stat 523

Course Outline
 
STAT 523
SELECTED TOPICS IN STATISTICS
 
Instructor
 
    Prof. Khalaf S. Sultan
    Department of Statistics and Operations Research
    College of Science     
     P.O.Box 2455, Riyadh 11451
     King Saud University
 
Phone:  4676263 (Office)
E-mail:   ksultan@ksu.edu.sa
 
Time  :  sunday 8:00 – 11:00 am                
 
Books
During the course, we use some parts from the following books:
 

  1. Gujarati, D. (2015). Econometrics by Example 2nd Edition, Red Globe Press, London.

 

  1. Garrett Fitzmaurice, G., Laird, N. and  Ware, J. (2011). Applied Longitudinal Analysis, 2nd Edition John Wiley & Sons, New York.

 

  1. Balakrishnan, N. and Cohen, A.C. (1991).  Order Statistics and Inference: Estimation Methods, Academic Press, New York.

 

  1. Arnold, A., Balakrishnan, N. And Nagaraja, H.N. (1992). A First Course in Order Statistics, John Wiley & Sons, New York.

 

  1. Rubinstein, R. Y. (1981). Simulation and the Monte Carlo Methods, John Wiley & Sons, New York.

 

  1. D’Agostino, R.B. and Stephens, M.A. (1986). Goodness-of-Fit Techniques, Marcel Dekker, New York.

 
 
Reference Books
 

  1. James J. Higgins (2004). Introduction to Modern Nonparametric Statistics, Duxbury (Thomson).
  2. Efron B. and Tibshiriani, R. J. (1980). An Introduction to the Bootstrap,  Chapman and Hall, New York.

 

  1. Johnson, N.L., Kotz, S. and Balakrishnan, N. (1995). Continuous Univariate Distributions, Vol. 2, Second edition, John Wiley, New York.

 

  1. Johnson, N.L., Kotz, S. and Balakrishnan, N. (1994). Continuous Univariate Distributions, Vol. 1, Second edition, John Wiley, New York.

 
Course Description and Objectives
 
In this course, we address several selected topics such as:

  • Panel (Longitudinal) data analysis
  • Some generalized linear models; including Logistic and ordinal Regression; Multinomial Logistic Regression Models; ordinal Multinomial Logistic Regression Models and Probit Regression Models.
  • Some Monte Carlo techniques
  • Some parametric and nonparametric goodness-of-fit tests
  • Bootstrap methods.

Assignments, project and Exams:

Assignments & Projects    30 marks
Midterm Exam   30 marks
Final Exam   40 marks

 
Computing:
In this course, we will use R language.
Attendance:
Students missing more than 25% of the total class hours won't be allowed to write the final exam. 

Course Materials