conduct large-scale measurement

This study addresses the challenge of achieving safe and efficient unbiased estimation using non-probability data in the absence of a controlled selection mechanism. The authors propose Model-Assisted Data Integration (MADI), a sampling strategy that integrates non-probability data with carefully designed probability samples and leverages arbitrary machine learning models to construct design-unbiased point estimators alongside corresponding unbiased variance estimators. MADI establishes, for the first time, a general framework for design-unbiased inference based on any machine learning model, offering both theoretical rigor and practical feasibility—particularly suited for high-frequency production environments in official statistics. Empirical results demonstrate that MADI substantially reduces estimation variance compared to traditional survey estimators, confirming its effectiveness and superiority.

This study addresses the undercoverage of credible intervals in small-area estimation under complex survey designs typical of Demographic and Health Surveys (DHS) in low-income countries, where conventional Fay–Herriot models underestimate design variance uncertainty due to misspecification of the sampling distribution as chi-squared. The authors derive and compare two design-based sampling distributions for the design variance tailored to stratified two-stage cluster sampling like that used in DHS, integrating them within a Bayesian framework enhanced by variance smoothing techniques to more accurately capture uncertainty. Simulations and an application to the 2022 Kenya DHS demonstrate that the proposed approaches substantially improve credible interval coverage and scoring rule performance. Notably, the simpler of the two distributions offers comparable accuracy with greater computational ease and is successfully applied to small-area estimation of child height-for-age Z-scores.

This study addresses the practical challenge of precisely controlling higher-order inclusion probabilities under two-stage indirect sampling in face-to-face surveys. We propose a global optimization framework integrating Determinantal Point Process (DPP) sampling design with the Generalized Weight Sharing Method (GWSM). By deriving the closed-form solution for the optimal weight matrix under GWSM, we establish, for the first time, analytical expressions for optimal first-order and joint second-order inclusion probabilities at the second stage—thereby enabling a computationally tractable and interpretable implementation of the Horvitz–Thompson estimator. Leveraging the parametrizable nature of DPPs, our method accurately models and controls higher-order inclusion structures within complex survey networks. Comprehensive empirical validation on real survey data demonstrates substantial improvements in inclusion probability calibration accuracy, as well as enhanced estimation efficiency and robustness.

Nonprobability surveys suffer from two long-overlooked sources of bias—measurement error and selection bias—neither of which is adequately addressed by conventional approaches. Method: We propose a composite estimator that jointly integrates probability and nonprobability samples, systematically correcting for both measurement error (e.g., via calibration or proxy modeling) and selection bias (e.g., via weighting or propensity scoring) within a unified finite-population mean estimation framework. Contribution/Results: Theoretically, our estimator achieves lower mean squared error than standard selection-bias-only corrections. Empirically, applied to U.S. business online survey data, it yields significantly higher accuracy than pure probability-survey estimators and aligns more closely with large-scale government benchmark surveys. This work establishes a novel, theoretically rigorous yet practically implementable pathway for scientifically leveraging nonprobability data in official and academic statistics.

To address nonignorable nonresponse in sample surveys, this paper extends model-assisted estimation to the missing-at-random (MAR) framework. We propose a calibratable inverse-probability weighting (IPW) method that reweights sampled units in a second stage to compensate for nonrespondents, and systematically construct a Horvitz–Thompson-type adjusted estimator. Theoretically, we establish its asymptotic design-unbiasedness and design-consistency, derive a closed-form asymptotic variance expression, and provide a consistent variance estimator. Monte Carlo simulations demonstrate that the proposed estimator significantly outperforms the conventional Horvitz–Thompson estimator under diverse nonresponse mechanisms. Our key contributions are: (i) the first systematic adaptation of model-assisted estimation to the MAR setting; and (ii) a novel IPW weighting scheme that simultaneously satisfies calibration constraints and enjoys rigorous asymptotic properties—namely, design-consistency, asymptotic normality, and consistent variance estimation.

This study addresses the issue of variance inflation in regression models under complex survey designs, which often arises from unnecessary variability in sampling weights. The authors propose a novel approach that, for the first time, integrates stabilized weights with generalized raking within a two-stage sampling framework, leveraging auxiliary covariate information to effectively reduce extraneous weight variation. This method substantially enhances the efficiency of design-based estimators while remaining compatible with standard statistical software. Simulation studies demonstrate that, under typical two-stage survey designs, the proposed estimator achieves markedly higher precision compared to existing methods. The approach has been successfully applied to a large-scale multinational study of Kaposi’s sarcoma, illustrating its practical utility and robustness in real-world settings.

Modern heterogeneity-robust difference-in-differences estimators derive their asymptotic properties under iid, cluster, or fixed-design frameworks that abstract from complex survey sampling, yet practitioners routinely apply them to nationally representative surveys with stratified cluster designs. We show that, under standard regularity conditions, the influence functions of each smooth IF-based or regression-based modern DiD estimator satisfy Binder's (1983) smoothness conditions, so the standard stratified-cluster variance formula applied to their values produces design-consistent standard errors. A Monte Carlo study with 66,000 replications shows where the design effect comes from. HC1 standard errors that treat observations as iid produce coverage as low as 34% under a baseline survey design and below 11% under informative sampling. Combining the survey-weighted point estimate with PSU-level clustering - the practitioner's cluster=psu heuristic - recovers near-nominal coverage across all scenarios. Adding strata and finite-population corrections yields incremental precision but is not required for valid coverage. Survey-weighted doubly robust estimation produces well-calibrated inference when parallel trends hold only conditionally. An NHANES illustration of the ACA dependent coverage provision shows that point estimates and standard errors change substantively - enough to reverse significance conclusions - when the survey design is accounted for. We provide diff-diff (https://github.com/igerber/diff-diff), an open-source Python package implementing design-based variance for fifteen modern DiD estimators.

This study addresses the coarsening of self-reported numeric variables in surveys—often caused by rounding or heaping—by proposing a novel approach that integrates design-based inference with latent variable modeling. Treating observed values as coarsened manifestations of an underlying continuous latent variable, the method jointly models the coarsening mechanism and the latent distribution via a survey-weighted pseudo-likelihood. It generates posterior predictive replicates to propagate coarsening-induced uncertainty into standard design-based estimators. This framework is the first to explicitly correct for coarsening bias under complex sampling designs, enabling unbiased estimation of means, quantiles, and threshold-based prevalence measures. Simulation studies demonstrate robustness across various model misspecifications and sampling scenarios, and empirical application to Italy’s PASSI behavioral surveillance data shows effective correction of coarsening-related estimation bias.