ssym: Fitting Semi-Parametric log-Symmetric Regression Models. Set of tools to fit a semi-parametric regression model suitable for analysis of data sets in which the response variable is continuous, strictly positive, asymmetric and possibly, censored. Under this setup, both the median and the skewness of the response variable distribution are explicitly modeled by using semi-parametric functions, whose non-parametric components may be approximated by natural cubic splines or P-splines. Supported distributions for the model error include log-normal, log-Student-t, log-power-exponential, log-hyperbolic, log-contaminated-normal, log-slash, Birnbaum-Saunders and Birnbaum-Saunders-t distributions.
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References in zbMATH (referenced in 2 articles )
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- Vanegas, Luis Hernando; Paula, Gilberto A.: Log-symmetric regression models under the presence of non-informative left- or right-censored observations (2017)
- Vanegas, Luis Hernando; Paula, Gilberto A.: A semiparametric approach for joint modeling of median and skewness (2015)