Topics in Regression  096415





Lecture 
Exercise 
Laboratory 
Project or Seminar 
House Work 
Weekly Hours 
3 


1 
5 

Determination of the grade according to progress during the semester and the submission of the final thesis
Prerequisites:
    Introduction to Statistics 
094423
 
The Course Contains Two Parts. the First Part Deals with a MisSpecified Linear Model and the Second Is About HighDimensional Regression. Topics: Best Linear Predictor, Least Squares, GaussMarkov Theorem, Asymptotic Distribution, Model Selection in Low and High Dimensions, Ridge Estimator, Existence Theorem, Lasso, Error Bound for Lasso. at the End of the Course the Student Should
1. Be Familiar with the Basic Theory of Least Squares Estimates Both in Linear and NonLinear Scenarios.
2. Use Standard Software in Order to Compute Least Squares Estimates, Confidence Intervals and Hypothesis Testing.
3. Program Different Statistical Methods and Compare Them Using Simulations.
4. Be Familiar with the Basic Theory of Model Selection in Regression as Well as Ridge and Lasso Estimates.
5. Use Standard Software in Order to Compute Ridge and Lasso Estimates.
Timetable to semester 01/2020
2020/2021 Winter Semester
semester
Previous
Room  Building  Hour  day  Lecturer  Exercise Lecture  no.  Registering Group 

215  נהול  09:3011:30  Tuesday  Doctor Azriel David  Lecture  10  11 
214  נהול  14:3015:30  Tuesday  Doctor Azriel David  Project  11 
TextbooksPublished  Publisher  Authors  Book 

2017  ספר אינטרנטי  b. hansen,  econometrics 
2014  crc  c. giraud  introduction to highdimensional statistics 
2015  crc  hastie, tibshirani and wainwrigh  statistical learning with sparsity: the lasso and snoitazilareneg 
Created in 22/04/2021 Time 13:49:51