scholarly journals The Bayesian Analysis of Complex, High-Dimensional Models: Can It Be CODA?

2014 ◽  
Vol 29 (4) ◽  
pp. 619-639 ◽  
Author(s):  
Y. Ritov ◽  
P. J. Bickel ◽  
A. C. Gamst ◽  
B. J. K. Kleijn
Keyword(s):  
Bayesian Analysis ◽  
High Dimensional ◽  
Dimensional Models

Inference for treatment effect parameters in potentially misspecified high-dimensional models

Biometrika ◽  
2020 ◽  
Author(s):  
Oliver Dukes ◽  
Stijn Vansteelandt
Keyword(s):  
Sample Size ◽  
Confidence Intervals ◽  
Treatment Effect ◽  
Observational Studies ◽  
Treatment Effects ◽  
Treatment Selection ◽  
High Dimensional ◽  
Selection Mechanism ◽  
Dimensional Models ◽  
Effect Parameter

Summary Eliminating the effect of confounding in observational studies typically involves fitting a model for an outcome adjusted for covariates. When, as often, these covariates are high-dimensional, this necessitates the use of sparse estimators, such as the lasso, or other regularization approaches. Naïve use of such estimators yields confidence intervals for the conditional treatment effect parameter that are not uniformly valid. Moreover, as the number of covariates grows with the sample size, correctly specifying a model for the outcome is nontrivial. In this article we deal with both of these concerns simultaneously, obtaining confidence intervals for conditional treatment effects that are uniformly valid, regardless of whether the outcome model is correct. This is done by incorporating an additional model for the treatment selection mechanism. When both models are correctly specified, we can weaken the standard conditions on model sparsity. Our procedure extends to multivariate treatment effect parameters and complex longitudinal settings.

scholarly journals Distributed testing and estimation under sparse high dimensional models

2018 ◽  
Vol 46 (3) ◽  
pp. 1352-1382 ◽  
Author(s):  
Heather Battey ◽  
Jianqing Fan ◽  
Han Liu ◽  
Junwei Lu ◽  
Ziwei Zhu
Keyword(s):  
High Dimensional ◽  
Distributed Testing ◽  
Dimensional Models
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