Many research fields increasingly involve analyzing data of a complex structure. Models
investigating the dependence of a response on a predictor have moved beyond the ordinary …

In the Design of Experiments, we seek to relate response variables to explanatory factors.
Response Surface methodology (RSM) approximates the relation between output variables …

We propose a multivariate functional response low‐rank regression model with possible
high‐dimensional functional responses and scalar covariates. By expanding the slope …

This work concerns spatial variable selection for scalar-on-image regression. We propose a
new class of Bayesian nonparametric models and develop an efficient posterior …

We propose new parametric frameworks of regression analysis with the conditional mode of
a bounded response as the focal point of interest. Covariate effects estimation and …

We extend the notion of an influence or hat matrix to regression with functional responses
and scalar predictors. For responses depending linearly on a set of predictors, our definition …

Regression models with a scalar response and a functional predictor have been extensively
studied. One approach is to approximate the functional predictor using eigenfunction …

We consider a logistic regression model for a binary response where part of its covariates
are subject‐specific random intercepts and slopes from a large number of longitudinal …

Linear and generalized linear scalar-on-function modeling have been commonly used to
understand the relationship between a scalar response variable (eg continuous, binary …

We consider functional linear regression models with a functional response and multiple
functional predictors, with the goal of finding the best finite-dimensional approximation to the …