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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-Dec-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) 1997-05en_US
dc.date.accessioned 24-Dec-2008 13:28:45 (UTC+8)-
dc.date.available 24-Dec-2008 13:28:45 (UTC+8)-
dc.date.issued (上傳時間) 24-Dec-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 enen_US
dc.language en-USen_US
dc.language.iso en_US-
dc.relation (關聯) Journal of Statistical Planning and Inference,61,141-154en_US
dc.title (題名) Kernel density estimation under weak dependence with sampled dataen_US
dc.type (資料類型) articleen
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-