Test for Heteroscedasticity in Partially Linear Regression Models

查看全文 作　　者：KHALED [1,2]Waled;LIN [1,3]Jinguan;HAN [1]Zhongcheng;ZHAO [3]Yanyong;HAO [3]Hongxia 机构地区：[1]School of Mathematics,Southeast University,Nanjing 210096,China;[2]Department of Applied Statistics,Faculty of Economics,Damascus University,Syria;[3]School of Statistics and Mathematics,Nanjing Audit University,Nanjing 211815,China 出　　处：《Journal of Systems Science & Complexity》索引2019年第32卷第4期,共17页 基　　金：partly supported by the National Natural Science Foundation of China under Grant Nos.11571073,11701286,NSF,JS(BK20171073) 摘　　要：Testing heteroscedasticity determines whether the regression model can predict the dependent variable consistently across all values of the explanatory variables.Since the proposed tests could not detect heteroscedasticity in all cases,more precisely in heavy-tailed distributions,the authors established new comprehensive test statistic based on Levene’s test.The authors built the asymptotic normality of the test statistic under the null hypothesis of homoscedasticity based on the recent theory of analysis of variance for the infinite factors level.The proposed test uses the residuals from a regression model fit of the mean function with Levene’s test to assess homogeneity of variance.Simulation studies show that our test yields better than other methods in almost all cases even if the variance is a nonlinear function.Finally,the proposed method is implemented through a real data-set. 关 键 词：ANOVA heteroscedastic ERRORS HYPOTHESIS testing PARTIALLY LINEAR regression model