Bangladesh, a developing country, gained success towards the fifth-millennium development goals target of reducing its maternal mortality ratio by three quarters by 2015, but yet worked more on it for further reductio...Bangladesh, a developing country, gained success towards the fifth-millennium development goals target of reducing its maternal mortality ratio by three quarters by 2015, but yet worked more on it for further reduction of maternal mortality. In this light, though Bangladesh is committed to the sustainable development goals target of reducing its maternal mortality ratio to be reduced from 170 to 105 per 100,000 live births, the scope of research on this issue is limited because the maternal morbidity data is scarce in Bangladesh. In this paper, the prospective data on maternal morbidity in rural Bangladesh (collected by BIRPERHT) have been employed to trace out the high-risk and life-threatening factors associated with pregnancy-related complications. The subject-specific generalized estimating equations (SS-GEE) model with random effect structure is used for multivariate binary data for the repeated observations. The findings indicate that the risk of suffering from pregnancy complications is higher for high economic status, lower age at marriage, not visited for medical check-ups, outside home workers, and having miscarriage or abortion. Comparing the SS-GEE model with other correlation structures and relative efficiency factors, the SS-GEE model with random effect structure is well fitted for the prospective repeated observation data.展开更多
For functional data,the most popular dimension reduction methods are functional sliced inverse regression(FSIR)and functional sliced average variance estimation(FSAVE).Both FSIR and FSAVE methods are based on the slic...For functional data,the most popular dimension reduction methods are functional sliced inverse regression(FSIR)and functional sliced average variance estimation(FSAVE).Both FSIR and FSAVE methods are based on the slice approach to estimate the conditional expectation E[x(t)|y].While sliced-based methods are effective for scalar responses,they often perform poorly or even lead to failure for multivariate responses and small sample sizes as the so-called“curse of dimensionality”.To avoid this problem,this study proposes a projective resampling method that first projects the multivariate response into a scalar-response and then uses SDR method for the univariate response to estimate the effective dimension reduction space(e.d.r space).The proposed projective resampling method is insensitive to the number of slices and the dimensionality of the response variable.In theory,the proposed resampling method can fully recover the effective dimension reduction space.Furthermore,this study investigates the performance of the proposed method through simulation studies and one real data analysis and compares the proposed method with other methods.展开更多
Given the limitation of traditional univariate analysis method in processing the multicollinearity of dam monitoring data,this paper reconstructs the multivariate response variables by introducing principal component ...Given the limitation of traditional univariate analysis method in processing the multicollinearity of dam monitoring data,this paper reconstructs the multivariate response variables by introducing principal component analysis(PCA)method,explores the ways of determining principal components(PCs),and extracts a few PCs that have major influence on data variance.For steady observation series,a control field for the whole observation values has been established based upon PCA;for unsteady observation series that have significant tendency,a control field for the future observation values has been constructed according to PC statistical predication model.These methods have already been applied to an actual project and the results showed that data interpretation method with PCA can not only realize data reduction,lower data redundancy,and reduce noise and false alarm rate,but also be effective to data analysis,having a broad application prospect.展开更多
Empirical likelihood in generalized linear models with multivariate responses and working covariance matrix is discussed.Under the weakest assumption on eigenvalues of Fisher’s information matrix and some other regul...Empirical likelihood in generalized linear models with multivariate responses and working covariance matrix is discussed.Under the weakest assumption on eigenvalues of Fisher’s information matrix and some other regular conditions,we prove that the non-parametric Wilk’s property still holds,that is,the empirical log-likelihood ratio at the true parameter values converges to the standard chi-square distribution.Numerical simulations are given to verify our theoretical result.展开更多
文摘Bangladesh, a developing country, gained success towards the fifth-millennium development goals target of reducing its maternal mortality ratio by three quarters by 2015, but yet worked more on it for further reduction of maternal mortality. In this light, though Bangladesh is committed to the sustainable development goals target of reducing its maternal mortality ratio to be reduced from 170 to 105 per 100,000 live births, the scope of research on this issue is limited because the maternal morbidity data is scarce in Bangladesh. In this paper, the prospective data on maternal morbidity in rural Bangladesh (collected by BIRPERHT) have been employed to trace out the high-risk and life-threatening factors associated with pregnancy-related complications. The subject-specific generalized estimating equations (SS-GEE) model with random effect structure is used for multivariate binary data for the repeated observations. The findings indicate that the risk of suffering from pregnancy complications is higher for high economic status, lower age at marriage, not visited for medical check-ups, outside home workers, and having miscarriage or abortion. Comparing the SS-GEE model with other correlation structures and relative efficiency factors, the SS-GEE model with random effect structure is well fitted for the prospective repeated observation data.
基金supported by the National Social Science Foundation of China under Grant No.20BTJ041。
文摘For functional data,the most popular dimension reduction methods are functional sliced inverse regression(FSIR)and functional sliced average variance estimation(FSAVE).Both FSIR and FSAVE methods are based on the slice approach to estimate the conditional expectation E[x(t)|y].While sliced-based methods are effective for scalar responses,they often perform poorly or even lead to failure for multivariate responses and small sample sizes as the so-called“curse of dimensionality”.To avoid this problem,this study proposes a projective resampling method that first projects the multivariate response into a scalar-response and then uses SDR method for the univariate response to estimate the effective dimension reduction space(e.d.r space).The proposed projective resampling method is insensitive to the number of slices and the dimensionality of the response variable.In theory,the proposed resampling method can fully recover the effective dimension reduction space.Furthermore,this study investigates the performance of the proposed method through simulation studies and one real data analysis and compares the proposed method with other methods.
基金supported by the National Natural Science Foundation of China(Grant Nos 50909041,50879024,50809025,50539010,50539110)the National Supporting Program(Grant Nos 2008BAB29B03,2008BAB-29B06)the Natural Science Foundation of Hohai University(Grant No 2008426811)
文摘Given the limitation of traditional univariate analysis method in processing the multicollinearity of dam monitoring data,this paper reconstructs the multivariate response variables by introducing principal component analysis(PCA)method,explores the ways of determining principal components(PCs),and extracts a few PCs that have major influence on data variance.For steady observation series,a control field for the whole observation values has been established based upon PCA;for unsteady observation series that have significant tendency,a control field for the future observation values has been constructed according to PC statistical predication model.These methods have already been applied to an actual project and the results showed that data interpretation method with PCA can not only realize data reduction,lower data redundancy,and reduce noise and false alarm rate,but also be effective to data analysis,having a broad application prospect.
基金supported by the National Social Science Fund(Grant No.18BTJ040)。
文摘Empirical likelihood in generalized linear models with multivariate responses and working covariance matrix is discussed.Under the weakest assumption on eigenvalues of Fisher’s information matrix and some other regular conditions,we prove that the non-parametric Wilk’s property still holds,that is,the empirical log-likelihood ratio at the true parameter values converges to the standard chi-square distribution.Numerical simulations are given to verify our theoretical result.
