arXiv subjects

Qianyu Dong

Publications and source records attributed to Qianyu Dong.

4 recordsLinked to original sources

arXiv2026

Nonprobability Samples for Small Area Estimation: A Review and Comparative Simulation Study

Nonprobability samples (NPS) are attractive because they are less costly to collect, can provide substantially larger sample sizes, and may reach populations that traditional probability surveys do not. As response rates for traditional surveys fall, interest in NPS has grown rapidly within the field of survey statistics. These methods are especially relevant for small area estimation (SAE), where there is ever-present demand for estimates at fine geographic scales and detailed demographic domains. Despite rapid methodological development, there remains limited understanding of which approaches perform best under different conditions. In this paper, we review recent developments in NPS methodology, including the concept of data defect correlation (DDC) as a measure of data quality and as a tool for categorizing the various NPS methods. We then present a comprehensive simulation study that evaluates a range of NPS approaches under varying levels of DDC and extend several existing methods to the SAE setting.

stat.ME↗

arXiv2026

Design-Based Cross-Validation for Comparing Small Area Estimators

Subnational monitoring of public health and development indicators often relies on household surveys where data are sparse at the desired spatial resolution. Small area estimation (SAE) methods address this challenge by borrowing strength across areas and incorporating auxiliary information. However, comparing these estimators remains difficult in the absence of ground truth. We propose a design-based cross-validation framework for evaluating small area estimators that accommodates complex survey designs. Our approach enables model-agnostic comparisons between area-level and unit-level SAE models. We derive a decomposition of the conditional mean squared error that yields a consistent cross-validation score, show that finite-sample comparisons carry an unidentifiable bias that can be bounded, and use this bound as a principled threshold for ranking models. We further show that leave-one-area-out cross-validation, a popular alternative, targets extrapolation rather than smoothing error and can reverse the correct ranking. We evaluate the framework through extensive design-based simulations. We apply the framework to compare subnational female literacy estimators in Zambia using the 2024 Demographic and Health Survey. The framework applies broadly across prevalence mapping and other SAE problems and is applicable to any small area estimator irrespective of the underlying model class.

stat.ME↗

arXiv2025

sae4health: An R Shiny Application for Small Area Estimation in Low- and Middle-Income Countries

Accurate subnational estimation of health indicators is critical for public health planning, particularly in low- and middle-income countries (LMICs), where data and analytic tools are often limited. sae4health is an open-access Shiny application (https://rsc.stat.washington.edu/sae4health/) that generates small area estimates for more than 150 demographic and health indicators, based on over 150 Demographic and Health Surveys (DHS) from 60 countries. The platform offers both area- and unit-level models with spatial random effects, implemented through fast Bayesian inference using Integrated Nested Laplace Approximation (INLA). The app is fully browser-based and requires no data input, programming skills, or statistical modeling expertise, making advanced methods accessible to a wide range of users. Estimates are processed in real time and presented as interactive maps, tables, and downloadable reports. A companion website (https://sae4health.stat.uw.edu) provides documentation and methodological background to support the app. Together, these resources enhance access to subnational health data and facilitate the use of DHS surveys for evidence-based decision making.

stat.CO↗

arXiv2025

Toward a Principled Workflow for Prevalence Mapping Using Household Survey Data

Understanding the prevalence of key demographic and health indicators in small geographic areas and domains is of global interest, especially in low- and middle-income countries (LMICs), where vital registration data is sparse and household surveys are the primary source of information. Recent advances in computation and the increasing availability of spatially detailed datasets have led to much progress in sophisticated statistical modeling of prevalence. As a result, high-resolution prevalence maps for many indicators are routinely produced in the literature. However, statistical and practical guidance for producing prevalence maps in LMICs has been largely lacking. In particular, advice in choosing and evaluating models and interpreting results is needed, especially when data is limited. Software and analysis tools are also usually inaccessible to researchers in low-resource settings to conduct their own analysis or reproduce findings in the literature. In this paper, we propose a general workflow for prevalence mapping using household survey data. We consider all stages of the analysis pipeline, with particular emphasis on model choice and interpretation. We illustrate the proposed workflow using a case study mapping the proportion of pregnant women who had at least four antenatal care visits in Kenya. Reproducible code is provided in the Supplementary Materials and can be readily extended to a broad collection of indicators.

stat.AP↗