报告人：杭卫强博士(新加坡国立大学)
时间：12月9日 上午 10:30-11:30
地点：数学楼二楼学术报告厅
摘要：Goodness-of-fit tests for the conditional mean and conditional variance have been extensively studied in the literature, which concern whether a postulated model represents the underlying conditional mean or variance of the data adequately. However, little attention has been paid to checking the adequacy of the whole model beyond the conditional moments. In this paper, we consider the goodness-of-fit of models that are designed to incorporate all relevant information provided by the covariates to the response. This is particularly relevant to the noise model in causality analysis. Combining a measure of independence and resampling method, we propose a new method for the test. Corresponding asymptotic results are established. We demonstrate numerically the performance of our tests and compare the results with existing relevant methods. Using our tests, we check for existence of possible causal effect of a real data set.
简介：杭卫强，新加坡国立大学在读博士，主要研究方向：高维数据分析，非参数统计，变量选择。曾编写R包“MAVE”。