Search Results

You are looking at 1 - 1 of 1 items for

  • Author or Editor: Graciela González-Farías x
Clear All Modify Search

Abstract  

Motivated by results in Rotnitzky et al. (2000), a family of parametrizations of the location-scale skew-normal model is introduced, and it is shown that, under each member of this class, the hypothesis H0: λ = 0 is invariant, where λ is the asymmetry parameter. Using the trace of the inverse variance matrix associated to a generalized gradient as a selection index, a subclass of optimal parametrizations is identified, and it is proved that a slight variant of Azzalini’s centred parametrization is optimal. Next, via an arbitrary optimal parametrization, a simple derivation of the limit behavior of maximum likelihood estimators is given under H0, and the asymptotic distribution of the corresponding likelihood ratio statistic for this composite hypothesis is determined.

Restricted access