Rebar: Reinforcing a Matching Estimator With Predictions From High-Dimensional Covariates
نویسندگان
چکیده
منابع مشابه
Semiparametric Quantile Regression with High-dimensional Covariates.
This paper is concerned with quantile regression for a semiparametric regression model, in which both the conditional mean and conditional variance function of the response given the covariates admit a single-index structure. This semiparametric regression model enables us to reduce the dimension of the covariates and simultaneously retains the flexibility of nonparametric regression. Under mil...
متن کاملOnline Decision-Making with High-Dimensional Covariates
Big data has enabled decision-makers to tailor choices at the individual-level. This involves learning a model of decision rewards conditional on individual-specific covariates. In domains such as medical decision-making and personalized advertising, these covariates are often high-dimensional ; however, typically only a small subset of these observed features are predictive of each decision’s ...
متن کاملTesting Endogeneity with High Dimensional Covariates∗
Modern, high dimensional data has renewed investigation on instrumental variables (IV) analysis, primary focusing on estimation of the included endogenous variable under sparsity and little attention towards specification tests. This paper studies in high dimensions the Durbin-Wu-Hausman (DWH) test, a popular specification test for endogeneity in IV regression. We show, surprisingly, that the D...
متن کاملTesting covariates in high-dimensional regression
Abstract In a high-dimensional linear regressionmodel, we propose a new procedure for testing statistical significance of a subset of regression coefficients. Specifically, we employ the partial covariances between the response variable and the tested covariates to obtain a test statistic. The resulting test is applicable even if the predictor dimension is much larger than the sample size. Unde...
متن کاملOn Sliced Inverse Regression With High-Dimensional Covariates
Sliced inverse regression is a promising method for the estimation of the central dimension-reduction subspace (CDR space) in semiparametric regression models. It is particularly useful in tackling cases with high-dimensional covariates. In this article we study the asymptotic behavior of the estimate of the CDR space with high-dimensional covariates, that is, when the dimension of the covariat...
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: Journal of Educational and Behavioral Statistics
سال: 2017
ISSN: 1076-9986,1935-1054
DOI: 10.3102/1076998617731518