Journal
INTERNATIONAL JOURNAL OF BIOSTATISTICS
Volume 17, Issue 1, Pages 39-53Publisher
WALTER DE GRUYTER GMBH
DOI: 10.1515/ijb-2019-0163
Keywords
correlated data; estimating functions; multiple continuous bounded outcomes; NORTA algorithm; simulation study; unit interval
Ask authors/readers for more resources
The study introduces a multivariate regression model for handling multiple continuous bounded data without the need for a multivariate probability distribution. Simulation studies show that the proposed quasi-beta regression model provides better fit and allows for measuring the correlation between response variables compared to its univariate counterparts.
We propose a multivariate regression model to deal with multiple continuous bounded data. The proposed model is based on second-moment assumptions, only. We adopted the quasi-score and Pearson estimating functions for estimation of the regression and dispersion parameters, respectively. Thus, the proposed approach does not require a multivariate probability distribution for the variable response vector. The multivariate quasi-beta regression model can easily handle multiple continuous bounded outcomes taking into account the correlation between the response variables. Furthermore, the model allows us to analyze continuous bounded data on the interval [0, 1], including zeros and/or ones. Simulation studies were conducted to investigate the behavior of the NORmal To Anything (NORTA) algorithm and to check the properties of the estimating function estimators to deal with multiple correlated response variables generated from marginal beta distributions. The model was motivated by a data set concerning the body fat percentage, which was measured at five regions of the body and represent the response variables. We analyze each response variable separately and compare it with the fit of the multivariate proposed model. The multivariate quasi-beta regression model provides better fit than its univariate counterparts, as well as allows us to measure the correlation between response variables. Finally, we adapted diagnostic tools to the proposed model. In the supplementary material, we provide the data set and R code.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available