A parametric model for wearable sensor-based physical activity monitoring data with informative device wear

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：74

、訪客IP：3.133.152.222

姓名

李建緯(Chien-Wei Lee) 查詢紙本館藏

畢業系所

統計研究所

論文名稱

(A parametric model for wearable sensor-based physical activity monitoring data with informative device wear)

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

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。

摘要(英)

Wearable 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 misspecication. On the other hand, parametric approaches can control such bias by model selection, and can reduce computing time signicantly. 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.

關鍵字(中)

★ 穿戴式裝置
★ 偏誤及變異數之抵換
★ 迴歸模型
★ 模型選擇
★ 三明治變異數估計法

關鍵字(英)

★ wearable devices
★ bias-variance trade-off
★ panel count regression
★ model selection
★ sandwich variance estimator

論文目次

1 Introduction 1
2 Data and Simulation 3
2.1 NHANES Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.2 Data Exploration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3 Parametric Panel Count Regression Model 8
3.1 Likelihood Function . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.2 Sandwich Variance Estimator . . . . . . . . . . . . . . . . . . . . . 10
3.3 Akaike Information Criterion . . . . . . . . . . . . . . . . . . . . . . 11
4 Numerical Results 13
4.1 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.2 Real-World Data Example . . . . . . . . . . . . . . . . . . . . . . . 22
5 Discussion 24
Reference 25

參考文獻

Burnham, K. P., and Anderson, D. R. (2004). Multimodel inference: understand-
ing AIC and BIC in model selection. Sociological Methods and Research, 33,
261-304.
Elasho, M., and Louise R. (2004). An EM algorithm for estimating equations.
Journal of Computational and Graphical Statistics, 13, 48-65.
Evenson, K. R., Buchner, D. M., and Morland, K. B. (2012). Objective measure-
ment of physical activity and sedentary behavior among US adults aged 60 years
or older. Preventing Chronic Disease, 9, E26.
Evenson, K. R., Goto, M. M., and Furberg, R. D. (2015). Systematic review of
the validity and reliability of consumer-wearable activity trackers. International
Journal of Behavioral Nutrition and Physical Activity, 12, 159.
Hardin, J. (2003). The sandwich estimate of variance. Fomby, T. and Carter Hill,
R. (Ed.) Maximum Likelihood Estimation of Misspecied Models: Twenty Years
Later, (Advances in Econometrics, Vol. 17). Emerald Group Publishing Limited,
Bingley, 45-73.
Huang, C. Y., Wang, M. C., and Zhang, Y. (2006). Analysing panel count data
with informative observation times. Biometrika, 93, 763-776.
M^asse, L. C., Fuemmeler, B. F., Anderson, C. B., Matthews, C. E., Trost, S.
G., Catellier, D. J., and Treuth, M. (2005). Accelerometer data reduction: a
comparison of four reduction algorithms on select outcome variables. Medicine
and Science in Sports and Exercise, 37, S544-554.
Song, J. and Cox, M. G. (2015). acc: an r package to process accelerometer data.
http://cran.r-project.org/web/packages/acc/.
Song, J., Swartz, M. D., Gabriel, K. P., and Basen-Engquist, K. (2018).
A semiparametric model for wearable sensor-based physical activity moni-
toring data with informative device wear. Biostatistics, 20, 287-298 (Code:
http://github.com/github-js/semiparametric).
Troiano, R. P. (2006). Translating accelerometer counts into energy expenditure:
advancing the quest. Journal of Applied Physiology, 100, 1107{1108.
Troiano, R. P., McClain, J. J., Brychta, R. J., and Chen, K. Y. (2014). Evolution of
accelerometer methods for physical activity research. British Journal of Sports
Medicine, 48, 1019{1023.
Wang, X., Ma, S., and Yan, J. (2013). Augmented estimating equations for semi-
parametice panel count regression with informative observation times and cen-
soring time. Statistica Sinica, 23, 359-381.
Weng, H.-W. (2019). New insights on A semi-parametric model for wearable
sensor-based physical activity monitoring data with informative device wear".
Master Thesis. Advised by Huang, S.-H. and Sun, L.-H.. Nation Central Uni-
versity, Taoyuan, Taiwan .

指導教授

黃世豪孫立憲(Shih-Hao Huang Li-Hsien Sun)

審核日期

2019-8-22

推文