The authors propose a robust semi-parametric empirical likelihood method to integrate all available information from multiple samples with a common center of measurements. Two different sets of estimating equations ar...The authors propose a robust semi-parametric empirical likelihood method to integrate all available information from multiple samples with a common center of measurements. Two different sets of estimating equations are used to improve the classical likelihood inference on the measurement center. The proposed method does not require the knowle- dge of the functional forms of the probability density functions of related populations. The advantages of the proposed method are demonstrated through extensive simulation studies by comparing the mean squared errors, coverage proba- bilities and average lengths of confidence intervals with those from the classical likelihood method. Simulation results suggest that our approach provides more informative and efficient inference than the conventional maximum likelihood estimator if certain structural relationship exists among the parameters of relevant samples.展开更多
Geographic simulation models can be used to explore and better understand the geographical environment. Recent advances in geographic and socio-environmental research have led to a dramatic increase in the number of m...Geographic simulation models can be used to explore and better understand the geographical environment. Recent advances in geographic and socio-environmental research have led to a dramatic increase in the number of models used for this purpose. Some model repositories provide opportunities for users to explore and apply models,but few provide a general evaluation method for assessing the applicability and recognition of models. In this study,an academic impact evaluation method for models is proposed. Five indices are designed based on their pertinence. The analytical hierarchy process is used to calculate the index weights,and the academic impacts of models are quantified with the weighted sum method. The time range is controlled to evaluate the life-term and annual academic impacts of the models. Some models that met the evaluation criteria from different domains are then evaluated. The results show that the academic impact of a model can be quantified with the proposed method,and the major research areas that models impact are identified.展开更多
Multivariate mixtures are encountered in situations where the data are repeated or clustered measurements in the presence of heterogeneity among the observations with unknown proportions.In such situations,the main in...Multivariate mixtures are encountered in situations where the data are repeated or clustered measurements in the presence of heterogeneity among the observations with unknown proportions.In such situations,the main interest may be not only in estimating the component parameters,but also in obtaining reliable estimates of the mixing proportions.In this paper,we propose an empirical likelihood approach combined with a novel dimension reduction procedure for estimating parameters of a two-component multivariate mixture model.The performance of the new method is compared to fully parametric as well as almost nonparametric methods used in the literature.展开更多
文摘The authors propose a robust semi-parametric empirical likelihood method to integrate all available information from multiple samples with a common center of measurements. Two different sets of estimating equations are used to improve the classical likelihood inference on the measurement center. The proposed method does not require the knowle- dge of the functional forms of the probability density functions of related populations. The advantages of the proposed method are demonstrated through extensive simulation studies by comparing the mean squared errors, coverage proba- bilities and average lengths of confidence intervals with those from the classical likelihood method. Simulation results suggest that our approach provides more informative and efficient inference than the conventional maximum likelihood estimator if certain structural relationship exists among the parameters of relevant samples.
基金supported by National Key Research and Development Program of China(Grant number 2022YFF0711604)the General Project of the NSF of China(Grant number 42071363).
文摘Geographic simulation models can be used to explore and better understand the geographical environment. Recent advances in geographic and socio-environmental research have led to a dramatic increase in the number of models used for this purpose. Some model repositories provide opportunities for users to explore and apply models,but few provide a general evaluation method for assessing the applicability and recognition of models. In this study,an academic impact evaluation method for models is proposed. Five indices are designed based on their pertinence. The analytical hierarchy process is used to calculate the index weights,and the academic impacts of models are quantified with the weighted sum method. The time range is controlled to evaluate the life-term and annual academic impacts of the models. Some models that met the evaluation criteria from different domains are then evaluated. The results show that the academic impact of a model can be quantified with the proposed method,and the major research areas that models impact are identified.
基金partially supported by the Natural Sciences and Engineering Research Council of Canada(NSERC)Discovery Grants(RGPIN-2018-05846,RGPIN-2018-05981)the National Natural Science Foundation of China(Grant Numbers 11771144,11501354 and 11501208)the Chinese 111 Project(B14019).
文摘Multivariate mixtures are encountered in situations where the data are repeated or clustered measurements in the presence of heterogeneity among the observations with unknown proportions.In such situations,the main interest may be not only in estimating the component parameters,but also in obtaining reliable estimates of the mixing proportions.In this paper,we propose an empirical likelihood approach combined with a novel dimension reduction procedure for estimating parameters of a two-component multivariate mixture model.The performance of the new method is compared to fully parametric as well as almost nonparametric methods used in the literature.
