Consider a partially linear regression model with an unknown vector parameter , an unknown function g(·), and unknown heteroscedastic error variances. Chen, You<SUP>[23]</SUP> proposed a semiparametri...Consider a partially linear regression model with an unknown vector parameter , an unknown function g(·), and unknown heteroscedastic error variances. Chen, You<SUP>[23]</SUP> proposed a semiparametric generalized least squares estimator (SGLSE) for , which takes the heteroscedasticity into account to increase efficiency. For inference based on this SGLSE, it is necessary to construct a consistent estimator for its asymptotic covariance matrix. However, when there exists within-group correlation, the traditional delta method and the delete-1 jackknife estimation fail to offer such a consistent estimator. In this paper, by deleting grouped partial residuals a delete-group jackknife method is examined. It is shown that the delete-group jackknife method indeed can provide a consistent estimator for the asymptotic covariance matrix in the presence of within-group correlations. This result is an extension of that in [21].展开更多
Objective To obtain the exposure-response relationship for temperature and mortality, and assess the risk of heat-related premature death. Methods A statistical model was developed using a Poisson generalized linear r...Objective To obtain the exposure-response relationship for temperature and mortality, and assess the risk of heat-related premature death. Methods A statistical model was developed using a Poisson generalized linear regression model with Beijing mortality and temperature data from October 1st, 2006 to September 30th, 2008. We calculated the exposure-response relationship for temperature and mortality in the central city, and inner suburban and outer suburban regions. Based on this relationship, a health risk model was used to assess the risk of heat-related premature death in the summer （June to August） of 2009. Results The population in the outer suburbs had the highest temperature-related mortality risk. People in the central city had a mid-range risk, while people in the inner suburbs had the lowest risk. Risk assessment predicted that the number of heat-related premature deaths in the summer of 2009 was 1581. The city areas of Chaoyang and Haidian districts had the highest number of premature deaths. The number of premature deaths in the southern areas of Beijing （Fangshan, Fengtai, Daxing, and Tongzhou districts） was in the mid-range. Conclusion Ambient temperature significantly affects human mortality in Beijing. People in the city and outer suburban area have a higher temperature-related mortality risk than people in the inner suburban area. This may be explained by a temperature-related vulnerability. Key words： Temperature; Mortality; Premature death; Health risk; Generalized linear regression model; Climate change展开更多
In this paper, we introduce a generalized Liu estimator and jackknifed Liu estimator in a linear regression model with correlated or heteroscedastic errors. Therefore, we extend the Liu estimator. Under the mean squar...In this paper, we introduce a generalized Liu estimator and jackknifed Liu estimator in a linear regression model with correlated or heteroscedastic errors. Therefore, we extend the Liu estimator. Under the mean square error（MSE）, the jackknifed estimator is superior to the Liu estimator and the jackknifed ridge estimator. We also give a method to select the biasing parameter for d. Furthermore, a numerical example is given to illustvate these theoretical results.展开更多
The varying-coefficient partially linear regression model is proposed by combining nonparametric and varying-coefficient regression procedures. Wong, et al. （2008） proposed the model and gave its estimation by the l...The varying-coefficient partially linear regression model is proposed by combining nonparametric and varying-coefficient regression procedures. Wong, et al. （2008） proposed the model and gave its estimation by the local linear method. In this paper its inference is addressed. Based on these estimates, the generalized like- lihood ratio test is established. Under the null hypotheses the normalized test statistic follows a x2-distribution asymptotically, with the scale constant and the degrees of freedom being independent of the nuisance param- eters. This is the Wilks phenomenon. Furthermore its asymptotic power is also derived, which achieves the optimal rate of convergence for nonparametric hypotheses testing. A simulation and a real example are used to evaluate the performances of the testing procedures empirically.展开更多
文摘Consider a partially linear regression model with an unknown vector parameter , an unknown function g(·), and unknown heteroscedastic error variances. Chen, You<SUP>[23]</SUP> proposed a semiparametric generalized least squares estimator (SGLSE) for , which takes the heteroscedasticity into account to increase efficiency. For inference based on this SGLSE, it is necessary to construct a consistent estimator for its asymptotic covariance matrix. However, when there exists within-group correlation, the traditional delta method and the delete-1 jackknife estimation fail to offer such a consistent estimator. In this paper, by deleting grouped partial residuals a delete-group jackknife method is examined. It is shown that the delete-group jackknife method indeed can provide a consistent estimator for the asymptotic covariance matrix in the presence of within-group correlations. This result is an extension of that in [21].
基金supported by the National Natural Science Foundation of China(project numbers:40905069,41110104015)the Chinese Center for Disease Control and Prevention Science Foundation for Youth(project number:2011A206)
文摘Objective To obtain the exposure-response relationship for temperature and mortality, and assess the risk of heat-related premature death. Methods A statistical model was developed using a Poisson generalized linear regression model with Beijing mortality and temperature data from October 1st, 2006 to September 30th, 2008. We calculated the exposure-response relationship for temperature and mortality in the central city, and inner suburban and outer suburban regions. Based on this relationship, a health risk model was used to assess the risk of heat-related premature death in the summer （June to August） of 2009. Results The population in the outer suburbs had the highest temperature-related mortality risk. People in the central city had a mid-range risk, while people in the inner suburbs had the lowest risk. Risk assessment predicted that the number of heat-related premature deaths in the summer of 2009 was 1581. The city areas of Chaoyang and Haidian districts had the highest number of premature deaths. The number of premature deaths in the southern areas of Beijing （Fangshan, Fengtai, Daxing, and Tongzhou districts） was in the mid-range. Conclusion Ambient temperature significantly affects human mortality in Beijing. People in the city and outer suburban area have a higher temperature-related mortality risk than people in the inner suburban area. This may be explained by a temperature-related vulnerability. Key words： Temperature; Mortality; Premature death; Health risk; Generalized linear regression model; Climate change
基金Supported by the National Natural Science Foundation of China(11071022)Science and Technology Project of Hubei Provincial Department of Education(Q20122202)
文摘In this paper, we introduce a generalized Liu estimator and jackknifed Liu estimator in a linear regression model with correlated or heteroscedastic errors. Therefore, we extend the Liu estimator. Under the mean square error（MSE）, the jackknifed estimator is superior to the Liu estimator and the jackknifed ridge estimator. We also give a method to select the biasing parameter for d. Furthermore, a numerical example is given to illustvate these theoretical results.
基金supported in part by National Natural Science Foundation of China(11171112,11201190)Doctoral Fund of Ministry of Education of China(20130076110004)+1 种基金Program of Shanghai Subject Chief Scientist(14XD1401600)the 111 Project of China(B14019)
文摘The varying-coefficient partially linear regression model is proposed by combining nonparametric and varying-coefficient regression procedures. Wong, et al. （2008） proposed the model and gave its estimation by the local linear method. In this paper its inference is addressed. Based on these estimates, the generalized like- lihood ratio test is established. Under the null hypotheses the normalized test statistic follows a x2-distribution asymptotically, with the scale constant and the degrees of freedom being independent of the nuisance param- eters. This is the Wilks phenomenon. Furthermore its asymptotic power is also derived, which achieves the optimal rate of convergence for nonparametric hypotheses testing. A simulation and a real example are used to evaluate the performances of the testing procedures empirically.
