POST - A - PENALIZED ESTIMATORS IN HIGH - DIMENSIONAL LINEAR REGRESSION MODELS Alexandre Belloni

نویسندگان

  • ALEXANDRE BELLONI
  • VICTOR CHERNOZHUKOV
چکیده

In this paper we study post-penalized estimators which apply ordinary, unpenal-ized linear regression to the model selected by first-step penalized estimators, typically LASSO.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Penalized Estimators in Cox Regression Model

The proportional hazard Cox regression models play a key role in analyzing censored survival data. We use penalized methods in high dimensional scenarios to achieve more efficient models. This article reviews the penalized Cox regression for some frequently used penalty functions. Analysis of medical data namely ”mgus2” confirms the penalized Cox regression performs better than the cox regressi...

متن کامل

Robust Estimation in Linear Regression with Molticollinearity and Sparse Models

‎One of the factors affecting the statistical analysis of the data is the presence of outliers‎. ‎The methods which are not affected by the outliers are called robust methods‎. ‎Robust regression methods are robust estimation methods of regression model parameters in the presence of outliers‎. ‎Besides outliers‎, ‎the linear dependency of regressor variables‎, ‎which is called multicollinearity...

متن کامل

Least Squares After Model Selection in High-dimensional Sparse Models

In this paper we study post-model selection estimators which apply ordinary least squares (ols) to the model selected by first-step penalized estimators, typically lasso. It is well known that lasso can estimate the nonparametric regression function at nearly the oracle rate, and is thus hard to improve upon. We show that ols post lasso estimator performs at least as well as lasso in terms of t...

متن کامل

Robust inference in high- dimensional approximately sparse quantile regression models

This work proposes new inference methods for the estimation of a regression coefficientof interest in quantile regression models. We consider high-dimensional models where the number ofregressors potentially exceeds the sample size but a subset of them suffice to construct a reasonableapproximation of the unknown quantile regression function in the model. The proposed methods are<lb...

متن کامل

Estimation and Selection via Absolute Penalized Convex Minimization And Its Multistage Adaptive Applications

The ℓ1-penalized method, or the Lasso, has emerged as an important tool for the analysis of large data sets. Many important results have been obtained for the Lasso in linear regression which have led to a deeper understanding of high-dimensional statistical problems. In this article, we consider a class of weighted ℓ1-penalized estimators for convex loss functions of a general form, including ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2011