Calibration Techniques Encompassing Survey Sampling, Missing Data Analysis and Causal Inference (Q6089884)

scientific article; zbMATH DE number 7779064

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English	Calibration Techniques Encompassing Survey Sampling, Missing Data Analysis and Causal Inference	scientific article; zbMATH DE number 7779064

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instance of

scholarly article

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title

Calibration Techniques Encompassing Survey Sampling, Missing Data Analysis and Causal Inference (English)

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International Statistical Review

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publication date

15 December 2023

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zbMATH Keywords

covariate balancing

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empirical likelihood

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double robustness

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multiple robustness

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inverse probability weighting (IPW)

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propensity score

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range-respecting

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benchmark constraints

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MaRDI profile type

MaRDI publication profile

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full work available at URL

https://doi.org/10.1111/insr.12518

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cites work

Large Sample Properties of Matching Estimators for Average Treatment Effects

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Approximate Residual Balancing: Debiased Inference of Average Treatment Effects in High Dimensions

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Oracle, multiple robust and multipurpose calibration in a missing response problem

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Globally Efficient Non-Parametric Inference of Average Treatment Effects by Empirical Balancing Calibration Weighting

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OUP accepted manuscript

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Using empirical likelihood methods to obtain range restricted weights in regression estimators for surveys

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EMPIRICAL LIKELIHOOD METHODS FOR COMPLEX SURVEYS WITH DATA MISSING-BY-DESIGN

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Jackknife empirical likelihood method for multiply robust estimation with missing data

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Multiply robust nonparametric multiple imputation for the treatment of missing data

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Semiparametric estimation of treatment effect in a pretest-posttest study with missing data (with comments and rejoinder)

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Deville and Särndal's calibration: revisiting a 25-years-old successful optimization problem

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Calibration Estimators in Survey Sampling

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A further study of the multiply robust estimator in missing data analysis

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Multiply Robust Estimation in Regression Analysis With Missing Data

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Combining Inverse Probability Weighting and Multiple Imputation to Improve Robustness of Estimation

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Intrinsic efficiency and multiple robustness in longitudinal studies with drop-out: Table 1.

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Calibration and Multiple Robustness When Data Are Missing Not At Random

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Estimation with missing data: beyond double robustness

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Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score

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Empirical Likelihood-Based Estimation of the Treatment Effect in a Pretest–Posttest Study

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Covariate Balancing Propensity Score

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Connections between Survey Calibration Estimators and Semiparametric Models for Incomplete Data

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Robust estimation of causal effects via a high-dimensional covariate balancing propensity score

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Identifiers

DOI

10.1111/insr.12518

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Mathematics Subject Classification ID

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Sitelinks

Mathematics(1 entry)

mardi Publication:6089884