Standard regression approaches assume that some finite number of the response
distribution characteristics, such as location and scale, change as a (parametric or …

We review inference under models with nonparametric Bayesian (BNP) priors. The
discussion follows a set of examples for some common inference problems. The examples …

I congratulate the authors on an interesting article and thank the
Editorsfortheopportunitytodiscussthework. Themanuscript under discussion devises an …

Spatial survival analysis has received a great deal of attention over the last 20 years due to
the important role that geographical information can play in predicting survival. This paper …

Survival analysis has received a great deal of attention as a subfield of Bayesian
nonparametrics over the last 50 years. In particular, the fitting of survival models that allow …

Eliciting information from experts for use in constructing prior distributions for logistic
regression coefficients can be challenging. The task is especially difficult when the model …

We explore a particular fully parametric approach to quantile regression and show that this
approach can be very successful. Motivated by the provision of reference charts, we work in …

Major disasters such as wildfire, tornado, hurricane, tropical storm, and flooding cause
disruptions in infrastructure systems such as power and water supply, wastewater …

This paper deals with the issue of concept-drift in machine learning in the context of high
dimensional problems. In contrast to previous concept drift detection methods, this …

Survival models are used to analyze time-to-event data in a variety of disciplines.
Proportional hazard models provide interpretable parameter estimates, but proportional …