Optimum Designs for a Multiresponse Regression Model

Heteroscedastic T-Optimum Designs for Multiresponse Dynamic Models

Regression Trees for Longitudinal and Multiresponse Data

Previous algorithms for constructing regression tree models for longitudinal and multiresponse data have mostly followed the CART approach. Consequently, they inherit the same selection biases and computational difficulties as CART. We propose an alternative, based on the GUIDE approach, that treats each longitudinal data series as a curve and uses chi-squared tests of the residual curve patter...

Model-Robust Designs for Quantile Regression

We give methods for the construction of designs for regression models, when the purpose of the investigation is the estimation of the conditional quantile function, and the estimation method is quantile regression. The designs are robust against misspecified response functions, and against unanticipated heteroscedasticity. The methods are illustrated by example, and in a case study in which the...

Shrinkage simplex-centroid designs for a quadratic mixture model

A simplex-centroid design for q mixture components comprises of all possible subsets of the q components, present in equal proportions. The design does not contain full mixture blends except the overall centroid. In real-life situations, all mixture blends comprise of at least a minimum proportion of each component. Here, we introduce simplex-centroid designs which contain complete blend...

Multiresponse Designs and Surrogate Endpoints in Clinical Trials

Incorporating plausible surrogate variates, a general class of incomplete multiresponse designs for clinical trials is considered. In this context, the basic differences between the conventional linear models and survival analysis models are discussed thoroughly. Along with some parametric and semi-parametric procedures, suitable nonparametric ones are considered, and the related asymptotic the...