Subject: Subject Sylbus: Topics in Regression - 096415 (Previous)

Topics in Regression - 096415
Credit
Points
3.0
 
Given In
Semester
a
 
  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 Mis-Specified Linear Model and the Second Is About High-Dimensional Regression. Topics: Best Linear Predictor, Least Squares, Gauss-Markov 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 Non-Linear 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
RoomBuildingHourdayLecturerExercise
Lecture
no.Registering
Group
215נהול09:30-11:30TuesdayDoctor Azriel DavidLecture1011
214נהול14:30-15:30TuesdayDoctor Azriel DavidProject11


Textbooks
PublishedPublisherAuthorsBook
2017ספר אינטרנטיb. hansen,econometrics
2014crcc. giraudintroduction to high-dimensional statistics
2015crchastie, tibshirani and wainwrighstatistical learning with sparsity: the lasso and snoitazilareneg

Created in 22/04/2021 Time 13:49:51