A Modified Neighborhood Hypothesis Test for Population Mean in Functional Data
A Modified Neighborhood Hypothesis Test for Population Mean in Functional Data
复制标题
函数数据中总体均值的修正邻域假设检验
DOI：
10.1007/s13253-023-00549-y
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Pal, Ranadip
中科院分区：
文献类型：
--
作者：
Bandara, Dhanamalee;Ellingson, Leif;Ghosh, Souparno;Pal, Ranadip
在处理非常高维的函数数据时，样本协方差矩阵的秩不足往往使总体均值的检验复杂化。为了缓解这一秩不足问题，Munk等人（J Multivar Anal 99:815-833, 2008）提出了邻域假设检验程序，该程序检验总体均值是否在已知数量M的预先指定的小邻域内。我们如何客观地指定一个合理的邻域，特别是当样本空间是无界的时候？社区的规模应该是多少？在本文中，我们开发了改进的邻域假设检验框架来回答这两个问题。我们将邻域定义为所研究的函数总体中变异总量的比例，并推导出适当检验统计量的渐近零分布。功率分析表明，当样本空间无界时，我们的方法是合适的，并且对具有非零平均值的误差结构具有鲁棒性。然后，我们应用该框架来评估接近默认的s型剂量-反应曲线规范是否足以适用于广泛使用的CCLE数据库。结果表明，我们的方法可以用作使用传统功效指标（从s型模型（例如：ICor AUC）获得）作为下游预测目标之前的预处理步骤。
When dealing with very high-dimensional and functional data, rank deficiency of sample covariance matrix often complicates the tests for population mean. To alleviate this rank deficiency problem, Munk et al. (J Multivar Anal 99:815–833, 2008) proposed neighborhood hypothesis testing procedure that tests whether the population mean is within a small, pre-specified neighborhood of a known quantity,M. How could we objectively specify a reasonable neighborhood, particularly when the sample space is unbounded? What should be the size of the neighborhood? In this article, we develop the modified neighborhood hypothesis testing framework to answer these two questions. We define the neighborhood as a proportion of the total amount of variation present in the population of functions under study and proceed to derive the asymptotic null distribution of the appropriate test statistic. Power analyses suggest that our approach is appropriate when sample space is unbounded and is robust against error structures with nonzero mean. We then apply this framework to assess whether the near-default sigmoidal specification of dose-response curves is adequate for widely used CCLE database. Results suggest that our methodology could be used as a pre-processing step before using conventional efficacy metrics, obtained from sigmoid models (for example: ICor AUC), as downstream predictive targets.
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影响因子：
8.8
作者：
通讯作者：
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希尔伯特流形均值的非参数估计和轮廓平均形状的外在分析
影响因子：
1.6
作者：
Leif Ellingson;V. Patrangenaru;F. Ruymgaart
通讯作者：
F. Ruymgaart
DOI：
--
发表时间：
2003
期刊：
影响因子：
--
作者：
H. Dette;A. Munk
通讯作者：
A. Munk
影响因子：
64.8
作者：
Barretina, Jordi;Caponigro, Giordano;Stransky, Nicolas;Venkatesan, Kavitha;Margolin, Adam A.;Kim, Sungjoon;Wilson, Christopher J.;Lehar, Joseph;Kryukov, Gregory V.;Sonkin, Dmitriy;Reddy, Anupama;Liu, Manway;Murray, Lauren;Berger, Michael F.;Monahan, John E.;Morais, Paula;Meltzer, Jodi;Korejwa, Adam;Jane-Valbuena, Judit;Mapa, Felipa A.;Thibault, Joseph;Bric-Furlong, Eva;Raman, Pichai;Shipway, Aaron;Engels, Ingo H.;Cheng, Jill;Yu, Guoying K.;Yu, Jianjun;Aspesi, Peter, Jr.;de Silva, Melanie;Jagtap, Kalpana;Jones, Michael D.;Wang, Li;Hatton, Charles;Palescandolo, Emanuele;Gupta, Supriya;Mahan, Scott;Sougnez, Carrie;Onofrio, Robert C.;Liefeld, Ted;MacConaill, Laura;Winckler, Wendy;Reich, Michael;Li, Nanxin;Mesirov, Jill P.;Gabriel, Stacey B.;Getz, Gad;Ardlie, Kristin;Chan, Vivien;Myer, Vic E.;Weber, Barbara L.;Porter, Jeff;Warmuth, Markus;Finan, Peter;Harris, Jennifer L.;Meyerson, Matthew;Golub, Todd R.;Morrissey, Michael P.;Sellers, William R.;Schlegel, Robert;Garraway, Levi A.
通讯作者：
Garraway, Levi A.
影响因子：
22.7
作者：
Ma J;Fong SH;Luo Y;Bakkenist CJ;Shen JP;Mourragui S;Wessels LFA;Hafner M;Sharan R;Peng J;Ideker T
通讯作者：
Ideker T