Predicting healthcare costs for chronic diseases is challenging for actuaries, as these costs depend not only on traditional risk factors but also on patients’ self-perception and treatment behaviors. To address this complexity and the unobserved heterogeneity in cost data, we propose a dual-structured learning statistical framework that integrates covariate clustering into finite mixture of generalized linear models, effectively handling high-dimensional, sparse, and highly correlated covariates while capturing their effects on specific subgroups. Specifically, this framework is realized by imposing a penalty on the prior similarities among covariates, and we further propose an expectation-maximization-alternating direction method of multipliers (EM-ADMM) algorithm to address the complex optimization problem by combining EM with the ADMM. This paper validates the stability and effectiveness of the framework through simulation and empirical studies. The results show that our framework can leverage shared information among high-dimensional covariates to enhance fitting and prediction accuracy, while covariate clustering can also uncover the covariates’ network relationships, providing valuable insights into diabetic patients’ self-perception data.

In a subgroup analysis for an actuarial problem, the goal is for the investigator to classify the policyholders into unique groups, where the claims experience within each group are made as homogenous as possible. In this paper, we illustrate how the alternating direction method of multipliers (ADMM) approach for subgroup analysis can be modified so that it can be more easily incorporated into an insurance claims analysis. We present an approach to penalize adjacent coefficients only and show how the algorithm can be implemented for fast estimation of the parameters. We present three different cases of the model, depending on the level of dependence among the different coverage groups within the data. In addition, we provide an interpretation of the credibility problem using both random effects and fixed effects, where the fixed effects approach corresponds to the ADMM approach to subgroup analysis, while the random effects approach represents the classic Bayesian approach. In an empirical study, we demonstrate how these approaches can be applied to real data using the Wisconsin Local Government Property Insurance Fund data. Our results show that the presented approach to subgroup analysis could provide a classification of the policyholders that improves the prediction accuracy of the claim frequencies in case other classifying variables are unavailable in the data.

The 2019 Canadian Election Study (CES) consists of two separate surveys with campaign-period rolling cross-sections and post-election follow-ups. The parallel studies were conducted online and through a random-digit-dial (RDD) telephone survey. Both continue the long tradition of gathering information about the attitudes, opinions, preferences and behaviours of the Canadian public. The online survey, in particular, introduces some important innovations that open up the potential for exciting new research on subgroups in the electorate.