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Course Unit Title Course Unit Code Type of Course Unit Level of Course Unit Year of Study Semester ECTS Credits
Robust Estimation Methods In Geodesy JJM514 Elective Master's degree 1 Spring 9

Name of Lecturer(s)

Prof. Dr. Haluk KONAK

Learning Outcomes of the Course Unit

1) Student establishes robust functions
2) Students interpret the concept of breaking point
3) Student adapts the robust estimation to Data Snooping test method
4) Student adapts the robust estimation for Photogrammetric data.
5) Student improves the robust estimatör for 2D and 3D coordinate transformations
6) Student improves the robust estimators for special geodetic problems
7) Student compares actuel practices on robust estimation

Program Competencies-Learning Outcomes Relation

  Program Competencies
1 2 3 4 5 6 7
Learning Outcomes
1 No relation No relation No relation No relation No relation No relation No relation
2 No relation No relation No relation No relation No relation No relation No relation
3 No relation High No relation No relation No relation No relation No relation
4 No relation No relation No relation No relation No relation No relation No relation
5 No relation No relation No relation No relation No relation No relation No relation
6 No relation High No relation No relation No relation No relation No relation
7 No relation No relation No relation No relation No relation No relation No relation

Mode of Delivery

Face to Face

Prerequisites and Co-Requisites

None

Recommended Optional Programme Components

JJM513 Estimation for Geodetic Parameters and Linear Hypothesis Testing, JJM509 Advanced Adjustment

Course Contents

Robust statistics. Basic concepts and properties. Robust estimation of linear models. Breaking Point Concept. Robust M-Estimation Methods and Confidence Intervals. Equivalent redundancy design. The concept of outliers.Test of robustness for outliers. Elimination outliers and geodetic applications. Localization of outliers in geodetic networks. Localization of outliers in the data for photogrammetric purposes. 2D and 3D coordinate transformations. The localization of outlier coordinate pairs. Robust Deformation Analysis.

Weekly Schedule

1) The basic concepts in the Robust statistic and its features
2) Robust Estimation functions.
3) Comparing according to Least Squares method for Robust estimation. Concept of Breaking Point
4) Robust M-estimators and their properties: Least Absolute Total, Huber, Modified Huber, Beaton Tukey and etc.
5) Confidence Intervals estimators and Equal Redundancy designs
6) The concept of outliers, robustness of outliers tests
7) Localization of outliers in geodetic networks. Results of Robust estimation.
8) Midterm examination/Assessment
9) Localization of outliers in geodetic networks. The Results of equal redunndancy design.
10) The localization of Outliers for photogrammetric data.
11) Two-and three-dimensional coordinate transformations and the localization of outlier coordinate pairs.
12) Applications of robust estimation for High-grade general coordinate transformations.
13) Robust Deformation Analysis.
14) Robust parameter estimation in Geometric improving operations of Satellite photographs
15) Final examination

Recommended or Required Reading

1- - Huber, P.J., (1981), Robust Statistic, John Willey and Sons, New York, Chichester.
2- - Seber, G.A.F. (1984) Multivariate Observations, John Willey and Sons, New York.
3- - Ata, M., (1999) Statik Deformasyon Analizinde Robust Kestirim Yöntemlerinin Kullanılması Üzerine Bir Araştırma, YTÜ-FBE, Doktora Tezi, İstanbul.
4- - Aksoy, A. (1987) Jeodezik Değerlerin Matematik-İstatistik Testlerle İrdelenmesi, Türkiye 1. Harita Bilimsel ve Teknik Kurultayı, s.559, Ankara.

Planned Learning Activities and Teaching Methods

1) Lecture
2) Question-Answer
3) Discussion
4) Group Study
5) Self Study
6) Problem Solving


Assessment Methods and Criteria

Contribution of Presentation/Seminar to Course Grade

30%

Contribution of Final Examination to Course Grade

70%

Total

100%

Language of Instruction

Turkish

Work Placement(s)

Not Required