Semiparametric estimation of outbreak regression

Marianne Frisén, Eva Andersson () and Kjell Pettersson ()
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Eva Andersson: Statistical Research Unit, Department of Economics, School of Business, Economics and Law, Göteborg University, Postal: Statistical Research Unit, Göteborg University, Box 640, SE 40530 GÖTEBORG
Kjell Pettersson: Statistical Research Unit, Department of Economics, School of Business, Economics and Law, Göteborg University, Postal: Statistical Research Unit, Göteborg University, Box 640, SE 40530 GÖTEBORG

No 2007:13, Research Reports from University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law

Abstract: A regression may be constant for small values of the independent variable (for example time), but then a monotonic increase starts. Such an “outbreak” regression is of interest for example in the study of the outbreak of an epidemic disease. We give the least square estimators for this outbreak regression without assumption of a parametric regression function. It is shown that the least squares estimators are also the maximum likelihood estimators for distributions in the regular exponential family such as the Gaussian or Poisson distribution. The approach is thus semiparametric. The method is applied to Swedish data on influenza, and the properties are demonstrated by a simulation study. The consistency of the estimator is proved.

Keywords: Constant Base-line; Monotonic change; Exponential family (search for similar items in EconPapers)
JEL-codes: C10 (search for similar items in EconPapers)
Pages: 16 pages
Date: 2008-02-04
New Economics Papers: this item is included in nep-ecm and nep-hea
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Published as Frisén, Marianne, Eva Andersson and Kjell Pettersson, 'Semiparametric estimation of outbreak regression' in Statistics, 2010, pages 107-117.

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