On bias reduction estimators of skew-normal and skew-t distributions

Maghami, Mohammad Mahdi; Bahrami, Mohammad; Sajadi, Farkhondeh Alsadat

doi:10.1080/02664763.2019.1710114

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Cited by 2 publications

(2 citation statements)

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“…Many authors claim that the normal distribution is the most important probability distribution in statistics [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ]. This is due to three reasons: 1) Many phenomena in nature are distributed naturally [ 18 ]; 2) Many statistical analyses require the application of normally distributed data; and 3) Lastly, many statistical applications, such as statistical quality control, need normally distributed data to be applied.…”

Section: Introductionmentioning

confidence: 99%

Ultra-fine transformation of data for normality

Hamasha

Ali

Hamasha

et al. 2022

Heliyon

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Section: Introductionmentioning

confidence: 99%

Ultra-fine transformation of data for normality

Hamasha

Ali

Hamasha

et al. 2022

Heliyon

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“…A penalized MLE approach for all the parameters, including the skewness parameter, is presented by [5]. Bias-reduction regimes were proposed by [26].…”

Section: Introductionmentioning

confidence: 99%

Skewed probit regression -- Identifiability, contraction and reformulation

van Niekerk,

Rue

2020

Preprint

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Skewed probit regression is but one example of a statistical model that generalizes a simpler model, like probit regression. All skew-symmetric distributions and link functions arise from symmetric distributions by incorporating a skewness parameter through some skewing mechanism. In this work we address some fundamental issues in skewed probit regression, and more genreally skew-symmetric distributions or skew-symmetric link functions.We address the issue of identifiability of the skewed probit model parameters by reformulating the intercept from first principles. A new standardization of the skew link function is given to provide and anchored interpretation of the inference. Possible skewness parameters are investigated and the penalizing complexity priors of these are derived. This prior is invariant under reparameterization of the skewness parameter and quantifies the contraction of the skewed probit model to the probit model.The proposed results are available in the R-INLA package and we illustrate the use and effects of this work using simulated data, and wellknown datasets using the link as well as the likelihood.

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On bias reduction estimators of skew-normal and skew-t distributions

Cited by 2 publications

References 17 publications

Ultra-fine transformation of data for normality

Ultra-fine transformation of data for normality

Skewed probit regression -- Identifiability, contraction and reformulation

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