Kernel density estimation under weak dependence with sampled data | 學術產出

題名	Kernel density estimation under weak dependence with sampled data
作者	吳柏林 Wu, Berlin
日期	1997-05
上傳時間	24-十二月-2008 13:28:45 (UTC+8)
摘要	Kernel type estimators of the density of continuous time R d-valued stochastic processes are studied. Uniform strong consistency on R d of the estimators and their rates of convergence are obtained. The stochastic processes are assumed to satisfy the strong mixing condition and the sampling instants are random. It is shown that the estimators can attain the optimal L2 rates of convergence.
關聯	Journal of Statistical Planning and Inference,61,141-154
資料類型	article
DOI	https://doi.org/10.1016/S0378-3758(96)00151-6

dc.creator (作者)	吳柏林	zh_TW
dc.creator (作者)	Wu, Berlin	-
dc.date (日期)	1997-05	en_US
dc.date.accessioned	24-十二月-2008 13:28:45 (UTC+8)	-
dc.date.available	24-十二月-2008 13:28:45 (UTC+8)	-
dc.date.issued (上傳時間)	24-十二月-2008 13:28:45 (UTC+8)	-
dc.identifier.uri (URI)	https://nccur.lib.nccu.edu.tw/handle/140.119/18681	-
dc.description.abstract (摘要)	Kernel type estimators of the density of continuous time R d-valued stochastic processes are studied. Uniform strong consistency on R d of the estimators and their rates of convergence are obtained. The stochastic processes are assumed to satisfy the strong mixing condition and the sampling instants are random. It is shown that the estimators can attain the optimal L2 rates of convergence.	-
dc.format	application/	en_US
dc.language	en	en_US
dc.language	en-US	en_US
dc.language.iso	en_US	-
dc.relation (關聯)	Journal of Statistical Planning and Inference,61,141-154	en_US
dc.title (題名)	Kernel density estimation under weak dependence with sampled data	en_US
dc.type (資料類型)	article	en
dc.identifier.doi (DOI)	10.1016/S0378-3758(96)00151-6	-
dc.doi.uri (DOI)	https://doi.org/10.1016/S0378-3758(96)00151-6	-