A BAYESIAN SIMULATION APPROACH TO INFERENCE ON A MULTI‐STATE LATENT FACTOR INTENSITY MODEL

The influence of economic conditions on the movement of a variable between states (for example a change in credit rating from A to B) can be modelled using a multi‐state latent factor intensity framework. Estimation of this type of model is, however, not straightforward, as transition probabilities are involved and the model contains a few highly analytically intractable distributions. In this paper, a Bayesian approach is adopted to manage the distributions. The innovation in the sampling algorithm used to obtain the posterior distributions of the model parameters includes a particle filter step and a Metropolis–Hastings step within a Gibbs sampler. The feasibility and accuracy of the proposed sampling algorithm is supported with a few simulated examples. The paper contains an application concerning what caused 1049 firms to change their credit ratings over a span of ten years.     

Nonparametric methodology for the time-dependent partial area under the ROC curve

To assess the classification accuracy of a continuous diagnostic result, the receiver operating characteristic (ROC) curve is commonly used in applications. The partial area under the ROC curve (pAUC) is one of the widely accepted summary measures due to its generality and ease of probability interpretation. In the field of life science, a direct extension of the pAUC into the time-to-event setting can be used to measure the usefulness of a biomarker for disease detection over time. Without using a trapezoidal rule, we propose nonparametric estimators, which are easily computed and have closed-form expressions, for the time-dependent pAUC. The asymptotic Gaussian processes of the estimators are established and the estimated variance-covariance functions are provided, which are essential in the construction of confidence intervals. The finite sample performance of the proposed inference procedures are investigated through a series of simulations. Our method is further applied to evaluate the classification ability of CD4 cell counts on patient's survival time in the AIDS Clinical Trials Group (ACTG) 175 study. In addition, the inferences can be generalized to compare the time-dependent pAUCs between patients received the prior antiretroviral therapy and those without it.     

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。     

Bayesian texture segmentation of weed and crop images using reversible jump Markov chain Monte Carlo methods

Summary. A Bayesian method for segmenting weed and crop textures is described and implemented. The work forms part of a project to identify weeds and crops in images so that selective crop spraying can be carried out. An image is subdivided into blocks and each block is modelled as a single texture. The number of different textures in the image is assumed unknown. A hierarchical Bayesian procedure is used where the texture labels have a Potts model (colour Ising Markov random field) prior and the pixels within a block are distributed according to a Gaussian Markov random field, with the parameters dependent on the type of texture. We simulate from the posterior distribution by using a reversible jump Metropolis–Hastings algorithm, where the number of different texture components is allowed to vary. The methodology is applied to a simulated image and then we carry out texture segmentation on the weed and crop images that motivated the work.     

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.     

Semiparametric predictive mean matching

Predictive mean matching is an imputation method that combines parametric and nonparametric techniques. It imputes missing values by means of the Nearest Neighbor Donor with distance based on the expected values of the missing variables conditional on the observed covariates, instead of computing the distance directly on the values of the covariates. In ordinary predictive mean matching the expected values are computed through a linear regression model. In this paper a generalization of the original predictive mean matching is studied. Here the expected values used for computing the distance are estimated through an approach based on Gaussian mixture models. This approach includes as a special case the original predictive mean matching but allows one to deal also with nonlinear relationships among the variables. In order to assess its performance, an empirical evaluation based on simulations is carried out.     

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.     

Sequential design for response surface model fit in computer experiments using derivative information

This article considers the problem of response surface model fit in computer experiment. We propose a new sequential adaptive design through the “maximum expected improvement” approach. The new method defines the improvement by the first order approximation from the known design points using derivative information and sequentially seeks point in area with large curvature and variance. A version with distance penalty is also considered. We demonstrate their superiority over some existing methods by simulation.