Alternative statistical modeling for radical prostatectomy data

Article Statistics & Probability

The semiparametric regression model for bimodal data with different penalized smoothers applied to climatology, ethanol and air quality data

J. C. S. Vasconcelos et al.

Summary: This study proposes three flexible regression models for bimodal data, namely additive regression, additive partial regression, and semiparametric regression. The precision of the models is verified through Monte Carlo simulations.

JOURNAL OF APPLIED STATISTICS (2022)

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Article Statistics & Probability

Generalized log-gamma additive partial linear models with P-spline smoothing

Carlos A. Cardozo et al.

Summary: This paper introduces the additive partial linear models with generalized log-gamma errors and P-spline smoothing for uncensored data. The models are able to fit various continuous asymmetric distributions, including the normal distribution, using a semiparametric approach. A joint iterative process is derived to estimate the regression coefficients, and diagnostic procedures are proposed for residual analysis and sensitivity studies. Simulation studies and the analysis of a real data set demonstrate the effectiveness of the proposed methodology.

STATISTICAL PAPERS (2022)

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Article Statistics & Probability

Generalized symmetrical partial linear model

Julio Cezar Souza Vasconcelos et al.

Summary: The study presents a new model called generalized symmetrical partial linear model, which is based on the theory of generalized linear models and symmetrical distributions. The model considers replacing traditional linear predictors with more general predictors in a non-parametric fashion. The backfitting algorithm is used for parameter estimation and simulations and real data analysis are conducted to assess model behavior.

JOURNAL OF APPLIED STATISTICS (2021)

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Article Engineering, Multidisciplinary

A novel generalized odd log-logistic Maxwell-based regression with application to microbiology

Fabio Prataviera et al.

Summary: This study introduces new regression methods for real data with common characteristics such as bimodality, heteroskedasticity, zero-inflation, and nonlinear effects, utilizing maximum likelihood estimation for parameter estimation. Through simulations with different fixed parameters, sample sizes, and percentages of zeros, the behavior of the estimators is assessed. Quantile residuals are defined for evaluating regression assumptions, with the method illustrated through an experiment on soil microbiology in a sugarcane field.

APPLIED MATHEMATICAL MODELLING (2021)

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Article Health Care Sciences & Services