کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1156717 | 958860 | 2013 | 47 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
On non-parametric estimation of the Lévy kernel of Markov processes
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موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
ریاضیات (عمومی)
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
We consider a recurrent Markov process which is an Itô semi-martingale. The Lévy kernel describes the law of its jumps. Based on observations X0,XΔ,…,XnΔX0,XΔ,…,XnΔ, we construct an estimator for the Lévy kernel’s density. We prove its consistency (as nΔ→∞nΔ→∞ and Δ→0Δ→0) and a central limit theorem. In the positive recurrent case, our estimator is asymptotically normal; in the null recurrent case, it is asymptotically mixed normal. Our estimator’s rate of convergence equals the non-parametric minimax rate of smooth density estimation. The asymptotic bias and variance are analogous to those of the classical Nadaraya–Watson estimator for conditional densities. Asymptotic confidence intervals are provided.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Stochastic Processes and their Applications - Volume 123, Issue 10, October 2013, Pages 3663–3709
Journal: Stochastic Processes and their Applications - Volume 123, Issue 10, October 2013, Pages 3663–3709
نویسندگان
Florian A.J. Ueltzhöfer,