Calibrating Non-Probability Surveys to Estimated Control Totals Using LASSO, with An Application to Political Polling

Chen, Jack Kuang Tsung; Valliant, Richard; Elliott, Michael R.

doi:10.1111/rssc.12327

Cited by 22 publications

(21 citation statements)

References 17 publications

(17 reference statements)

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“…Chen, Valliant and Elliott (2018) and Chen et al . () proposed model‐based calibration estimators using the lasso based on non‐probability samples integrating with auxiliary known totals or probability samples respectively. However, their methods require that the working outcome model includes sufficient population information and therefore are not doubly robust.…”

Section: Asymptotic Results For Variable Selection and Estimationmentioning

confidence: 99%

“…On the basis of a single probability sample source, McConville et al (2017) proposed a model-assisted survey regression estimator of finite population totals using the lasso (the least absolute shrinkage and selection operator) to improve the efficiency. Chen, Valliant and Elliott (2018) and Chen et al (2019) proposed modelbased calibration estimators using the lasso based on non-probability samples integrating with auxiliary known totals or probability samples respectively. However, their methods require that the working outcome model includes sufficient population information and therefore are not doubly robust.…”

Section: Asymptotic Results For Variable Selection and Estimationmentioning

confidence: 99%

“…The calibration weighting estimator is

{\overset{μ}{true}}_{normalcal} = {\overset{μ}{true}}_{normalcal} = \frac{1}{N} {false\sum}_{i = 1}^{N} ω_{i} I_{B, i} Y_{i},

where

{ω_{i} : i \in B}

satisfies constraint (i)

{normalΣ}_{i \in {scriptS}_{normalB}} ω_{i} X_{i} = {normalΣ}_{i \in {scriptS}_{normalA}} d_{A, i} X_{i}

or constraint (ii)

{normalΣ}_{i \in {scriptS}_{normalB}} ω_{i} m false(X_{i}; \overset{β}{true} false) = {normalΣ}_{i \in A} d_{A, i} m false(X_{i}; \overset{β}{true} false)

(McConville et al ., ; Chen, Valliant and Elliott, 2018; Chen et al ., ).…”

Section: Basic Set‐upmentioning

confidence: 97%

“…The calibration weighting estimator is Chen, Valliant and Elliott, 2018;Chen et al, 2019). The justification forμ cal subject to constraint (i) relies on the linearity of the outcome model, i.e.…”

Section: Example 3 (Calibration Weighting)mentioning

confidence: 99%

“…One notable disadvantage of propensity score methods is that they rely on an explicit propensity score model and may be biased and highly variable if the model is misspecified (Kang and Schafer, 2007). The second type uses calibration weighting (Deville and Särndal, 1992;Kott, 2006;Chen, Valliant and Elliott, 2018;Chen et al, 2019). This technique forces the moments or the empirical distribution of auxiliary variables to be the same between the probability sample and the non-probability sample, so that after calibration the weighted distribution in the non-probability sample appears similar to that in the target population (DiSogra et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Doubly Robust Inference when Combining Probability and Non-Probability Samples with High Dimensional Data

Yang

Kim

Song

2020

Journal of the Royal Statistical Society Series B: Statistical Methodology

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Summary We consider integrating a non‐probability sample with a probability sample which provides high dimensional representative covariate information of the target population. We propose a two‐step approach for variable selection and finite population inference. In the first step, we use penalized estimating equations with folded concave penalties to select important variables and show selection consistency for general samples. In the second step, we focus on a doubly robust estimator of the finite population mean and re‐estimate the nuisance model parameters by minimizing the asymptotic squared bias of the doubly robust estimator. This estimating strategy mitigates the possible first‐step selection error and renders the doubly robust estimator root n consistent if either the sampling probability or the outcome model is correctly specified.

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Section: Asymptotic Results For Variable Selection and Estimationmentioning

confidence: 99%

Section: Asymptotic Results For Variable Selection and Estimationmentioning

confidence: 99%

“…The calibration weighting estimator is

{\overset{μ}{true}}_{normalcal} = {\overset{μ}{true}}_{normalcal} = \frac{1}{N} {false\sum}_{i = 1}^{N} ω_{i} I_{B, i} Y_{i},

where

{ω_{i} : i \in B}

satisfies constraint (i)

{normalΣ}_{i \in {scriptS}_{normalB}} ω_{i} X_{i} = {normalΣ}_{i \in {scriptS}_{normalA}} d_{A, i} X_{i}

or constraint (ii)

{normalΣ}_{i \in {scriptS}_{normalB}} ω_{i} m false(X_{i}; \overset{β}{true} false) = {normalΣ}_{i \in A} d_{A, i} m false(X_{i}; \overset{β}{true} false)

(McConville et al ., ; Chen, Valliant and Elliott, 2018; Chen et al ., ).…”

Section: Basic Set‐upmentioning

confidence: 97%

Section: Example 3 (Calibration Weighting)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Doubly Robust Inference when Combining Probability and Non-Probability Samples with High Dimensional Data

Yang

Kim

Song

2020

Journal of the Royal Statistical Society Series B: Statistical Methodology

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Variable selection in Propensity Score Adjustment to mitigate selection bias in online surveys

Ferri‐García

Rueda

2022

Stat Papers

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The development of new survey data collection methods such as online surveys has been particularly advantageous for social studies in terms of reduced costs, immediacy and enhanced questionnaire possibilities. However, many such methods are strongly affected by selection bias, leading to unreliable estimates. Calibration and Propensity Score Adjustment (PSA) have been proposed as methods to remove selection bias in online nonprobability surveys. Calibration requires population totals to be known for the auxiliary variables used in the procedure, while PSA estimates the volunteering propensity of an individual using predictive modelling. The variables included in these models must be carefully selected in order to maximise the accuracy of the final estimates. This study presents an application, using synthetic and real data, of variable selection techniques developed for knowledge discovery in data to choose the best subset of variables for propensity estimation. We also compare the performance of PSA using different classification algorithms, after which calibration is applied. We also present an application of this methodology in a real-world situation, using it to obtain estimates of population parameters. The results obtained show that variable selection using appropriate methods can provide less biased and more efficient estimates than using all available covariates.

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Model-assisted calibration with SCAD to estimated control for non-probability samples

Liu

Pan

2022

Stat Methods Appl

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Calibrating Non-Probability Surveys to Estimated Control Totals Using LASSO, with An Application to Political Polling

Cited by 22 publications

References 17 publications

Doubly Robust Inference when Combining Probability and Non-Probability Samples with High Dimensional Data

Doubly Robust Inference when Combining Probability and Non-Probability Samples with High Dimensional Data

Variable selection in Propensity Score Adjustment to mitigate selection bias in online surveys

Model-assisted calibration with SCAD to estimated control for non-probability samples

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