Least squares after model selection in high-dimensional sparse models
نویسندگان
چکیده
منابع مشابه
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...
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ژورنال
عنوان ژورنال: Bernoulli
سال: 2013
ISSN: 1350-7265
DOI: 10.3150/11-bej410