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Semiparametric estimation of outbreak regression

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Author Info
Frisén, Marianne () (Statistical Research Unit, Department of Economics, School of Business, Economics and Law, Göteborg University)
Andersson, Eva () (Statistical Research Unit, Department of Economics, School of Business, Economics and Law, Göteborg University)
Pettersson, Kjell () (Statistical Research Unit, Department of Economics, School of Business, Economics and Law, Göteborg University)

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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.

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File URL: http://hdl.handle.net/2077/10526
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Publisher Info
Paper provided by Statistical Research Unit, Department of Economics, School of Business, Economics and Law, Göteborg University in its series Research Reports with number 2007:13.

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Length: 16 pages
Date of creation: 04 Feb 2008
Date of revision:
Handle: RePEc:hhs:gunsru:2007_013

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Postal: Statistical Research Unit, Göteborg University, Box 640, SE 40530 GÖTEBORG
Web page: http://www.statistics.gu.se/

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Related research
Keywords: Constant Base-line; Monotonic change; Exponential family;

Find related papers by JEL classification:
C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - General

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