LIKELIHOOD BASED INFERENCE FOR SKEW-NORMAL INDEPENDENT LINEAR MIXED MODELS

By Victor Hugo Lachos, Pulak Ghosh, Reinaldo Boris Arellano-Valle. 2010

222 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5525f491d7ddfe8bc8059a0e293d083fa8c1a50f

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Citations in the corpus

Multivariate measurement error models based on scale mixtures of the skew–normal distribution (2010, background): “[14], it follows that the conditional moments are given by”
Linear censored regression models with scale mixtures of normal distributions (2015, methodology): “For instance, Lange et al. (1989) discussed the use of the Student-t distribution in multivariate regression models.”
Robust linear mixed models with skew-normal independent distributions from a Bayesian perspective (2009, unknown): “( 7 ) The maximum likelihood estimates (MLEs) of h can be obtained by direct maximization of (7) or alternatively by using the EMtype algorithm proposed in Lachos et al. (2009a), while inference for the parameters will be based on the asymptotic normality of the MLEs (Cox and Hinkley, 1974).”
Robust mixture regression modeling based on scale mixtures of skew-normal distributions (2015, methodology): “A random variable Y with a pdf as in (1) has a marginal stochastic representation, see Lachos et al. (2010), given by Y d= μ + ΔT + κ1/2(U )γ T1, (2) where d= means “equal in distribution”, Δ = σδ, δ = λ√ 1+λ2 , γ 2 = σ 2 − Δ2, T1 = κ1/2(U )|T0|, |T0| denotes the absolute value of T0, U ∼ H(·; ν),…”
Likelihood-based Inference For Mixed-effects Models With Censored Response Using The Multivariate-t Distribution (2013, background): “Recently, Lachos, Ghosh, and Arellano-Valle (2010) (see also Ho and Lin (2010)) proposed a remedy to accommodate skewness and heavy- tailedness simultaneously, using scale mixtures of skew-normal (SMSN) distribu- tions.”