Detangling robustness in high dimensions: Composite versus model-averaged estimation
نویسندگان
چکیده
منابع مشابه
Model averaged double robust estimation.
Researchers estimating causal effects are increasingly challenged with decisions on how to best control for a potentially high-dimensional set of confounders. Typically, a single propensity score model is chosen and used to adjust for confounding, while the uncertainty surrounding which covariates to include into the propensity score model is often ignored, and failure to include even one impor...
متن کاملRobust Sparse Estimation Tasks in High Dimensions
In this paper we initiate the study of whether or not sparse estimation tasks can be performed efficiently in high dimensions, in the robust setting where an ε-fraction of samples are corrupted adversarially. We study the natural robust version of two classical sparse estimation problems, namely, sparse mean estimation and sparse PCA in the spiked covariance model. For both of these problems, w...
متن کاملVariance Function Estimation in High-dimensions
We consider the high-dimensional heteroscedastic regression model, where the mean and the log variance are modeled as a linear combination of input variables. Existing literature on high-dimensional linear regression models has largely ignored nonconstant error variances, even though they commonly occur in a variety of applications ranging from biostatistics to finance. In this paper we study a...
متن کاملEstimation in High Dimensions: a Geometric Perspective
This tutorial provides an exposition of a flexible geometric framework for high dimensional estimation problems with constraints. The tutorial develops geometric intuition about high dimensional sets, justifies it with some results of asymptotic convex geometry, and demonstrates connections between geometric results and estimation problems. The theory is illustrated with applications to sparse ...
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: 2020
ISSN: 1935-7524
DOI: 10.1214/20-ejs1728