
LO.655

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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