Generalized additive models for location, scale and shape - Discussion

By B. Rigby, Mikis D. Stasinopoulos, C. Akantziliotou. 2005

522 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0eba8ee81c91f45af411670ffbd14adf377673dd

Citations in the corpus

• Bayesian Structured Additive Distributional Regression (2013, unknown): “Such an approach is given by the class of generalised additive models for location, scale and shape (GAMLSS, [Rigby and Stasinopoulos, 2005]) in which all parameters of a potentially complex response distribution are related to additive regression predictors in the spirit of GAMs [Ruppert et al.,…”
• Bayesian structured additive distributional regression with an application to regional income inequality in Germany (2015, unknown): “Distributional regression is closely related to generalised additive models for location, scale and shape (GAMLSS) as suggested by Rigby and Stasinopoulos (2005).”
• Negative Deceptive Opinion Spam (2013, unknown): “(6)We use the R package GAMLSS (Rigby and Stasinopoulos, 2005) to fit a log-normal distribution (left truncated at 150 characters) to the lengths of the deceptive reviews.”
• Distributional regression forests for probabilistic precipitation forecasting in complex terrain (2018, unknown): “The GAMLSS framework (Rigby and Stasinopoulos 2005) provides a wide range of such distributional families with parameterizations corresponding to location, scale, and shape.”
• Bayesian Generalized Additive Models for Location, Scale, and Shape for Zero-Inflated and Overdispersed Count Data (2015, unknown): “Both specifications can be embedded in the general class of generalized additive models for location, scale, and shape proposed by Rigby and Stasinopoulos (2005).”
• A new family of distributions: the Kumaraswamy odd log-logistic, properties and applications (2014, unknown): “Numerical maximization of (11.1) is performed by using the RS method (Rigby and Stasinopoulos, 2005) which is available in the gamlss package (R Development Core Team, 2013), SAS (Proc NLMixed) or the Ox program (sub-routine MaxBFGS) (see, Doornik, 2007) or by solving the nonlinear likelihood…”
• Scale-Dependent Priors for Variance Parameters in Structured Additive Distributional Regression (2016, unknown): “…where further moments or general shape parameters of the conditional response distribution can be related to a predictor (similar as in generalised additive models for location, scale and shape, Rigby and Stasinopoulos, 2005) which is currently (to the best of our knowledge) not possible with INLA.”
• A general class of zero-or-one inflated beta regression models (2011, unknown): “The zero-or-one inflated beta distribution has been incorporated in the GAMLSS framework (Rigby & Stasinopoulos, 2005); see Ospina (2006).”
• Nonlife Ratemaking and Risk Management with Bayesian Additive Models for Location, Scale and Shape (2013, unknown): “More details on how the penalized log likelihood is maximized are given in Rigby and Stasinopoulos (2005).”
• Flexible Imputation of Missing Data (2012, unknown): “More generally, the transformation could be made to depend on known covariates like age and sex, for example as done in the LMS model (Cole and Green, 1992) or the GAMLSS model (Rigby and Stasinopoulos, 2005).”