A transitional non-parametric maximum pseudo-likelihood estimator for disease mapping

2003 ◽  
Vol 41 (3-4) ◽  
pp. 617-629 ◽  
Author(s):  
A. Biggeri ◽  
E. Dreassi ◽  
C. Lagazio ◽  
D. Böhning
Keyword(s):  
Disease Mapping ◽  
Likelihood Estimator ◽  
Pseudo Likelihood ◽  
Non Parametric

Non-parametric maximum likelihood estimators for disease mapping

2000 ◽  
Vol 19 (17-18) ◽  
pp. 2539-2554 ◽  
Author(s):  
Annibale Biggeri ◽  
Marco Marchi ◽  
Corrado Lagazio ◽  
Marco Martuzzi ◽  
Dankmar Böhning
Keyword(s):  
Maximum Likelihood ◽  
Disease Mapping ◽  
Maximum Likelihood Estimators ◽  
Non Parametric

Non-parametric estimation of the directional distribution of stationary line and fibre processes

2001 ◽  
Vol 33 (1) ◽  
pp. 6-24 ◽  
Author(s):  
Markus Kiderlen
Keyword(s):  
Input Data ◽  
Dimensional Space ◽  
Parametric Estimation ◽  
Estimation Methods ◽  
Parametric Methods ◽  
Likelihood Estimator ◽  
Directional Distribution ◽  
Line Processes ◽  
Non Parametric Estimation ◽  
Non Parametric

Two non-parametric methods for the estimation of the directional measure of stationary line and fibre processes in d-dimensional space are presented. The input data for both methods are intersection counts with finitely many test windows situated in hyperplanes. The first estimator is a measure valued maximum likelihood estimator, if applied to Poisson line processes. The second estimator uses an approximation of the associated zonoid (the Steiner compact) by zonotopes. Consistency of both estimators is proved (without use of the Poisson assumption). The estimation methods are compared empirically by simulation.

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