Forthcoming and Online First Articles

International Journal of Knowledge Engineering and Soft Data Paradigms

International Journal of Knowledge Engineering and Soft Data Paradigms (IJKESDP)

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International Journal of Knowledge Engineering and Soft Data Paradigms (One paper in press)

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  • Stable Estimation of the Slant Parameter in Skew Normal Regression via an MM Algorithm and Ridge Shrinkage   Order a copy of this article
    by Mineaki Ohishi, Hirokazu Yanagihara, Hirofumi Wakaki, Masahiko Ono 
    Abstract: This paper deals with a skew normal linear regression model in which the error is distributed according to a skew normal distribution. The skew normal distribution has three parameters: a location parameter, a scale parameter, and a slant parameter, and their maximum likelihood estimators (MLEs) can be obtained with, e.g., an R package sn. Although it is known that an MLE has good properties, estimation via likelihood maximization causes the estimate of the slant parameter to be particularly unstable. To improve the stability of the slant parameter estimation, we employ a maximum penalized-likelihood estimation method based on ridge shrinkage and derive a new algorithm based on MM principle to obtain the maximum penalized-likelihood estimates.
    Keywords: MM Algorithm; Ridge Regression; Skew Normal Distribution.
    DOI: 10.1504/IJKESDP.2023.10057725
     

 

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