Multivariate Response in More Than One Sample

1977 ◽  
Vol 26 (1) ◽  
pp. 1 ◽  
Author(s):  
Douglas Liddell
Keyword(s):  
Multivariate Response

scholarly journals Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

2017 ◽  
Vol 49 (3) ◽  
pp. 228-243 ◽  
Author(s):  
Edward V. Thomas ◽  
John R. Lewis ◽  
Christine M. Anderson-Cook ◽  
Tom Burr ◽  
Michael S. Hamada
Keyword(s):  
Multivariate Response ◽  
Inverse Prediction

Multivariate response optimization of Pb(II) extraction from wastewater using Box-Behnken design

2022 ◽  
Author(s):  
Vijayalakshmi Rajadurai ◽  
Brinda Lakshmi Anguraj ◽  
Muthuvel Inbasekaran ◽  
Hemalatha Manivannan
Keyword(s):  
Box Behnken Design ◽  
Multivariate Response ◽  
Response Optimization

A program for individual and population optimal design for univariate and multivariate response pharmacokinetic–pharmacodynamic models

2007 ◽  
Vol 86 (1) ◽  
pp. 51-61 ◽  
Author(s):  
Ivelina Gueorguieva ◽  
Kayode Ogungbenro ◽  
Gordon Graham ◽  
Sophie Glatt ◽  
Leon Aarons
Keyword(s):  
Optimal Design ◽  
Multivariate Response ◽  
Population Optimal Design

scholarly journals A joint regression modeling framework for analyzing bivariate binary data in R

2017 ◽  
Vol 5 (1) ◽  
pp. 268-294 ◽  
Author(s):  
Giampiero Marra ◽  
Rosalba Radice
Keyword(s):  
Binary Data ◽  
Regression Models ◽  
Joint Modeling ◽  
Data Sets ◽  
Modeling Framework ◽  
Flexible Joint ◽  
Sampling Schemes ◽  
Multivariate Response ◽  
Interpretable Models ◽  
Non Gaussian

Abstract We discuss some of the features of the R add-on package GJRM which implements a flexible joint modeling framework for fitting a number of multivariate response regression models under various sampling schemes. In particular,we focus on the case inwhich the user wishes to fit bivariate binary regression models in the presence of several forms of selection bias. The framework allows for Gaussian and non-Gaussian dependencies through the use of copulae, and for the association and mean parameters to depend on flexible functions of covariates. We describe some of the methodological details underpinning the bivariate binary models implemented in the package and illustrate them by fitting interpretable models of different complexity on three data-sets.

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