数学
马尔可夫链
平滑的
估计员
扩展(谓词逻辑)
功能数据分析
构造(python库)
协方差
非参数统计
领域(数学分析)
应用数学
算法
统计
计算机科学
数学分析
程序设计语言
作者
Aurore Delaigle,Peter Hall
出处
期刊:Biometrika
[Oxford University Press]
日期:2016-11-11
卷期号:103 (4): 779-799
被引量:34
标识
DOI:10.1093/biomet/asw040
摘要
We consider curve extension and linear prediction for functional data observed only on a part of their domain, in the form of fragments. We suggest an approach based on a combination of Markov chains and nonparametric smoothing techniques, which enables us to extend the observed fragments and construct approximated prediction intervals around them, construct mean and covariance function estimators, and derive a linear predictor. The procedure is illustrated on real and simulated data.
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