Thresholding least-squares inference in high-dimensional regression models
نویسندگان
چکیده
منابع مشابه
Sign-constrained least squares estimation for high-dimensional regression
Many regularization schemes for high-dimensional regression have been put forward. Most require the choice of a tuning parameter, using model selection criteria or cross-validation. We show that a simple sign-constrained least squares estimation is a very simple and effective regularization technique for a certain class of high-dimensional regression problems. The sign constraint has to be deri...
متن کاملLeast 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 کاملPEDOMODELS FITTING WITH FUZZY LEAST SQUARES REGRESSION
Pedomodels have become a popular topic in soil science and environmentalresearch. They are predictive functions of certain soil properties based on other easily orcheaply measured properties. The common method for fitting pedomodels is to use classicalregression analysis, based on the assumptions of data crispness and deterministic relationsamong variables. In modeling natural systems such as s...
متن کاملRegression: Least Squares and Statistical Inference. . .in a Nutshell
Least squares may be viewed as a best-fit procedure or as a statistical estimation procedure. There is much overlap between the two perspectives but the emphasis can be different: approximation in the best-fit context, and inference in the statistical estimation context. In this nutshell we summarize the intepretation of least-squares estimators from a statistical perspective. Note that we do n...
متن کاملRobust 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 کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Electronic Journal of Statistics
سال: 2016
ISSN: 1935-7524
DOI: 10.1214/16-ejs1160