標(biāo)題：Ensemble methods for testing a global null
報(bào)告時(shí)間：2024年04月09日(星期五)15:00-16:00
報(bào)告地點(diǎn)：人民大街校區(qū)數(shù)學(xué)與統(tǒng)計(jì)學(xué)院415室
主講人：劉耀午
主辦單位：數(shù)學(xué)與統(tǒng)計(jì)學(xué)院
報(bào)告內(nèi)容簡(jiǎn)介：
Testing a global null is a canonical problem in statistics and has a wide range of applications. In view of the fact that no uniformly most powerful test exists, prior and/or domain knowledge are commonly used to focus on a certain class of alternatives to improve the testing power. However, it is generally challenging to develop tests that are particularly powerful against a certain class of alternatives. In this paper, motivated by the success of ensemble learning methods for prediction or classification, we propose an ensemble framework for testing that mimics the spirit of random forests to deal with the challenges. Our ensemble testing framework aggregates a collection of weak base tests to form a final ensemble test that maintains strong and robust power. We apply the framework to four problems about global testing in different classes of alternatives arising from Whole Genome Sequencing (WGS) association studies. Specific ensemble tests are proposed for each of these problems, and their theoretical optimality is established in terms of Bahadur efficiency. Extensive simulations and an analysis of a real WGS dataset are conducted to demonstrate the type I error control and/or power gain of the proposed ensemble tests.
主講人簡(jiǎn)介：
劉耀午，西南財(cái)經(jīng)大學(xué)統(tǒng)計(jì)學(xué)院教授。他的研究興趣包括統(tǒng)計(jì)遺傳學(xué)，大規(guī)模假設(shè)檢驗(yàn)，全基因組關(guān)聯(lián)性分析等。他的多項(xiàng)研究成果發(fā)表于JASA，JRSSB, American Journal of Human Genetics等統(tǒng)計(jì)學(xué)和遺傳學(xué)知名期刊。
