正项级数敛散性的密率判别法

借鉴数论方法中的密率论 ,给出判别正项级数敛散性的密率判别法 ,此方法特别适用于判定一些较难或不能给出通项表达式的级数的敛散性     

Dimension reduction boosting

L₂Boosting is an effective method for constructing model. In the case of high-dimensional setting, Bühlmann and Yu (2003 Bühlmann, P., Yu, B. (2003). Boosting with the L₂-loss: regression and classification. J. Amer. Stat. Assoc. 98:324–339.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) proposed the componentwise L₂Boosting, but componentwise L₂Boosting can only fit a special limited model. In this paper, by combining a boosting and sufficient dimension reduction method, e.g., sliced inverse regression (SIR), we propose a new method for regression, called dimension reduction boosting (DRBoosting). Compared with L₂Boosting, the computation of DRBoosting is less intensive and its prediction is better, especially for high-dimensional data. Simulations confirm the advantage of the new method.     

时滞系统PID控制器参数稳定域的实现

基于广义Hermite-Biehler定理,运用时滞对象的逆Nyquist曲线,可确定PID控制器比例增益的稳定范围。在积分和微分增益平面上,针对多条边界直线,提出一种逆时针规律的判断方法,可快速确定该二维平面上参数的稳定区域,从而给出了一种确定时滞系统PID控制器参数稳定域的新算法。该算法适合软件实现,仿真实例验证了该算法的有效性。     

Model-based clustering with non-elliptically contoured distributions

The majority of the existing literature on model-based clustering deals with symmetric components. In some cases, especially when dealing with skewed subpopulations, the estimate of the number of groups can be misleading; if symmetric components are assumed we need more than one component to describe an asymmetric group. Existing mixture models, based on multivariate normal distributions and multivariate t distributions, try to fit symmetric distributions, i.e. they fit symmetric clusters. In the present paper, we propose the use of finite mixtures of the normal inverse Gaussian distribution (and its multivariate extensions). Such finite mixture models start from a density that allows for skewness and fat tails, generalize the existing models, are tractable and have desirable properties. We examine both the univariate case, to gain insight, and the multivariate case, which is more useful in real applications. EM type algorithms are described for fitting the models. Real data examples are used to demonstrate the potential of the new model in comparison with existing ones.     

局部环上一类矩阵广义逆的偏序

设R是局部环,A=P^-1（I^（r）＋D）P∈Mn（R）,其中D=diag{d1,…,dn-r}或D=0,d1,…,dn-r∈M,.研究R上集合意义下广义逆的矩阵偏序A≤^[3]和A≤^[2,3],确定了具有偏序A≤^[3]和A≤^[2,3]的矩阵集Mn（B┇A≤^[3]B）和Mn（B┇A≤^[2,3]B）都不是单点集。     

Defective models induced by gamma frailty term for survival data with cured fraction

In this paper, we propose a defective model induced by a frailty term for modeling the proportion of cured. Unlike most of the cure rate models, defective models have advantage of modeling the cure rate without adding any extra parameter in model. The introduction of an unobserved heterogeneity among individuals has bring advantages for the estimated model. The influence of unobserved covariates is incorporated using a proportional hazard model. The frailty term assumed to follow a gamma distribution is introduced on the hazard rate to control the unobservable heterogeneity of the patients. We assume that the baseline distribution follows a Gompertz and inverse Gaussian defective distributions. Thus we propose and discuss two defective distributions: the defective gamma-Gompertz and gamma-inverse Gaussian regression models. Simulation studies are performed to verify the asymptotic properties of the maximum likelihood estimator. Lastly, in order to illustrate the proposed model, we present three applications in real data sets, in which one of them we are using for the first time, related to a study about breast cancer in the A.C.Camargo Cancer Center, São Paulo, Brazil.     

Adaptive Deconvolution on the Non‐negative Real Line

In this paper, we consider the problem of adaptive density or survival function estimation in an additive model defined by Z=X+Y with X independent of Y, when both random variables are non‐negative. This model is relevant, for instance, in reliability fields where we are interested in the failure time of a certain material that cannot be isolated from the system it belongs. Our goal is to recover the distribution of X (density or survival function) through n observations of Z, assuming that the distribution of Y is known. This issue can be seen as the classical statistical problem of deconvolution that has been tackled in many cases using Fourier‐type approaches. Nonetheless, in the present case, the random variables have the particularity to be supported. Knowing that, we propose a new angle of attack by building a projection estimator with an appropriate Laguerre basis. We present upper bounds on the mean squared integrated risk of our density and survival function estimators. We then describe a non‐parametric data‐driven strategy for selecting a relevant projection space. The procedures are illustrated with simulated data and compared with the performances of a more classical deconvolution setting using a Fourier approach. Our procedure achieves faster convergence rates than Fourier methods for estimating these functions.     

干涉ISAR三维成像试验研究

阐述了干涉式逆合成孔径雷达(ISAR)三维成像的基本原理,与传统的ISAR二维成像方法的性能进行了比较.结合实际的宽带雷达平台,介绍了试验验证的基本方法,并通过静态双散射点和过航飞机等目标的实际跟踪试验,获取了目标回波信号的和、方位差和俯仰差三通道宽带数据,给出了干涉ISAR三维成像的算法流程.采用干涉ISAR三维成像的算法对实际测量数据进行了处理,得到了各类目标的成像结果,对成像效果进行了分析和评估,提出了工程应用的建议.     

The weighted general linear model for longitudinal medical cost data – an application in colorectal cancer

Identifying cost-effective decisions that can take into account of medical cost and health outcome is an important issue under very limited resources. Analyzing medical costs has been challenged owing to skewness of cost distributions, heterogeneity across samples and censoring. When censoring is due to administrative reasons, the total cost might be related to the survival time since longer survivals are likely to be censored and the corresponding total cost will be censored as well. This paper uses the general linear model for the longitudinal data to model the repeated medical cost data and the weighted estimating equation is used to find more accurate estimates for the parameter. Furthermore, the asymptotic properties for the proposed model are discussed. Simulations are used to evaluate the performance of estimators under various scenarios. Finally, the proposed model is implemented on the data extracted from National Health Insurance database for patients with the colorectal cancer.     

Bayesian prediction of unobserved values for Type-II censored data

In this paper, we consider posterior predictive distributions of Type-II censored data for an inverse Weibull distribution. These functions are given by using conditional density functions and conditional survival functions. Although the conditional survival functions were expressed by integral forms in previous studies, we derive the conditional survival functions in closed forms and thereby reduce the computation cost. In addition, we calculate the predictive confidence intervals of unobserved values and coverage probabilities of unobserved values by using the posterior predictive survival functions.