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A non-parametric estimator for the doubly periodic Poisson intensity function
Authors:Roelof Helmers, I. Wayan Mangku,Ri   ardas Zitikis
Affiliation:

aCentre for Mathematics and Computer Science (CWI), P.O. Box 94079, 1090 GB, Amsterdam, The Netherlands

bDepartment of Mathematics, Bogor Agricultural University, Jl. Meranti, Kampus IPB Darmaga, Bogor 16680, Indonesia

cDepartment of Statistical and Actuarial Sciences, University of Western Ontario, London, Ontario, Canada N6A 5B7

Abstract:
In a series of papers, J. Garrido and Y. Lu have proposed and investigated a doubly periodic Poisson model, and then applied it to analyze hurricane data. The authors have suggested several parametric models for the underlying intensity function. In the present paper we construct and analyze a non-parametric estimator for the doubly periodic intensity function. Assuming that only a single realization of the process is available in a bounded window, we show that the estimator is consistent and asymptotically normal when the window expands indefinitely. In addition, we calculate the asymptotic bias and variance of the estimator, and in this way gain helpful information for optimizing the performance of the estimator.
Keywords:Poisson process   Doubly periodic Poisson process   Periodic intensity function   Non-parametric estimation   Consistency   Asymptotic normality   Bias   Variance   Mean-squared error
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