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研究生: 翁許瑋
Hsu-Wei Weng
論文名稱: New insights on ''A semi-parametric model for wearable sensor-based physical activity monitoring data with informative device wear"
指導教授: 黃世豪
孫立憲
口試委員:
學位類別: 碩士
Master
系所名稱: 理學院 - 統計研究所
Graduate Institute of Statistics
論文出版年: 2019
畢業學年度: 107
語文別: 英文
論文頁數: 32
中文關鍵詞: 增廣估計方程式時間尺度不變性半母數模型
外文關鍵詞: augmented estimating equation, time-scale invariant, semi-parametric regression
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  • 在本文我們指出並解決在Song et al.(2018)中的兩個問題。第一,我們描述了他們使用的半參數模型對時間尺度具有估計的不變性,並指出我們在運用Song et al.(2018)所提供的R程式套件進行估計時,結果並無具備這個性質。第二,我們發現他們的模擬設定不符合他所使用的半母數模型的假設,這可能導致模擬結果的不可靠。我們提供了一個正確的R程式碼並使用較合適的模擬設定來分析,並用以一個真實資料的例子來分析。


    In this article we indicate and solve two issues found in Song et al.(2018). First, we characterize the time-scale invariant property of the semi-parametric model they utilize, and show that their R package provides different results after time scaling. Second, we find the setting of their main simulation does not follow the assumptions of the utilized model, which may lead to an unreliable conclusion. We provide corrected R code for practical use, and give an appropriate simulation setting to illustrate the behavior of the utilized model in a real-data example.

    1 Introduction 1 2 Motivating Data 2 3 Semi-Parametric Panel Count Regression Model 5 3.1 Augmented estimating equations and ES-algorithm . . . . . . . . . 6 3.2 Variance estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.3 Time-scale invariant property . . . . . . . . . . . . . . . . . . . . . 10 4 Numerical Study 11 4.1 The issue of the simulation setting in Song et al. (2018) . . . . . . . 12 4.2 Simulation studies . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 4.3 Real world data analysis . . . . . . . . . . . . . . . . . . . . . . . . 18 5 Discussion 19 Reference 21

    Elasho , M., and Ryan, L. (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.
    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).
    Toledano, A. Y., and Gatsonis, C. (1999). Generalized estimating equations for
    ordinal categorical data: arbitrary patterns of missing responses and missingness
    in a key covariate. Biometrics, 55, 488-496.
    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.
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