Generalized Regression Estimators with High-Dimensional Covariates.

@article{Ta2020GeneralizedRE,
  title={Generalized Regression Estimators with High-Dimensional Covariates.},
  author={Tram Ta and Jun Shao and Quefeng Li and Lei Wang},
  journal={Statistica Sinica},
  year={2020},
  volume={30 3},
  pages={
          1135-1154
        }
}
Data from a large number of covariates with known population totals are frequently observed in survey studies. These auxiliary variables contain valuable information that can be incorporated into estimation of the population total of a survey variable to improve the estimation precision. We consider the generalized regression estimator formulated under the model-assisted framework in which a regression model is utilized to make use of the available covariates while the estimator still has basic… 

Tables from this paper

Model-assisted estimation in high-dimensional settings for survey data
TLDR
A large simulation study is conducted on real data of Irish electricity consumption curves to assess the performance of several model-assisted estimators in terms of bias and efficiency, including penalized estimators and tree-based estimators.
Inference on Average Treatment Effect under Minimization and Other Covariate-Adaptive Randomization Methods
Covariate-adaptive randomization schemes such as the minimization and stratified permuted blocks are often applied in clinical trials to balance treatment assignments across prognostic factors. The
Inference after covariate-adaptive randomisation: aspects of methodology and theory
Covariate-adaptive randomisation has a more than 45 years of history of applications in clinical trials, in order to balance treatment assignments across prognostic factors that may have influence on
Toward Better Practice of Covariate Adjustment in Analyzing Randomized Clinical Trials
In randomized clinical trials, adjustments for baseline covariates at both design and analysis stages are highly encouraged by regulatory agencies. A recent trend is to use a model-assisted approach
Principles for Covariate Adjustment in Analyzing Randomized Clinical Trials
TLDR
This article presents three principles for model-assisted inference in simple or covariate-adaptive randomized trials and recommends a working model that includes all covariates utilized in randomization and all treatment-by-covariate interaction terms.
On Making Valid Inferences by Integrating Data from Surveys and Other Sources
TLDR
How big data may be used as predictors in small area estimation, a topic of current interest because of the growing demand for reliable local area statistics, is explained.
On Making Valid Inferences by Integrating Data from Surveys and Other Sources
TLDR
How big data may be used as predictors in small area estimation, a topic of current interest because of the growing demand for reliable local area statistics, is explained.

References

SHOWING 1-10 OF 40 REFERENCES
Improved estimation for complex surveys using modern regression techniques
In the field of survey statistics, finite population quantities are often estimated based on complex survey data. In this thesis, estimation of the finite population total of a study variable is
Model-Assisted Survey Regression Estimation with the Lasso
In the U.S. Forest Service’s Forest Inventory and Analysis (FIA) program, as in other natural resource surveys, many auxiliary variables are available for use in model-assisted inference about finite
Calibration Estimators in Survey Sampling
Abstract This article investigates estimation of finite population totals in the presence of univariate or multivariate auxiliary information. Estimation is equivalent to attaching weights to the
Calibration and partial calibration on principal components when the number of auxiliary variables is large
In survey sampling, calibration is a very popular tool used to make total estimators consistent with known totals of auxiliary variables and to reduce variance. When the number of auxiliary variables
Poststratification and Conditional Variance Estimation
Poststratification estimation is a technique used in sample surveys to improve efficiency of estimators. Survey weights are adjusted to force the estimated numbers of units in each of a set of
A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers
TLDR
A unified framework for establishing consistency and convergence rates for regularized M-estimators under high-dimensional scaling is provided and one main theorem is state and shown how it can be used to re-derive several existing results, and also to obtain several new results.
A two‐way classification of regression estimation strategies in probability sampling
This paper examines strategies for estimating the mean of a finite population in the following situation: A linear regression model is assumed to describe the population scatter. Various estimators β
Regression Shrinkage and Selection via the Lasso
TLDR
A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Some results on generalized difference estimation and generalized regression estimation for finite populations
Let S = {s} be the set of subsets of {1, ..., N} such that each s eS contains n elements. We consider in this paper only designs p(s) with fixed sample size, that is P(S) > 0 only if s eS and ESp(s)
Towards optimal regression estimation in sample surveys
The Montanari (1987) regression estimator is optimal when the population regression coefficients are known. When the coefficients are estimated, the Montanari estimator is not optimal and can be
...
...