.png){kind=icon}
.png){kind=icon}
Return
Beta, simplex, and beta rectangular generalized additive partial linear models
.png){kind=icon}
PRE
AI
DOI：10.1080/03610918.2025.2562463.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
It is common to find studies where the aim is to model proportion or ratio data in terms of a set of covariates, where for some of them the relationship with the response variable is not clear and nonlinear. In these situations, it is suitable to consider extensions of the Generalized Additive Partial Linear Models (GAPLM) that deal with data on the unit interval. In this work, we developed a new class of GAPLM based on beta, beta rectangular (BR), and simplex distributions. We considered a combination of parametric and nonparametric predictors for the response mean and parametric predictor for the precision/dispersion parameter. Under the frequentist paradigm, we developed parameter estimation, we adapted some Information Criteria and proposed quantile residuals. We discussed some inferential tools as standard errors and hypothesis test based on Wald-type statistic. Also, we developed measures of global influence, based on Cook's distance and generalized leverage, and local influence. Then, our approach offers a flexible way to analyze data in (0,1) interval in several ways: response variable, regression structure, and model fit assessment. Simulation studies and real data analysis were conducted to illustrate the potential of the developed methodologies, showing their advantage over some usual methodologies.
Keywords:
Beta distribution
Beta rectangular distribution
Data in the unit interval
Generalized additive partial linear models
Simplex distribution
Journal
C
IF:
0.8
Papers:
213
Citations:
4.7K
Organization
No organization information available