The aim of this course is to provide a strong mathematical and conceptual foundation in the methods of Robust Statistics with an emphasis on practical aspects of the interpretation and communication of statistically based conclusions in research. Content includes: review of the key concepts of basic statistics, estimation, and probability

To produce the students, that has applicable knowledge about robust methods, which they apply in different fields.

1. To impart applied knowledge about robust measures and its tools.
2. To impart skills on the data collection, description measures of data interpretation of results, and decision making.

 After successfully completing the course, students will be able to:

Understand the philosophy and basic concepts of robustness. Apply appropriate robust methods for comparing population parameters. Demonstrate the ability to derive M-estimator of location .E-estimator, R-estimator and W-estimator, Redesending M- estimator’s. Demonstrate understanding of the theory of influential observations and  outliers in Regression analysis

CONTENTS

Introduction to Robustness. Objective function. M-estimator of location .E-estimator, R-estimator and W-estimator, Redesending M- estimator’s. The Breakdown point of Robust estimator Influence function. M-estimator for scale. Outliers and influential observations. Outliers in Regression analysis

1. Wilcox R. (2012). Introduction to Robust Estimation and Hypothesis Testing, 1st Ed., Elsevier, USA
2. Maronna R., Martin R,. and Yohai, V. (2006). Robust Statistics: Theory and Methods. John Wiley and Sons, New York.
3. Rousseau, P.J. and Leroy, A.M. (1987). Robust Regression and outlier detection, John Wiley. New York.
4. Hamper, T.R. Brochette, E.M. Rousseau, P.J. and Satchel, W. A. (1986). Robust Statistics: The approach Based on Influence functions, John Wiley New York.

Distribution of Marks:
Mid Exam:           30

Final exam:         50
Sessional (Assignment,Presentation,Participation,Attendance,Quizes)    20
Scheduled on:      

MSc III(R):               Monday (11:00-12:30)   Tuseday(12:00-1:30)