Semiparametric Models  097470





Lecture 
Exercise 
Laboratory 
Project or Seminar 
House Work 
Weekly Hours 
2 



2 

Determination of the grade according to progress during the semester and a final examination.
Prerequisites:
    Statistical Theory for Data Analysis 
097414
 

or

(
  Introduction to Statistics 
094423
 
  
and
 Algebraic Methods for Data Science 
095295
 ) 

or

(
  Introduction to Statistics 
094423
 
  
and
 Numerical Algorithms 
234125
 ) 

or

(
  Introduction to Statistics 
094423
 
  
and
 Modern Algebra H 
104134
 ) 

or

(
  Introduction to Statistics 
094423
 
  
and
 Algebra B 
104168
 ) 
The Course Presents the Theory of Semiparametric Models and Show How to Apply This Theory to Solve Statistical Problems. the First Part of the Course Deals with the Definition of Semiparametric Models and the Theoretical Development for Estimators of the Parameters in These Models. the Second Part of the Course Will Focus on the Use of Semiparmatric Tools for MissingData Problems. at the End of the Course Students Will Know:
1. How to Define the Statistical Model in Semiparametric Problems,
2. How to Define Hilbert Spaces of Functions,
3. How to Develop Semiparametic Estimators, and to Analyze Their Theoretical Properties.
Timetable to semester 01/2020
2020/2021 Winter Semester
semester
Previous
Room  Building  Hour  day  Lecturer  Exercise Lecture  no.  Registering Group 

214  ِلمٌ  10:3012:30  Wednesday  Associate Professor Goldberg Yair  Lecture  11  11 
TextbooksPublished  Publisher  Authors  Book 

2006  springer  anastasios tsiatis  semiparametric theory and missing data 
Created in 21/04/2021 Time 23:56:35