[HTML][HTML] A selective overview of variable selection in high dimensional feature space
High dimensional statistical problems arise from diverse fields of scientific research and
technological development. Variable selection plays a pivotal role in contemporary statistical …
technological development. Variable selection plays a pivotal role in contemporary statistical …
High-dimensional statistics with a view toward applications in biology
We review statistical methods for high-dimensional data analysis and pay particular
attention to recent developments for assessing uncertainties in terms of controlling false …
attention to recent developments for assessing uncertainties in terms of controlling false …
A modern maximum-likelihood theory for high-dimensional logistic regression
Students in statistics or data science usually learn early on that when the sample size n is
large relative to the number of variables p, fitting a logistic model by the method of maximum …
large relative to the number of variables p, fitting a logistic model by the method of maximum …
High-dimensional methods and inference on structural and treatment effects
Data with a large number of variables relative to the sample size—“high-dimensional data”—
are readily available and increasingly common in empirical economics. Highdimensional …
are readily available and increasingly common in empirical economics. Highdimensional …
On asymptotically optimal confidence regions and tests for high-dimensional models
On asymptotically optimal confidence regions and tests for high-dimensional models Page 1
The Annals of Statistics 2014, Vol. 42, No. 3, 1166–1202 DOI: 10.1214/14-AOS1221 © Institute …
The Annals of Statistics 2014, Vol. 42, No. 3, 1166–1202 DOI: 10.1214/14-AOS1221 © Institute …
Inference on treatment effects after selection among high-dimensional controls
A Belloni, V Chernozhukov… - Review of Economic …, 2014 - academic.oup.com
We propose robust methods for inference about the effect of a treatment variable on a scalar
outcome in the presence of very many regressors in a model with possibly non-Gaussian …
outcome in the presence of very many regressors in a model with possibly non-Gaussian …
Adaptive huber regression
Big data can easily be contaminated by outliers or contain variables with heavy-tailed
distributions, which makes many conventional methods inadequate. To address this …
distributions, which makes many conventional methods inadequate. To address this …
Classification vs regression in overparameterized regimes: Does the loss function matter?
We compare classification and regression tasks in an overparameterized linear model with
Gaussian features. On the one hand, we show that with sufficient overparameterization all …
Gaussian features. On the one hand, we show that with sufficient overparameterization all …
Nearly unbiased variable selection under minimax concave penalty
CH Zhang - 2010 - projecteuclid.org
We propose MC+, a fast, continuous, nearly unbiased and accurate method of penalized
variable selection in high-dimensional linear regression. The LASSO is fast and continuous …
variable selection in high-dimensional linear regression. The LASSO is fast and continuous …
Program evaluation and causal inference with high‐dimensional data
In this paper, we provide efficient estimators and honest confidence bands for a variety of
treatment effects including local average (LATE) and local quantile treatment effects (LQTE) …
treatment effects including local average (LATE) and local quantile treatment effects (LQTE) …