Flexible Regression and Smoothing: Using Gamlss in R

By Mikis D. Stasinopoulos, Robert A. Rigby, Gillian Z. Heller, Vlasios Voudouris, Fernanda De Bastiani. 2017

446 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/918f5668bfb0614ee416abfd0e3636f696802003

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Citations in the corpus

• A review of spline function procedures in R (2019, methodology): “For details refer to [31].”
• Unit-Gompertz Distribution with Applications (2019, unknown): “(iii) McDonald distribution: f (x;µ,σ , ν ,τ) = τνβxτα−1(1− xτ)β−1 Beta(α,β) [ν +(1− ν)xτ]α+β , where in (iii) we have α=µ 1 σ2 − 1 and β= α 1−µ µ (see, Stasinopoulos et al., 2017).”
• GAMLSS: A distributional regression approach (2018, methodology): “Statistical Modelling 2018; 18(3–4): 248–273 More details about both methodologies can be found in Stasinopoulos et al. (2017), Ch 13.”
• glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling (2017, methodology): “Among other generalizations, the Sichel and Delaporte distributions (Stasinopoulos et al., 2017) provide flexibility in skewness in addition to dispersion.”