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政大機構典藏 > 商學院 > 統計學系 > 學位論文 > Item 140.119/87303

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/87303

Title:	時間數列之核密度估計探討 Kernel Density Estimation for Time Series
Authors:	姜一銘 Jiang, I Ming
Contributors:	吳柏林 Wu, B 姜一銘 Jiang, I Ming
Keywords:	核密度估計區間帶寬強混和幾乎確定 kernel density estimation bandwidth strong mixing almost sure
Date:	1996
Issue Date:	2016-04-28 11:48:29 (UTC+8)
Abstract:	對樣本資料之機率密度函數f(x)的無母數估計方法，一直是統計推論領域的研究重點之一，而且在通訊理論與圖形辨別上有非常重要的地位。傳統的文獻對密度函數的估計方法大部分著重於獨立樣本的情形。對於時間數列的相關樣本（例如：經濟指標或加權股票指數資料）比較少提到。本文針對具有弱相關性的穩定時間數列樣本，嘗試提出一個核密度估計的方法並探討其性質。　　For a sample data, the nonparametric estimation of a probability density f(x) is always one point of research problem in statistical inference and plays an important role in communication theory and pattern recognition. Traditionally, the literature dealing with density estimation when the observations are independent is extensive. Time series sample with weak dependence, (for example, an economic indicator or a stock market index data), less in this aspect of discussion. Our main purpose is concerned with the estimation of the probability density function f(x) of a stationary time series sample and discusses some properties of this kernel density.
Description:	碩士國立政治大學統計學系 83354005
Source URI:	http://thesis.lib.nccu.edu.tw/record/#B2002002788
Data Type:	thesis
Appears in Collections:	[統計學系] 學位論文

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