Likelihood inference of nonlinear models based on a class of flexible skewed distributions
type: Item
instance of: scholarly article
title: Likelihood inference of nonlinear models based on a class of flexible skewed distributions
zbMATH Open document ID: 1474.60029
DOI: 10.1155/2014/542985
author: Qianying Zeng
author: Xuedong Chen
author: Qiankun Song
published in: Abstract and Applied Analysis
publication date: 2019-02-14T00:00:00Z
review text: Summary: This paper deals with the issue of the likelihood inference for nonlinear models with a flexible skew-t-normal (FSTN) distribution, which is proposed within a general framework of flexible skew-symmetric (FSS) distributions by combining with skew-t-normal (STN) distribution. In comparison with the common skewed distributions such as skew normal (SN), and skew-t (ST) as well as scale mixtures of skew normal (SMSN), the FSTN distribution can accommodate more flexibility and robustness in the presence of skewed, heavy-tailed, especially multimodal outcomes. However, for this distribution, a usual approach of maximum likelihood estimates based on EM algorithm becomes unavailable and an alternative way is to return to the original Newton-Raphson type method. In order to improve the estimation as well as the way for confidence estimation and hypothesis test for the parameters of interest, a modified Newton-Raphson iterative algorithm is presented in this paper, based on profile likelihood for nonlinear regression models with FSTN distribution, and, then, the confidence interval and hypothesis test are also developed. Furthermore, a real example and simulation are conducted to demonstrate the usefulness and the superiority of our approach.
Mathematics Subject Classification ID: 60E05
Mathematics Subject Classification ID: 62F03
Mathematics Subject Classification ID: 62F10
zbMATH DE Number: 7022591
Wikidata QID: Q59039327
describes a project that uses: sn
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/542985
OpenAlex ID: W2122122423
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description: scientific article; zbMATH DE number 7022591