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• W2984409259 abstract "The heteroscedastic nonlinear regression model (HNLM) is an important tool in data modeling. In this paper we propose a HNLM considering skew scale mixtures of normal (SSMN) distributions, which allows fitting asymmetric and heavy-tailed data simultaneously. Maximum likelihood (ML) estimation is performed via the expectation-maximization (EM) algorithm. The observed information matrix is derived analytically to account for standard errors. In addition, diagnostic analysis is developed using case-deletion measures and the local influence approach. A simulation study is developed to verify the empirical distribution of the likelihood ratio statistic, the power of the homogeneity of variances test and a study for misspecification of the structure function. The method proposed is also illustrated by analyzing a real dataset." @default.
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• W2984409259 date "2019-11-11" @default.
• W2984409259 modified "2023-09-24" @default.
• W2984409259 title "Inference and diagnostics for heteroscedastic nonlinear regression models under skew scale mixtures of normal distributions" @default.
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• W2984409259 doi "https://doi.org/10.1080/02664763.2019.1691158" @default.
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