Multi-level nonlinear mixed effects (ML-NLME) models have received a great deal of attention in recent years because of the flexibility they offer in handling the repeated-measures data arising from various disciplines. In this study, we propose both maximum likelihood and restricted maximum...

An algorithm that computes nonparametric maximum likelihood estimates of a mixing distribution for a logistic regression model containing random intercepts and slopes is proposed. The algorithm identifies mixing distribution support points as the maxima of the gradient function using a direct...

Examination of multiple conditional quantile functions provides a comprehensive view of the relationship between the response and covariates. In situations where quantile slope coefficients share some common features, estimation efficiency and model interpretability can be improved by utilizing...

An efficient estimating equations procedure is developed for performing variable selection and defining semiparametric efficient estimates simultaneously for the heteroscedastic partially linear single-index model. The estimating equations are proposed based on the smooth threshold estimating...

Generalized fiducial inference is closely related to the Dempster–Shafer theory of belief functions. It is a general methodology for constructing a distribution on a (possibly vector-valued) model parameter without the use of any prior distribution. The resulting distribution is called the...

Stochastic dominance, which is based on the comparison of distribution functions, is one of the most popular preference measures. However, its use is limited to the case where the goal is to compare pairs of distribution functions, whereas in many cases it is interesting to compare sets of...

A non-randomized triangular design has been shown to be more efficient than the conventional random response model in estimating the prevalence of sensitive attributes in surveys. Since most surveys focus on estimation, herein we derive sample size formulas for estimation of prevalence and a...

Composite endpoints are often used as primary efficacy endpoints, particularly in the field of oncology and cardiology. These endpoints combine several time-to-event variables of interest within a single time-to-first-event variable. Thereby, it is intended to enlarge the expected effect size...

Model selection and model averaging are two important techniques to obtain practical and useful models in applied research. However, it is now well-known that many complex issues arise, especially in the context of model selection, when the stochastic nature of the selection process is ignored...

In diagnostic studies, both sensitivity and specificity depend on cut-off point and they are well-known measures for diagnostic accuracy. The diagnostic cut-off point is mostly unknown and needs to be determined by some optimization criteria out of which the one based on the Youden index has...