Journal of Data Science

Density deconvolution is a fundamental problem in measurement error settings, arising when the goal is to recover the distribution of unobserved latent variables based on their error-contaminated proxies. An important application arises in nutritional epidemiology, where the objective is to estimate the long-term average intake of nutritional components using 24-hour dietary recall data. This task involves complex multivariate models that account for conditionally heteroscedastic measurement errors, zero-inflated proxies for episodically consumed dietary components, diverse marginal distribution shapes across components, etc. While flexible Bayesian hierarchical methods, coupled with Markov chain Monte Carlo techniques, have been increasingly successful in recent years to enable deconvolution under such intricate but realistic scenarios, the development of rapid and user-friendly software implementations has lagged behind. The $\mathsf{R}$ package BayesDecon has been developed to address this challenge, providing a fast and accessible implementation of flexible Bayesian deconvolution methods for practitioners working with measurement errors. In the process, we have introduced substantial improvements to several aspects of the original models and algorithms, and have also added options to fit simpler parametric models. Although illustrated on challenges in nutritional epidemiology, the package addresses general deconvolution problems and hence is broadly applicable to other domains as well.

Reinforcement Learning (RL) is a powerful framework for sequential decision-making, enabling agents to optimize actions through interaction with their environment. While widely studied in computer science, statisticians have advanced RL by addressing challenges like uncertainty quantification, sample efficiency, and interpretability. These contributions are particularly impactful in healthcare, where RL complements Dynamic Treatment Regimes (DTRs), optimizing personalized medicine by tailoring treatments to individuals based on evolving characteristics. This paper serves as both a tutorial for statisticians new to RL and a review of its integration with statistical methodologies. It introduces foundational RL concepts, classical algorithms, and Q-learning variants, and highlights how statistical perspectives, especially causal inference, address challenges in DTRs. By bridging RL and statistical perspectives, the paper highlights opportunities to enhance decision-making in high-stakes domains like healthcare.

Abstract: Of interest in this paper is the development of a model that uses inverse sampling of binary data that is subject to false-positive misclassification in an effort to estimate a proportion. From this model, both the proportion of success and false positive misclassification rate may be estimated. Also, three first-order likelihood based confidence intervals for the proportion of success are mathematically derived and studied via a Monte Carlo simulation. The simulation results indicate that the score and likelihood ratio intervals are generally preferable over the Wald interval. Lastly, the model is applied to a medical data set.

Abstract: Panel data transcends cross-sectional data by tapping pooled inter- and intra-individual differences, along with between and within individual variation separately. In the present study these micro variations in ill-being are predicted by psychological indicators constructed from the British Household Panel Survey (BHPS). Panel regression effects are corrected for errors-in-variables, which attenuate slopes estimated by traditional panel regressions. These corrections reveal that unhappiness and life dissatisfaction are distinct variables that have different psychological causations.

Abstract: We derive three likelihood-based confidence intervals for the risk ratio of two proportion parameters using a double sampling scheme for mis classified binomial data. The risk ratio is also known as the relative risk. We obtain closed-form maximum likelihood estimators of the model parameters by maximizing the full-likelihood function. Moreover, we develop three confidence intervals: a naive Wald interval, a modified Wald interval, and a Fieller-type interval. We apply the three confidence intervals to cervical cancer data. Finally, we perform two Monte Carlo simulation studies to assess and compare the coverage probabilities and average lengths of the three interval estimators. Unlike the other two interval estimators, the modified Wald interval always produces close-to-nominal confidence intervals for the various simulation scenarios examined here. Hence, the modified Wald confidence interval is preferred in practice.