Transform Both Sides Model: A Parametric Approach

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Authors Adriano Polpo, Cassio Polpo de Campos, D. Sinha, S. Lipsitz, Jeremy Lin
Journal/Conference Name Computational Statistics & Data Analysis
Paper Category
Paper Abstract A parametric regression model for right-censored data with a log-linear median regression function and a transformation in both response and regression parts, named parametric Transform-Both-Sides (TBS) model, is presented. The TBS model has a parameter that handles data asymmetry while allowing various different distributions for the error, as long as they are unimodal symmetric distributions centered at zero. The discussion is focused on the estimation procedure with five important error distributions (normal, double-exponential, Student's t, Cauchy and logistic) and presents properties, associated functions (that is, survival and hazard functions) and estimation methods based on maximum likelihood and on the Bayesian paradigm. These procedures are implemented in TBSSurvival, an open-source fully documented R package. The use of the package is illustrated and the performance of the model is analyzed using both simulated and real data sets.
Date of publication 2014
Code Programming Language R
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