@article{Nakamura_Ramires_Righetto_Silva_Konrath_2022, title={Using the Box-Cox family of distributions to model censored data:a distributional regression approach}, volume={40}, url={https://biometria.ufla.br/index.php/BBJ/article/view/625}, DOI={10.28951/bjb.v40i4.625}, abstractNote={<p><span class="fontstyle0">The study of the expected time until an event of interest is a recurring topic in different fields, such as medical, economics and engineering. The Kaplan-Meier method and the Cox proportional hazards model are the most used methodologies to deal with such kind of data. Nevertheless, in recent years, the generalised additive models for location, scale and shape (GAMLSS) models – which can be seen as distributional regression and/or beyond the mean regression models – have been standing out as a result of its highly flexibility and ability to fit complex data. GAMLSS are a class of semi-parametric regression models, in the sense that they assume a distribution for the response variable, and any and all of its parameters can be modelled as linear and/or non-linear functions of a set of explanatory variables. In this paper, we present the Box-Cox family of distributions under the distributional regression framework as a solid alternative to model censored data.</span> </p>}, number={4}, journal={Brazilian Journal of Biometrics}, author={Nakamura, Luiz R. and Ramires, Thiago and Righetto, Ana and Silva, Viviane and Konrath, Andréa}, year={2022}, month={Dec.}, pages={407–414} }