博碩士論文 106225018 完整後設資料紀錄

DC 欄位 語言
DC.contributor統計研究所zh_TW
DC.creator李建緯zh_TW
DC.creatorChien-Wei Leeen_US
dc.date.accessioned2019-8-22T07:39:07Z
dc.date.available2019-8-22T07:39:07Z
dc.date.issued2019
dc.identifier.urihttp://ir.lib.ncu.edu.tw:444/thesis/view_etd.asp?URN=106225018
dc.contributor.department統計研究所zh_TW
DC.description國立中央大學zh_TW
DC.descriptionNational Central Universityen_US
dc.description.abstract穿戴式裝置提供了收集人類身體活動信息的機會。然而受試者的意願和其他潛在行為,將會使參數估計時產生不可忽略的偏差。在此類型資料的分析中,研究人員通常使用半母數或無母數方法來避免模型錯誤所造成的偏差。但另一方面,有母數方法可以通過模型選擇來控制這種偏差,並且可以大幅的提升運算效率。在本文中,我們提供模擬研究來比較半母數方法和有母數方法的表現,並將我們的方法應用於來自美國國家健康和營養檢查調查的穿戴式裝置數據。zh_TW
dc.description.abstractWearable devices provide the opportunity to collect information of human being′s physical activity. However, there is non-negligible deviation from the subject′s willingness and other potential behaviors. In wearable device data analysis, researchers usually utilize semi-parametric or nonparametric approaches to avoid the bias from model misspeci cation. On the other hand, parametric approaches can control such bias by model selection, and can reduce computing time signi cantly. In this paper, we provide simulation studies to compare the performance of the semiparametic and parametric approaches. We apply our approach to the wearable device data from National Health and Nutrition Examination Survey is USA.en_US
DC.subject穿戴式裝置zh_TW
DC.subject偏誤及變異數之抵換zh_TW
DC.subject迴歸模型zh_TW
DC.subject模型選擇zh_TW
DC.subject三明治變異數估計法zh_TW
DC.subjectwearable devicesen_US
DC.subjectbias-variance trade-o ffen_US
DC.subjectpanel count regressionen_US
DC.subjectmodel selectionen_US
DC.subjectsandwich variance estimatoren_US
DC.titleA parametric model for wearable sensor-based physical activity monitoring data with informative device wearen_US
dc.language.isoen_USen_US
DC.type博碩士論文zh_TW
DC.typethesisen_US
DC.publisherNational Central Universityen_US

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