Air quality is a critical concern for public health and environmental regulation. The Air Quality Index (AQI), a widely adopted index by the US Environmental Protection Agency (EPA), serves as a crucial metric for rep...Air quality is a critical concern for public health and environmental regulation. The Air Quality Index (AQI), a widely adopted index by the US Environmental Protection Agency (EPA), serves as a crucial metric for reporting site-specific air pollution levels. Accurately predicting air quality, as measured by the AQI, is essential for effective air pollution management. In this study, we aim to identify the most reliable regression model among linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression, and K-nearest neighbors (KNN). We conducted four different regression analyses using a machine learning approach to determine the model with the best performance. By employing the confusion matrix and error percentages, we selected the best-performing model, which yielded prediction error rates of 22%, 23%, 20%, and 27%, respectively, for LDA, QDA, logistic regression, and KNN models. The logistic regression model outperformed the other three statistical models in predicting AQI. Understanding these models' performance can help address an existing gap in air quality research and contribute to the integration of regression techniques in AQI studies, ultimately benefiting stakeholders like environmental regulators, healthcare professionals, urban planners, and researchers.展开更多
High-dimensional longitudinal data arise frequently in biomedical and genomic research. It is important to select relevant covariates when the dimension of the parameters diverges as the sample size increases. We cons...High-dimensional longitudinal data arise frequently in biomedical and genomic research. It is important to select relevant covariates when the dimension of the parameters diverges as the sample size increases. We consider the problem of variable selection in high-dimensional linear models with longitudinal data. A new variable selection procedure is proposed using the smooth-threshold generalized estimating equation and quadratic inference functions (SGEE-QIF) to incorporate correlation information. The proposed procedure automatically eliminates inactive predictors by setting the corresponding parameters to be zero, and simultaneously estimates the nonzero regression coefficients by solving the SGEE-QIF. The proposed procedure avoids the convex optimization problem and is flexible and easy to implement. We establish the asymptotic properties in a high-dimensional framework where the number of covariates increases as the number of cluster increases. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed variable selection procedure.展开更多
This paper studies the problem of robust H∞ control of piecewise-linear chaotic systems with random data loss. The communication links between the plant and the controller are assumed to be imperfect （that is, data ...This paper studies the problem of robust H∞ control of piecewise-linear chaotic systems with random data loss. The communication links between the plant and the controller are assumed to be imperfect （that is, data loss occurs intermittently, which appears typically in a network environment）. The data loss is modelled as a random process which obeys a Bernoulli distribution. In the face of random data loss, a piecewise controller is designed to robustly stabilize the networked system in the sense of mean square and also achieve a prescribed H∞ disturbance attenuation performance based on a piecewise-quadratic Lyapunov function. The required H∞ controllers can be designed by solving a set of linear matrix inequalities （LMIs）. Chua＇s system is provided to illustrate the usefulness and applicability of the developed theoretical results.展开更多
In this article, we propose a generalized empirical likelihood inference for the parametric component in semiparametric generalized partially linear models with longitudinal data. Based on the extended score vector, a...In this article, we propose a generalized empirical likelihood inference for the parametric component in semiparametric generalized partially linear models with longitudinal data. Based on the extended score vector, a generalized empirical likelihood ratios function is defined, which integrates the within-cluster?correlation meanwhile avoids direct estimating the nuisance parameters in the correlation matrix. We show that the proposed statistics are asymptotically?Chi-squared under some suitable conditions, and hence it can be used to construct the confidence region of parameters. In addition, the maximum empirical likelihood estimates of parameters and the corresponding asymptotic normality are obtained. Simulation studies demonstrate the performance of the proposed method.展开更多
因传统方法单一使用遥感影像或兴趣点(point of interest,POI)数据识别城市功能区时,存在城市特征信息利用不完全、识别精度不高的问题,提出利用POI数据、遥感影像等多源数据,并将自然特征和人文特征相结合,采用基于掩膜区域卷积神经网...因传统方法单一使用遥感影像或兴趣点(point of interest,POI)数据识别城市功能区时,存在城市特征信息利用不完全、识别精度不高的问题,提出利用POI数据、遥感影像等多源数据,并将自然特征和人文特征相结合,采用基于掩膜区域卷积神经网络和样方密度法(mask region based convolutional neural network and quadrat density method,Mask R-CNN-QDM)模型识别城市功能区的方法。首先基于遥感影像采用Mask R-CNN模型识别建筑物,然后将识别结果与POI数据进行补充校验,得到结合自然特征和人文特征的分类结果,再引入面积要素对分类结果进行赋分,以计算样方密度,并采用随机抽样方式对所提方法功能区的识别精度进行评价。研究结果表明,Mask R-CNN-QDM模型的识别精确度高达0.900,平均Kappa系数为0.802,说明该方法能较好地区分单一城市功能区和混合城市功能区。展开更多
文摘Air quality is a critical concern for public health and environmental regulation. The Air Quality Index (AQI), a widely adopted index by the US Environmental Protection Agency (EPA), serves as a crucial metric for reporting site-specific air pollution levels. Accurately predicting air quality, as measured by the AQI, is essential for effective air pollution management. In this study, we aim to identify the most reliable regression model among linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression, and K-nearest neighbors (KNN). We conducted four different regression analyses using a machine learning approach to determine the model with the best performance. By employing the confusion matrix and error percentages, we selected the best-performing model, which yielded prediction error rates of 22%, 23%, 20%, and 27%, respectively, for LDA, QDA, logistic regression, and KNN models. The logistic regression model outperformed the other three statistical models in predicting AQI. Understanding these models' performance can help address an existing gap in air quality research and contribute to the integration of regression techniques in AQI studies, ultimately benefiting stakeholders like environmental regulators, healthcare professionals, urban planners, and researchers.
文摘High-dimensional longitudinal data arise frequently in biomedical and genomic research. It is important to select relevant covariates when the dimension of the parameters diverges as the sample size increases. We consider the problem of variable selection in high-dimensional linear models with longitudinal data. A new variable selection procedure is proposed using the smooth-threshold generalized estimating equation and quadratic inference functions (SGEE-QIF) to incorporate correlation information. The proposed procedure automatically eliminates inactive predictors by setting the corresponding parameters to be zero, and simultaneously estimates the nonzero regression coefficients by solving the SGEE-QIF. The proposed procedure avoids the convex optimization problem and is flexible and easy to implement. We establish the asymptotic properties in a high-dimensional framework where the number of covariates increases as the number of cluster increases. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed variable selection procedure.
基金Project partially supported by the Young Scientists Fund of the National Natural Science Foundation of China(Grant No.60904004)the Key Youth Science and Technology Foundation of University of Electronic Science and Technology of China (Grant No.L08010201JX0720)
文摘This paper studies the problem of robust H∞ control of piecewise-linear chaotic systems with random data loss. The communication links between the plant and the controller are assumed to be imperfect （that is, data loss occurs intermittently, which appears typically in a network environment）. The data loss is modelled as a random process which obeys a Bernoulli distribution. In the face of random data loss, a piecewise controller is designed to robustly stabilize the networked system in the sense of mean square and also achieve a prescribed H∞ disturbance attenuation performance based on a piecewise-quadratic Lyapunov function. The required H∞ controllers can be designed by solving a set of linear matrix inequalities （LMIs）. Chua＇s system is provided to illustrate the usefulness and applicability of the developed theoretical results.
文摘In this article, we propose a generalized empirical likelihood inference for the parametric component in semiparametric generalized partially linear models with longitudinal data. Based on the extended score vector, a generalized empirical likelihood ratios function is defined, which integrates the within-cluster?correlation meanwhile avoids direct estimating the nuisance parameters in the correlation matrix. We show that the proposed statistics are asymptotically?Chi-squared under some suitable conditions, and hence it can be used to construct the confidence region of parameters. In addition, the maximum empirical likelihood estimates of parameters and the corresponding asymptotic normality are obtained. Simulation studies demonstrate the performance of the proposed method.
文摘因传统方法单一使用遥感影像或兴趣点(point of interest,POI)数据识别城市功能区时,存在城市特征信息利用不完全、识别精度不高的问题,提出利用POI数据、遥感影像等多源数据,并将自然特征和人文特征相结合,采用基于掩膜区域卷积神经网络和样方密度法(mask region based convolutional neural network and quadrat density method,Mask R-CNN-QDM)模型识别城市功能区的方法。首先基于遥感影像采用Mask R-CNN模型识别建筑物,然后将识别结果与POI数据进行补充校验,得到结合自然特征和人文特征的分类结果,再引入面积要素对分类结果进行赋分,以计算样方密度,并采用随机抽样方式对所提方法功能区的识别精度进行评价。研究结果表明,Mask R-CNN-QDM模型的识别精确度高达0.900,平均Kappa系数为0.802,说明该方法能较好地区分单一城市功能区和混合城市功能区。
基金The project supported by the National Natural Science Foundation of China(11171012)the Science and Technology Project of the Faculty Adviser of Excellent PHD Degree Thesis of Beijing(20111000503)+1 种基金the Beijing Municipal Education Commission Foundation(KM201110005029)Beijing Municipal Key Disciplines(006000541212010)
