Bootstrap Inference for the Finite Population Mean under Complex Sampling Designs

Wang, Zhonglei; Peng, Liuhua; Kim, Jae Kwang

doi:10.1111/rssb.12506

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…The proposed estimator performs better than Bugni's estimator and Ye's estimator for the outcome models being nonlinear. In the future, we will consider how to improve the estimation efficiency in complex outcome models and how to estimate the treatment effect in the finite population under complex sampling designs (Wang, Kim, & Peng, 2022).…”

Section: Discussionmentioning

confidence: 99%

Improved estimation of average treatment effects under covariate‐adaptive randomization methods

Wang,

2023

Statistica Neerlandica

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Estimation of the average treatment effect is one of the crucial problems in clinical trials for two or multiple treatments. The covariate‐adaptive randomization methods are often applied to balance treatment assignments across prognostic factors in clinical trials, such as the minimization and stratified permuted blocks method. We propose a model‐free estimator of average treatment effects under covariate‐adaptive randomization methods, which is least square adjustment for the estimator of outcome models. The proposed estimator is not only applicable to the case of binary treatment, but also can be extended to the case of multiple treatment. The proposed estimator is consistent and asymptotically normally distributed. Simulation studies show that the proposed estimator and Ye's estimator are comparable, and it performs better than Bugni's estimator when the outcome model is linear. The proposed estimator has some advantages over targeted maximum likelihood estimator, Bugni's estimator and Ye's estimator in terms of the standard error and root mean squared error when the outcome model is nonlinear. The proposed estimator is stable for the from of outcome model. Finally, we apply the proposed methodology to a dataset that studies the causal effect promotional videos mode on the school‐age children's educational attainment in Peru.

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Section: Discussionmentioning

confidence: 99%

Improved estimation of average treatment effects under covariate‐adaptive randomization methods

Wang,

2023

Statistica Neerlandica

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“…under stratified random sampling is used. See [16] and [15] for the solution used in our implementation and [19] for a recent discussion on the use of Bootstrap ideas under complex survey sampling. The second strategy is based on a Bayesian estimation procedure and uses a dynamic post-stratification to impute the sufficient statistics of the Bayesian model.…”

Section: Objectivesmentioning

confidence: 99%

Dealing with missing data under stratified sampling designs where strata are study domains

Rodríguez¹,

Nieto‐Barajas

Pérez-Pérez

2022

Journal of Applied Statistics

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A quick count seeks to estimate the voting trends of an election and communicate them to the population on the evening of the same day of the election. In quick counts, the sampling is based on a stratified design of polling stations. Voting information is gathered gradually, often with no guarantee of obtaining the complete sample or even information in all the strata. However, accurate interval estimates with partial information must be obtained. Furthermore, this becomes more challenging if the strata are additionally study domains. To produce partial estimates, two strategies are proposed: 1) A Bayesian model using a dynamic post-stratification strategy and a single imputation process defined after a thorough analysis of historic voting information. Additionally, a credibility level correction is included to solve the underestimation of the variance; 2) a frequentist alternative that combines standard multiple imputation ideas with classic sampling techniques to obtain estimates under a missing information framework. Both solutions are illustrated and compared using information from the 2021 quick count. The aim was to estimate the composition of the Chamber of Deputies in Mexico.

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Matrix completion under complex survey sampling

Mao

Wang²,

Yang³

2022

Ann Inst Stat Math

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Bootstrap Inference for the Finite Population Mean under Complex Sampling Designs

Cited by 3 publications

References 33 publications

Improved estimation of average treatment effects under covariate‐adaptive randomization methods

Improved estimation of average treatment effects under covariate‐adaptive randomization methods

Dealing with missing data under stratified sampling designs where strata are study domains

Matrix completion under complex survey sampling

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