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• W2199664410 abstract "A compound Poisson process whose parameters are all unknown is observed at finitely many equispaced times. Nonparametric estimators of the jump and Lévy distributions are proposed and functional central limit theorems using the uniform norm are proved for both under mild conditions. The limiting Gaussian processes are identified and efficiency of the estimators is established. Kernel estimators for the mass function, the intensity and the drift are also proposed, their asymptotic properties including efficiency are analysed, and joint asymptotic normality is shown. Inference tools such as confidence regions and tests are briefly discussed." @default.
• W2199664410 created "2016-06-24" @default.
• W2199664410 creator A5072359658 @default.
• W2199664410 date "2017-03-16" @default.
• W2199664410 modified "2023-09-24" @default.
• W2199664410 title "Efficient nonparametric inference for discretely observed compound Poisson processes" @default.
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• W2199664410 doi "https://doi.org/10.1007/s00440-017-0761-5" @default.
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