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• W1522036537 abstract "A generalized linear point process is specified in terms of an intensity that depends upon a linear predictor process through a fixed non-linear function. We present a framework where the linear predictor is parametrized by a Banach space and give results on Gateaux differentiability of the log-likelihood. Of particular interest is when the intensity is expressed in terms of a linear filter parametrized by a Sobolev space. Using that the Sobolev spaces are reproducing kernel Hilbert spaces we derive results on the representation of the penalized maximum likelihood estimator in a special case and the gradient of the negative log-likelihood in general. The latter is used to develop a descent algorithm in the Sobolev space. We conclude the paper by extensions to multivariate and additive model specifications. The methods are implemented in the R-package ppstat." @default.
• W1522036537 created "2016-06-24" @default.
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• W1522036537 date "2010-03-03" @default.
• W1522036537 modified "2023-09-27" @default.
• W1522036537 title "Penalized maximum likelihood estimation for generalized linear point processes" @default.
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