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

Chen, J.; Sitter, R. R.; Wu, Changbao

doi:10.1093/biomet/89.1.230

Cited by 114 publications

(96 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Detailed algorithms and computational code for finding empirical likelihood ratio confidence intervals can be found in Chen, Sitter and Wu (2002) and Wu (2004Wu ( , 2005. Finite sample performances of the interval C will be investigated through simulation studies to be presented in Section 4. through conditional models often requires that the characteristics of X for the two samples (i.e., the treatment group and the control group) follow the same distributions.…”

Section: The Propensity Score Adjusted Two-sample Empirical Likelihoodmentioning

confidence: 99%

Empirical Likelihood Methods for Complex Surveys With Data Missing-by-Design

Chen¹,

Thompson²,

Wu³

2018

STAT SINICA

View full text Add to dashboard Cite

We consider nonrandomized pretest-posttest designs with complex survey data for observational studies. We show that two-sample pseudo empirical likelihood methods provide efficient inferences on the treatment effect, with a missing-by-design feature used for forming the two samples and the baseline information incorporated through suitable constraints. The proposed maximum pseudo empirical likelihood estimators of the treatment effect are consistent and pseudo empirical likelihood ratio confidence intervals are constructed through bootstrap calibration methods. The proposed methods require estimation of propensity scores which depend on the underlying missing-by-design mechanism.A simulation study is conducted to examine finite sample performances of the proposed methods under different scenarios of nonignorable and ignorable missing patterns. An application to the International Tobacco Control Policy Evaluation Project Four Country Surveys is also presented to demonstrate the use of the proposed methods for examining the mode effect in survey data collection.

show abstract

Section: The Propensity Score Adjusted Two-sample Empirical Likelihoodmentioning

confidence: 99%

Empirical Likelihood Methods for Complex Surveys With Data Missing-by-Design

Chen¹,

Thompson²,

Wu³

2018

STAT SINICA

View full text Add to dashboard Cite

show abstract

“…For this choice of h x (u), for linear constraints, we need an outer loop (4), but not an inner (3) Chen et al (2002) give an alternative iterative Newton method for linear constraints using this estimator.…”

Section: Alternatives To Newton's Methodsmentioning

confidence: 99%

“…Deville and Särndal (1992), Chen et al (2002), and Singh and Mohl (1996) discuss methods for imposing range restrictions on calibrated weights. These ensure that each sampled unit represents a reasonable positive number of units in the population.…”

Section: Introductionmentioning

confidence: 99%

Incorporating User Input Into Optimal Constraining Procedures for Survey Estimates

Williams¹,

Berg

2013

Journal of Official Statistics

View full text Add to dashboard Cite

We examine the incorporation of analyst input into the constrained estimation process. In the calibration literature, there are numerous examples of estimators with "optimal" properties. We show that many of these can be derived from first principles. Furthermore, we provide mechanisms for injecting user input to create user-constrained optimal estimates. We include derivations for common deviance measures with linear and nonlinear constraints and we demonstrate these methods on a contingency table and a simulated survey data set. R code and examples are available at https://github.com/mwilli/Constrained-estimation.git.

show abstract

“…Théberge (2000) for a closed-form solution to the problem, and Beaumont and Bocci (2008) and Section 9 in Fuller (2002) for closely related model-based "ridge regression" approaches. See Chen et al (2002) for a less general modelling of errors using empirical likelihood methods. Similarly, in Wagner (2013) a vector of unknowns ε B was used to model the multiplicative error in part of the benchmark totals so that the corresponding subset B of BC is satisfied:…”

Section: Non-convergence and Penalised Calibrationmentioning

confidence: 99%

A global optimisation approach to range-restricted survey calibration

2017

View full text Add to dashboard Cite

Survey calibration methods modify minimally sample weights to satisfy domain-level benchmark constraints (BC), e.g. census totals. This allows exploitation of auxiliary information to improve the representativeness of sample data (addressing coverage limitations, non-response) and the quality of sample-based estimates of population parameters. Calibration methods may fail with samples presenting small/zero counts for some benchmark groups or when range restrictions (RR), such as positivity, are imposed to avoid unrealistic or extreme weights. Userdefined modifications of BC/RR performed after encountering non-convergence allow little control on the solution, and penalisation approaches modelling infeasibility may not guarantee convergence. Paradoxically, this has led to underuse in calibration of highly disaggregated information, when available. We present an always-convergent flexible twostep global optimisation (GO) survey calibration approach. The feasibility of the calibration problem is assessed, and automatically controlled minimum errors in BC or changes in RR are allowed to guarantee convergence in advance, while preserving the good properties of calibration estimators. Modelling alternatives under different scenarios using various error/change and distance measures are formulated and discussed. The GO approach is validated by calibrating the weights of the 2012 Health Survey for England to a fine age-gender-region cross-tabulation (378 counts) from the 2011 Census in England and Wales.

show abstract

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

Cited by 114 publications

References 5 publications

Empirical Likelihood Methods for Complex Surveys With Data Missing-by-Design

Empirical Likelihood Methods for Complex Surveys With Data Missing-by-Design

Incorporating User Input Into Optimal Constraining Procedures for Survey Estimates

A global optimisation approach to range-restricted survey calibration

Contact Info

Product

Resources

About