arXiv subjects

Yunhan Wu

Publications and source records attributed to Yunhan Wu.

11 recordsLinked to original sources

arXiv2026

Areal Disaggregation: A Small Area Estimation Perspective

Producing reliable estimates of health and demographic indicators at fine areal scales is crucial for examining heterogeneity and supporting localized health policy. However, many surveys release outcomes only at coarser administrative levels, thereby limiting their relevance for decision-making. We propose a fully Bayesian, single-stage spatial modeling framework for area-level disaggregation that generates fine-scale estimates of indicators directly from coarsely aggregated survey data. By defining a latent spatial process at the target resolution and linking it to observed outcomes through an aggregation step, the framework adopts small-area estimation techniques while incorporating covariates and delivering coherent uncertainty quantification. The proposed methods are implemented with inlabru to achieve computational efficiency. We evaluate performance through a simulation study of general fertility rates in Kenya to demonstrate the models' ability to recover fine-scale variation across diverse data-generating scenarios. We further apply the framework to two national surveys to produce district-level fertility estimates from the 2022 Kenya Demographic and Health Survey and, more importantly, district-level indicators for unpaid care and domestic work and mass media usage from the 2021 Kenya Time Use Survey.

stat.ME↗

arXiv2026

Automatic Variance Adjustment for Small Area Estimation

Small area estimation (SAE) is a common endeavor and is used in a variety of disciplines. In low- and middle-income countries (LMICs), in which household surveys provide the most reliable and timely source of data, SAE is vital for highlighting disparities in health and demographic indicators. Weighted estimators are ideal for inference, but for fine geographical partitions in which there are insufficient data, SAE models are required. The most common approach is Fay-Herriot area-level modeling in which the data requirements are a weighted estimate and an associated variance estimate. The latter can be undefined or unstable when data are sparse and so we propose a principled modification which is based on augmenting the available data with a prior sample from a hypothetical survey. This adjustment is generally available, respects the design and is simple to implement. We examine the empirical properties of the adjustment through simulation and illustrate its use with wasting data from a 2018 Zambian Demographic and Health Survey. The modification is implemented as an automatic remedy in the R package surveyPrev, which provides a comprehensive suite of tools for conducing SAE in LMICs.

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

Small Area Estimation of Fertility in Low- and Middle-Income Countries

Accurate fertility estimates at fine spatial resolution are essential for localized public health planning, particularly in low- and middle-income countries (LMICs). While national-level indicators such as age-specific fertility rates (ASFR) and total fertility rate (TFR) are often reported through official statistics, they lack the spatial granularity needed to guide targeted interventions. To address this, we develop a framework for subnational fertility estimation using small-area estimation (SAE) techniques applied to birth history data from household surveys, in particular Demographic and Health Surveys (DHS). Disaggregation by geographic area, time period, and maternal age group leads to significant data sparsity, limiting the reliability of direct estimates at fine scales. To overcome this, we propose a suite of methods, including direct estimators, area-level and unit-level Bayesian hierarchical models, to produce accurate estimates across varying spatial resolutions. The model-based approaches incorporate spatiotemporal smoothing and integrate covariates such as maternal education, contraceptive use and urbanicity. Using data from the 2021 Madagascar DHS, we generate district-level ASFR and TFR estimates and evaluate model performance through cross-validation.

stat.ME↗

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↗

arXiv2025

Small Area Estimation of Education Levels in Low- and Middle-Income Countries

Education is a key driver of social and economic mobility, yet disparities in attainment persist, particularly in low- and middle-income countries (LMICs). Existing indicators, such as mean years of schooling for adults aged 25 and older (MYS25) and expected years of schooling (EYS), offer a snapshot of an educational system, but lack either cohort-specific or temporal granularity. To address these limitations, we introduce the ultimate years of schooling (UYS)-a birth cohort-based metric targeting the final educational attainment of any individual cohort, including those with ongoing schooling trajectories. As with many attainment indicators, we propose to estimate UYS with cross-sectional household surveys. However, for younger cohorts, estimation fails, because these individuals are right-censored leading to severe downwards bias. To correct for this, we propose to re-frame educational attainment as a time-to-event process and deploy discrete-time survival models that explicitly account for censoring in the observations. At the national level, we estimate the parameters of the model using survey-weighted logistic regression, while for finer spatial resolutions, where sample sizes are smaller, we embed the discrete-time survival model within a Bayesian spatiotemporal framework to improve stability and precision. Applying our proposed methods to data from the 2022 Tanzania Demographic and Health Surveys, we estimate female educational trajectories corrected for censoring biases, and reveal substantial subnational disparities. By providing a dynamic, bias-corrected, and spatially disaggregated measure, our approach enhances education monitoring; it equips policymakers and researchers with a more precise tool for monitoring current progress towards education goals, and for designing future targeted policy interventions in LMICs.

stat.ME↗

arXiv2022

Modeling Urban/Rural Fractions in Low- and Middle-Income Countries

In low- and middle-income countries, household surveys are the most reliable data source to examine health and demographic indicators at the subnational level, an exercise in small area estimation. Model-based unit-level models are favored in producing the subnational estimates at fine scale, such as the admin-2 level. Typically, the surveys employ stratified two-stage cluster sampling with strata consisting of an urban/rural designation crossed with administrative regions. To avoid bias and increase predictive precision, the stratification should be acknowledged in the analysis. To move from the cluster to the area requires an aggregation step in which the prevalence surface is averaged with respect to population density. This requires estimating a partition of the study area into its urban and rural components, and to do this we experiment with a variety of classification algorithms, including logistic regression, Bayesian additive regression trees and gradient boosted trees. Pixel-level covariate surfaces are used to improve prediction. We estimate spatial HIV prevalence in women of age 15-49 in Malawi using the stratification/aggregation method we propose.

stat.ME↗

arXiv2022

Bilingual by default: Voice Assistants and the role of code-switching in creating a bilingual user experience

Conversational User Interfaces such as Voice Assistants are hugely popular. Yet they are designed to be monolingual by default, lacking support for, or sensitivity to, the bilingual dialogue experience. In this provocation paper, we highlight the language production challenges faced in VA interaction for bilingual users. We argue that, by facilitating phenomena seen in bilingual interaction, such as code-switching, we can foster a more inclusive and improved user experience for bilingual users. We also explore ways that this might be achieved, through the support of multiple language recognition as well as being sensitive to the preferences of code-switching in speech output.

cs.HC↗

arXiv2020

See what I'm saying? Comparing Intelligent Personal Assistant use for Native and Non-Native Language Speakers

Limited linguistic coverage for Intelligent Personal Assistants (IPAs) means that many interact in a non-native language. Yet we know little about how IPAs currently support or hinder these users. Through native (L1) and non-native (L2) English speakers interacting with Google Assistant on a smartphone and smart speaker, we aim to understand this more deeply. Interviews revealed that L2 speakers prioritised utterance planning around perceived linguistic limitations, as opposed to L1 speakers prioritising succinctness because of system limitations. L2 speakers see IPAs as insensitive to linguistic needs resulting in failed interaction. L2 speakers clearly preferred using smartphones, as visual feedback supported diagnoses of communication breakdowns whilst allowing time to process query results. Conversely, L1 speakers preferred smart speakers, with audio feedback being seen as sufficient. We discuss the need to tailor the IPA experience for L2 users, emphasising visual feedback whilst reducing the burden of language production.

cs.HC↗

arXiv2020

Mental Workload and Language Production in Non-Native Speaker IPA Interaction

Through proliferation on smartphones and smart speakers, intelligent personal assistants (IPAs) have made speech a common interaction modality. Yet, due to linguistic coverage and varying levels of functionality, many speakers engage with IPAs using a non-native language. This may impact the mental workload and pattern of language production displayed by non-native speakers. We present a mixed-design experiment, wherein native (L1) and non-native (L2) English speakers completed tasks with IPAs through smartphones and smart speakers. We found significantly higher mental workload for L2 speakers during IPA interactions. Contrary to our hypotheses, we found no significant differences between L1 and L2 speakers in terms of number of turns, lexical complexity, diversity, or lexical adaptation when encountering errors. These findings are discussed in relation to language production and processing load increases for L2 speakers in IPA interaction.

cs.HC↗

arXiv2016

Atomic size zone interaction potential between two ground-state cold atoms

The complex-source-point model are already used in the exact solution for the urtrashort pulse and nonparaxial beam. In this letter we have used the complex-source-point model to deduce the interaction potential equation for the separation R between two atoms which is comparable with the size of the atoms. We show the result and the characteristics of the numerical calculation. Since the singular point around R=0 is removed by using the complex-source-point model, so that we can obtain the result force around R=0. With the decreasing of the distance between two atoms, the force switches from the electromagnetic force to the strong force by use our equation.

physics.atom-ph↗