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Activity Number: 271
Type: Contributed
Date/Time: Monday, August 10, 2015 : 3:05 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #317851
Title: Semiparametric Mixed Beta Regression with Penalized Splines for Disease Severity
Author(s): Pedro Torres-Saavedra* and Raúl E. Macchiavelli
Companies: University of Puerto Rico at Mayaguez and University of Puerto Rico at Mayaguez
Keywords: beta regression ; semiparametric ; disease severity ; penalized splines ; longitudinal data ; glmm
Abstract:

The Severity Index (SI) of Black Sigatoka in banana, measured using the Stover-Gauhl scale, can be considered as a continuous variable in a 0-1 scale. Mixed beta regression has emerged as an appealing alternative to model this type of data. However, when the average and the plant-specific curves in longitudinal data do not follow a parametric form, semi-parametric methods are preferred. We propose a semiparametric mixed beta regression with smooth average curves and plant-specific curves to model disease progress curves. Parameters in the proposed model are estimated using a modified version of the newly proposed SAS macro HPGLIMMIX, a tool designed for high performance estimation in GLMM. The proposed semi-parametric method allows to model flexible shapes for disease progress curves, and can be used to compare treatments while taking into account the longitudinal and design structures of the data. We apply the proposed method to model the SI of Black Sigatoka in an experimental banana plantation in Isabela, Puerto Rico. The use of the proposed method yields very useful results that allow the plant pathologists and crop managers to understand, monitor and control diseases.


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