arXiv subjects

Victoire Michal

Publications and source records attributed to Victoire Michal.

4 recordsLinked to original sources

arXiv2024

A spatio-temporal model to detect potential outliers in disease mapping

Spatio-temporal disease mapping models are commonly used to estimate the relative risk of a disease over time and across areas. For each area and time point, the disease count is modelled with a Poisson distribution whose mean is the product of an offset and the disease relative risk. This relative risk is commonly decomposed in the log scale as the sum of fixed and latent effects. The Rushworth model allows for spatio-temporal autocorrelation of the random effects. We build on the Rushworth model to accommodate and identify potentially outlying areas with respect to their disease relative risk evolution, after taking into account the fixed effects. An area may display outlying behaviour at some points in time but not all. At each time point, we assume the latent effects to be spatially structured and include scaling parameters in the precision matrix, to allow for heavy-tails. Two prior specifications are considered for the scaling parameters: one where they are independent across space and one with spatial autocorrelation. We investigate the performance of the different prior specifications of the proposed model through simulation studies and analyse the weekly evolution of the number of COVID-19 cases across the 33 boroughs of Montreal and the 96 French departments during the second wave. In Montreal, 6 boroughs are found to be potentially outlying. In France, the model with spatially structured scaling parameters identified 21 departments as potential outliers. We find that these departments tend to be close to each other and within common French regions.

stat.ME↗

arXiv2023

Small Area Estimation with Random Forests and the LASSO

We consider random forests and LASSO methods for model-based small area estimation when the number of areas with sampled data is a small fraction of the total areas for which estimates are required. Abundant auxiliary information is available for the sampled areas, from the survey, and for all areas, from an exterior source, and the goal is to use auxiliary variables to predict the outcome of interest. We compare areal-level random forests and LASSO approaches to a frequentist forward variable selection approach and a Bayesian shrinkage method. Further, to measure the uncertainty of estimates obtained from random forests and the LASSO, we propose a modification of the split conformal procedure that relaxes the assumption of identically distributed data. This work is motivated by Ghanaian data available from the sixth Living Standard Survey (GLSS) and the 2010 Population and Housing Census. We estimate the areal mean household log consumption using both datasets. The outcome variable is measured only in the GLSS for 3\% of all the areas (136 out of 5019) and more than 170 potential covariates are available from both datasets. Among the four modelling methods considered, the Bayesian shrinkage performed the best in terms of bias, MSE and prediction interval coverages and scores, as assessed through a cross-validation study. We find substantial between-area variation, the log consumption areal point estimates showing a 1.3-fold variation across the GAMA region. The western areas are the poorest while the Accra Metropolitan Area district gathers the richest areas.

stat.ME↗

arXiv2021

A Bayesian hierarchical model for disease mapping that accounts for scaling and heavy-tailed latent effects

In disease mapping, the relative risk of a disease is commonly estimated across different areas within a region of interest. The number of cases in an area is often assumed to follow a Poisson distribution whose mean is decomposed as the product between an offset and the logarithm of the disease's relative risk. The log risk may be written as the sum of fixed effects and latent random effects. The BYM2 model decomposes each latent effect into a weighted sum of independent and spatial effects. We build on the BYM2 model to allow for heavy-tailed latent effects and accommodate potentially outlying risks, after accounting for the fixed effects. We assume a scale mixture structure wherein the variance of the latent process changes across areas and allows for outlier identification. We propose two prior specifications for this scale mixture parameter. These are compared through simulation studies and in the analysis of Zika cases from the first (2015-2016) epidemic in Rio de Janeiro city, Brazil. The simulation studies show that, in terms of the model assessment criterion WAIC and outlier detection, the two proposed parametrisations perform better than the model proposed by Congdon (2017) to capture outliers. In particular, the proposed parametrisations are more efficient, in terms of outlier detection, than Congdon's when outliers are neighbours. Our analysis of Zika cases finds 19 out of 160 districts of Rio as potential outliers, after accounting for the socio-development index. Our proposed model may help prioritise interventions and identify potential issues in the recording of cases.

stat.ME↗

arXiv2020

Efficient multiply robust imputation in the presence of influential units in surveys

Item nonresponse is a common issue in surveys. Because unadjusted estimators may be biased in the presence of nonresponse, it is common practice to impute the missing values with the objective of reducing the nonresponse bias as much as possible. However, commonly used imputation procedures may lead to unstable estimators of population totals/means when influential units are present in the set of respondents. In this article, we consider the class of multiply robust imputation procedures that provide some protection against the failure of underlying model assumptions. We develop an efficient version of multiply robust estimators based on the concept of conditional bias, a measure of influence. We present the results of a simulation study to show the benefits of the proposed method in terms of bias and efficiency.

stat.ME↗