Topics in Regression - 096415
Determination of the grade according to progress during the semester and the submission of the final thesis
||Introduction to Statistics||
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.
|2017||ספר אינטרנטי||b. hansen,||econometrics|
|2014||crc||c. giraud||introduction to high-dimensional statistics|
|2015||crc||hastie, tibshirani and wainwrigh||statistical learning with sparsity: the lasso and snoitazilareneg|
Created in 06/03/2021 Time 01:43:45