In this paper we derive general formulae for second-order biases of maximum-likelihood estimates in a class of symmetric nonlinear regression models. This class of models is commonly used for the analysis of data containing extreme or outlying observations in samples from a supposedly normal distrib
β¦ LIBER β¦
Maximum likelihood estimator in a two-phase nonlinear random regression model
β Scribed by Ciuperca, Gabriela
- Book ID
- 115443935
- Publisher
- Oldenbourg Wissenschaftsverlag
- Year
- 2004
- Weight
- 135 KB
- Volume
- 22
- Category
- Article
- ISSN
- 0721-2631
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In this note we discuss the breakdown behavior of the maximum likelihood (ML) estimator in the logistic regression model. We formally prove that the ML-estimator never explodes to inΓΏnity, but rather breaks down to zero when adding severe outliers to a data set. An example conΓΏrms this behavior.