Two-sample Testing for Mean Functions with Incompletely Observed Functional Data  

作　　者：Yan-qiu ZHOU Yan-ling WAN Tao ZHANG 

机构地区：[1]School of Science,Guangxi University of Science and Technology,Liuzhou 545006,China [2]School of Social Science,Guangxi University of Science and Technology,Liuzhou 545006,China

出　　处：《Acta Mathematicae Applicatae Sinica》2020年第2期374-389,共16页应用数学学报（英文版）

基　　金：supported by National Natural Science Foundation of China(11561006,11861014);Natural Science Foundation of Guangxi(2018GXNSFAA281145);Social Science Foundation Project of Jilin for Doctoral and Youth Support(2019c24).

摘　　要：In functional data analysis,the collected data are often assumed to be fully observed on the domain.However,in dealing with real data(for example,environmental pollution data),we are often faced with the scenario that some functional data are fully observed on dense lattice while others are incompletely observed.In this paper,we propose a method for testing equivalence of mean functions of two samples under this scenario.Some asymptotic results of the proposed methods are established.The proposed test is employed to analyze an environmental pollution study in Liuzhou City of China.Simulations show that the proposed test has a good control of the type-I error,and is more powerful than the complete case test in most cases.

关 键 词：FUNCTIONAL DATA analysis SIGNIFICANCE test incompletely OBSERVED FUNCTIONAL DATA mean FUNCTION 

分 类 号：O212.2[理学—概率论与数理统计]