Abstract
In this paper we consider classes of statistical models that are natural generalizations of generalized linear models. Generalized linear models cover a very broad class of classical statistical models including linear regression, ANOVA, logit, and probit models. An important element of generalized linear models is that they contain parametric components of which the influence has to be determined by the experimentator. Here we describe some lines of thought and research relaxing the parametric structure of these components.
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Härdle, W.K., Turlach, B.A. (1992). Nonparametric Approaches to Generalized Linear Models. In: Fahrmeir, L., Francis, B., Gilchrist, R., Tutz, G. (eds) Advances in GLIM and Statistical Modelling. Lecture Notes in Statistics, vol 78. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2952-0_33
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DOI: https://doi.org/10.1007/978-1-4612-2952-0_33
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