Subject: Subject Sylbus: Semiparametric Models - 097470 (Previous)

Semiparametric Models - 097470
Credit
Points
2.0
 
Given In
Semester
a
 
  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 Missing-Data 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
RoomBuildingHourdayLecturerExercise
Lecture
no.Registering
Group
214рдем10:30-12:30WednesdayAssociate Professor Goldberg YairLecture1111


Textbooks
PublishedPublisherAuthorsBook
2006springeranastasios tsiatissemiparametric theory and missing data

Created in 21/04/2021 Time 23:56:35