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Supplementary Appendix for "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls"
Supplementary Appendix "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls"
2013/6/14
In this supplementary appendix we provide additional results, omitted proofs and extensive simulations that complement the analysis of the main text
Model Selection for High-Dimensional Regression under the Generalized Irrepresentability Condition
Model Selection High-Dimensional Regression Generalized Irrepresentability Condition
2013/6/13
In the high-dimensional regression model a response variable is linearly related to $p$ covariates, but the sample size $n$ is smaller than $p$. We assume that only a small subset of covariates is `ac...
Efficient sampling of high-dimensional Gaussian fields: the non-stationary / non-sparse case
Efficient sampling high-dimensional Gaussian non-stationary non-sparse case
2011/6/20
This paper is devoted to the problem of sampling Gaussian fields
in high dimension. Solutions exist for two specific structures of inverse
covariance : sparse and circulant. The proposed approach is...
Parameter estimation in high dimensional Gaussian distributions
high dimensional Gaussian Parameter estimation massive memory
2011/6/20
In order to compute the log-likelihood for high dimensional spatial Gaussian models, it is
necessary to compute the determinant of the large, sparse, symmetric positive definite precision
matrix, Q....
Independent screening for single-index hazard rate models with ultra-high dimensional features
screening univariate regression models generalized linear models single-index
2011/6/17
In data sets with many more features than observations, independent screening based on all
univariate regression models leads to a computationally convenient variable selection method.
Recent effort...
A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers
unified framework high-dimensional analysis $M$-estimators decomposable regularizers
2010/10/19
High-dimensional statistical inference deals with models in which the the number of parameters $p$ is comparable to or larger than the sample size $n$. Since it is usually impossible to obtain consist...