# 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…

## 7 Citations

Model-assisted estimation in high-dimensional settings for survey data

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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…

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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…

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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.

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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

- Computer Science
- 2020

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.

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