An important disadvantage of the h-index is that typically it cannot take into account the specific field of research of a researcher. Usually sample point estimates of the average and median h-index values for the various fields are reported that are highly variable and dependent of the...

We propose a general class of models and a unified Bayesian inference methodology for flexibly estimating the density of a response variable conditional on a possibly high-dimensional set of covariates. Our model is a finite mixture of component models with covariate-dependent mixing weights....

We develop a method to estimate and assess uncertainty in the total fertility rate over time. Our estimates are based on multiple imperfect observations from different data sources including surveys and censuses. We take account of measurement error by decomposing it into bias and variance and...

The investigation of species abundance in rivers involves data which are inherently sequential and unlikely to be fully independent. To take these characteristics into account, a Bayesian hierarchical model within the class of hidden Markov models is proposed to map the distribution of...

Smooth mixtures, i.e. mixture models with covariate-dependent mixing weights, are very useful flexible models for conditional densities. Previous work shows that using too simple mixture components for modeling heteroscedastic and/or heavy tailed data can give a poor fit, even with a large...

The influence of maternal health problems on child’s worrying status is important in practice in terms of the intervention of maternal health problems early for the influence on child’s worrying status. Conventional methods apply symmetric prior distributions such as a normal...