Hidden Markov models—most often abbreviated to the acronym “HMMs”—are one of the
most successful statistical modelling ideas that have came up in the last forty years: the use …

In this self-contained survey/review paper, we systematically investigate the roots of
Bayesian filtering as well as its rich leaves in the literature. Stochastic filtering theory is …

Missing Data in Clinical Studies provides a comprehensive account of the problems arising
when data from clinical and related studies are incomplete, and presents the reader with …

When I was a graduate student more than twenty five years ago, I was struggling to read
many statistical research papers. This is particularly true at the time when I had passed my …

Researchers often face the problem of how to address missing data. Multiple imputation is a
popular approach, with multiple imputation by chained equations (MICE) being among the …

This article examines the problem of estimation in a quantile regression model when
observations are missing at random under independent and nonidentically distributed …

Hospital bed demand forecast is a first-order concern for public health action to avoid
healthcare systems to be overwhelmed. Predictions are usually performed by estimating …

Self-tracking has become part of a modern lifestyle; wearables and smartphones support
self-tracking in an easy fashion and change our behavior such as in the health sphere. The …

There is a large and growing body of literature on estimating equation (EE) as an estimation
approach. One basic property of EE that has been universally adopted in practice is that of …

Cure models are a class of time-to-event models where a proportion of individuals will never
experience the event of interest. The lifetimes of these so-called cured individuals are …