@article{robledo2021,
   author = {Robledo, Kristy P. and Marschner, Ian C.},
   title = {A new algorithm for fitting semi-parametric variance regression models},
   journal = {Computational Statistics},
   volume = {36},
   number = {4},
   pages = {2313-2335},
   abstract = {Variance regression allows for heterogeneous variance, or heteroscedasticity, by incorporating a regression model into the variance. This paper uses a variant of the expectation–maximisation algorithm to develop a new method for fitting additive variance regression models that allow for regression in both the mean and the variance. The algorithm is easily extended to allow for B-spline bases, thus allowing for the incorporation of a semi-parametric model in both the mean and variance. Although there are existing methods to fit these types of models, this new algorithm provides a reliable alternative approach that is not susceptible to numerical instability that can arise in this constrained estimation context. We utilise the developed algorithm with a series of simulation studies and analyse illustrative data. Various simulation studies show that the algorithm can recover the true model for a variety of scenarios. We also study automatic selection of model complexity based on information-based criteria, and show that the Akaike information criterion is useful for choosing the optimal number of knots in a B-spline model. An R package is available for implementing these methods.},
   ISSN = {1613-9658},
   DOI = {10.1007/s00180-021-01067-6},
   url = {https://doi.org/10.1007/s00180-021-01067-6},
   year = {2021},
   type = {Journal Article}
}

