arXiv subjects

Katherine Wilson

Publications and source records attributed to Katherine Wilson.

3 recordsLinked to original sources

arXiv2024

Direct-Assisted Bayesian Unit-level Modeling for Small Area Estimation of Rare Event Prevalence

Small area estimation using survey data can be achieved by using either a design-based or a model-based inferential approach. Design-based direct estimators are generally preferable because of their consistency, asymptotic normality, and reliance on fewer assumptions. However, when data are sparse at the desired area level, as is often the case when measuring rare events, these direct estimators can have extremely large uncertainty, making a model-based approach preferable. A model-based approach with a random spatial effect borrows information from surrounding areas at the cost of inducing shrinkage. As a result, estimates may be over-smoothed and inconsistent with design-based estimates at higher area levels when aggregated. We propose two unit-level Bayesian models for small area estimation of rare event prevalence which use design-based direct estimates at a higher area level to increase consistency in aggregation. This model framework is designed to accommodate sparse data obtained from two-stage stratified cluster sampling, which is particularly relevant to applications in low- and middle-income countries. After introducing the model framework and its implementation, we conduct a simulation study to evaluate its properties and apply it to the estimation of the neonatal mortality rate in Zambia, using 2014 Demographic Health Surveys data.

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arXiv2023

A Pseudo-likelihood Approach to Under-5 Mortality Estimation

Accurate and precise estimates of the under-5 mortality rate (U5MR) are an important health summary for countries. However, full survival curves allow us to better understand the pattern of mortality in children under five. Modern demographic methods for estimating a full mortality schedule for children have been developed for countries with good vital registration and reliable census data, but perform poorly in many low- and middle-income countries (LMICs). In these countries, the need to utilize nationally representative surveys to estimate the U5MR requires additional care to mitigate potential biases in survey data, acknowledge the survey design, and handle the usual characteristics of survival data, for example, censoring and truncation. In this paper, we develop parametric and non-parametric pseudo-likelihood approaches to estimating child mortality across calendar time from complex survey data. We show that the parametric approach is particularly useful in scenarios where data are sparse and parsimonious models allow efficient estimation. We compare a variety of parametric models to two existing methods for obtaining a full survival curve for children under the age of 5, and argue that a parametric pseudo-likelihood approach is advantageous in LMICs. We apply our proposed approaches to survey data from four LMICs.

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arXiv2017

Pointless Continuous Spatial Surface Reconstruction

The analysis of area-level aggregated summary data is common in many disciplines including epidemiology and the social sciences. Typically, Markov random field spatial models have been employed to acknowledge spatial dependence and allow data-driven smoothing. In this paper, we exploit recent theoretical and computational advances in continuous spatial modeling to carry out the reconstruction of an underlying continuous spatial surface. In particular, we focus on models based on stochastic partial differential equations (SPDEs). We also consider the interesting case in which the aggregate data are supplemented with point data. We carry out Bayesian inference, and in the language of generalized linear mixed models, if the link is linear, an efficient implementation of the model is available via integrated nested Laplace approximations. For nonlinear links, we present two approaches: a fully Bayesian implementation using a Hamiltonian Monte Carlo algorithm, and an empirical Bayes implementation, that is much faster, and is based on Laplace approximations. We examine the properties of the approach using simulation, and then estimate an underlying continuous risk surface for the classic Scottish lip cancer data.

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