arXiv subjects

Francis Hui

Publications and source records attributed to Francis Hui.

4 recordsLinked to original sources

arXiv2026

Climate sensitivity analysis -- A case study from forty years of US compositional cause of death data

Emerging climate risks pose pressing challenges for insurers, governments, and businesses worldwide, as they face growing uncertainty in quantifying the impacts of climate change from physical risks. This paper aims to understand the impact of specific climate factors on mortality by cause for subgroups of the United States (US) population, through sensitivity and scenario analysis based on an increase in temperature and sea level extremes. We apply compositional data analysis (CODA) techniques to examine cause-specific deaths, treating the density of deaths as a set of dependent, non-negative values that sum to one. We couple CODA with principal component analysis on the climate factors as a means of dimension reduction, and fit generalised additive models to better reflect the non-linear relationships between the dimension-reduced principal scores and mortality by cause. The results of our analysis indicate climate-related factors have varying impacts by cause and ages within each cause, with more pronounced increases in the proportions of deaths from hypertensive heart disease as temperature and sea level extremes increase. Scenario analysis also indicates that an increase in temperature high extremes, sea level, and rainfall, in conjunction with a decrease in low temperature extremes, lead to offsetting impacts on the proportion of deaths between climate-related causes, but less offsetting by age within causes. Furthermore, the impacts on the proportions of death are more pronounced for ages between 55 and 95, reinforcing the observation that climate-related risks have a greater impact on older (and potentially more vulnerable) subgroups of the population. For life insurers specifically, these results are consistent with the natural hedge that arises between annuity and protection products, in light of increasing climate risk.

stat.AP↗

arXiv2025

A Compositional Approach to Modelling Cause-specific Mortality with Zero Counts

Understanding and forecasting mortality by cause is an essential branch of actuarial science, with wide-ranging implications for decision-makers in public policy and industry. To accurately capture trends in cause-specific mortality, it is critical to consider dependencies between causes of death and produce forecasts by age and cause coherent with aggregate mortality forecasts. One way to achieve these aims is to model cause-specific deaths using compositional data analysis (CODA), treating the density of deaths by age and cause as a set of dependent, non-negative values that sum to one. A major drawback of standard CODA methods is the challenge of zero values, which frequently occur in cause-of-death mortality modelling. Thus, we propose using a compositional power transformation, the $\alpha$-transformation, to model cause-specific life-table death counts. The $\alpha$-transformation offers a statistically rigorous approach to handling zero value subgroups in CODA compared to \emph{ad-hoc} techniques: adding an arbitrarily small amount. We illustrate the $\alpha$-transformation on England and Wales, and US death counts by cause from the Human Cause-of-Death database, for cardiovascular-related causes of death. Results demonstrate the $\alpha$-transformation improves forecast accuracy of cause-specific life-table death counts compared with log-ratio-based CODA transformations. The forecasts suggest declines in proportions of deaths from major cardiovascular causes (myocardial infarction and other ischemic heart diseases (IHD)).

stat.AP↗

arXiv2024

Asymptotic Results for Penalized Quasi-Likelihood Estimation in Generalized Linear Mixed Models

Generalized Linear Mixed Models (GLMMs) are widely used for analysing clustered data. One well-established method of overcoming the integral in the marginal likelihood function for GLMMs is penalized quasi-likelihood (PQL) estimation, although to date there are few asymptotic distribution results relating to PQL estimation for GLMMs in the literature. In this paper, we establish large sample results for PQL estimators of the parameters and random effects in independent-cluster GLMMs, when both the number of clusters and the cluster sizes go to infinity. This is done under two distinct regimes: conditional on the random effects (essentially treating them as fixed effects) and unconditionally (treating the random effects as random). Under the conditional regime, we show the PQL estimators are asymptotically normal around the true fixed and random effects. Unconditionally, we prove that while the estimator of the fixed effects is asymptotically normally distributed, the correct asymptotic distribution of the so-called prediction gap of the random effects may in fact be a normal scale-mixture distribution under certain relative rates of growth. A simulation study is used to verify the finite sample performance of our theoretical results.

math.ST↗

arXiv2018

Bootstrapping F test for testing Random Effects in Linear Mixed Models

Recently Hui et al. (2018) use F tests for testing a subset of random effect, demonstrating its computational simplicity and exactness when the first two moment of the random effects are specified. We extended the investigation of the F test in the following two aspects: firstly, we examined the power of the F test under non-normality of the errors. Secondly, we consider bootstrap counterparts to the F test, which offer improvement for the cases with small cluster size or for the cases with non-normal errors.

stat.ME↗