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Diagnostics on nonlinear model with scale mixtures of skew-normal and first-order autoregressive errors

Lookup NU author(s): Dr Jian Shi


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In this work, we develop some diagnostics for nonlinear regression model with scale mixtures of skew-normal (SMSN) and first-order autoregressive errors. The SMSN distribution class covers symmetric as well as asymmetric and heavy-tailed distributions, which offers a more flexible framework for modelling. Maximum-likelihood (ML) estimates are computed via an expectation-maximization-type algorithm. Local influence diagnostics and score test for the correlation are also derived. The performances of the ML estimates and the test statistic are investigated through Monte Carlo simulations. Finally, a real data set is used to illustrate our diagnostic methods.

Publication metadata

Author(s): Cao CZ, Lin JG, Shi JQ

Publication type: Article

Publication status: Published

Journal: Statistics

Year: 2014

Volume: 48

Issue: 5

Pages: 1033-1047

Print publication date: 01/09/2014

Online publication date: 30/05/2013

Acceptance date: 21/11/2012

ISSN (print): 0233-1888

ISSN (electronic): 1029-4910

Publisher: Taylor & Francis


DOI: 10.1080/02331888.2013.800072


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Funder referenceFunder name
11171065National Science Foundation of China
BK2012459Natural Science Foundation of Jiangsu Province of China