Pseudo-perfect and adaptive variants of the Metropolis–Hastings algorithm with an independent candidate density

We describe and examine an imperfect variant of a perfect sampling algorithm based on the Metropolis–Hastings algorithm that appears to perform better than a more traditional approach in terms of speed and accuracy. We then describe and examine an ‘adaptive’ Metropolis–Hastings algorithm which generates and updates a self-target candidate density in such a way that there is no ‘wrong choice’ for an initial candidate density. Simulation examples are provided.     

Minimum variance unbiased invariant estimation of variance components under normality

In a variance components model for normally distributed data, for a specified vector of linear combinations of the variance components, necessary and sufficient conditions are given under which the vector has a uniformly minimum variance unbiased translation-invariant estimator. The competing class of estimators is not restricted to those that are quadratic. For classification models, the conditions are translated into easy-to-check partial balance requirements on the incidence array.     

Dealing with big data: comparing dimension reduction and shrinkage regression methods

In the past decades, the number of variables explaining observations in different practical applications increased gradually. This has led to heavy computational tasks, despite of widely using provisional variable selection methods in data processing. Therefore, more methodological techniques have appeared to reduce the number of explanatory variables without losing much of the information. In these techniques, two distinct approaches are apparent: ‘shrinkage regression’ and ‘sufficient dimension reduction’. Surprisingly, there has not been any communication or comparison between these two methodological categories, and it is not clear when each of these two approaches are appropriate. In this paper, we fill some of this gap by first reviewing each category in brief, paying special attention to the most commonly used methods in each category. We then compare commonly used methods from both categories based on their accuracy, computation time, and their ability to select effective variables. A simulation study on the performance of the methods in each category is generated as well. The selected methods are concurrently tested on two sets of real data which allows us to recommend conditions under which one approach is more appropriate to be applied to high-dimensional data.     

预编码和子空间跟踪的OFDM信道盲估计

讨论了一种OFDM信道盲估计方法,首先对进入OFDM系统前的相邻两个OFDM符号进行线性预编码,利用预编码引入的数据结构在接收端构造了一个新的矩阵,使得OFDM频域信道响应成为该矩阵的一维子空间,最后通过自适应子空间跟踪技术实现了信道的盲估计。仿真结果表明该方法能有效地抑制信号分布波动、时变信道及噪声对信道估计的影响,从而有效地降低了OFDM系统的信道估计平均均方误差和无编码码元出错率。     

广义特征子空间

本文对广义特征向量和广义特征子空间进行刻划，得到ｎ维线性空间的一组基构成过渡矩阵，使任一ｎ级复数矩阵相似于它的约旦（Ｊｏｒｄａｎ）形矩阵．     

On geometry of the set of admissible invariant quadratic estimators in balanced two variance components model

Two variance components model for which each invariant quadratic admissible estimator of a linear function of variance components (under quadratic loss function) is a linear combination of two quadratic forms,Z ₁,Z ₂, say, is considered. A setD={(d ₁,d ₂)′:d ₁ Z ₁+d ₂ Z ₂ is admissible} is described by giving formulae on the boundary ofD. Different forms of the setD are presented on figures.     

A theory of statistical models for Monte Carlo integration

Summary. The task of estimating an integral by Monte Carlo methods is formulated as a statistical model using simulated observations as data. The difficulty in this exercise is that we ordinarily have at our disposal all of the information required to compute integrals exactly by calculus or numerical integration, but we choose to ignore some of the information for simplicity or computational feasibility. Our proposal is to use a semiparametric statistical model that makes explicit what information is ignored and what information is retained. The parameter space in this model is a set of measures on the sample space, which is ordinarily an infinite dimensional object. None-the-less, from simulated data the base-line measure can be estimated by maximum likelihood, and the required integrals computed by a simple formula previously derived by Vardi and by Lindsay in a closely related model for biased sampling. The same formula was also suggested by Geyer and by Meng and Wong using entirely different arguments. By contrast with Geyer's retrospective likelihood, a correct estimate of simulation error is available directly from the Fisher information. The principal advantage of the semiparametric model is that variance reduction techniques are associated with submodels in which the maximum likelihood estimator in the submodel may have substantially smaller variance than the traditional estimator. The method is applicable to Markov chain and more general Monte Carlo sampling schemes with multiple samplers.     

线性空间的幂等变换与对合变换的几个等价表示

给出数域F上线性空间(不一定是有限维的)的线性变换是对合变换与幂等变换的几个等价描述.作为其推论,得到数域F上n阶方阵是对合矩阵与幂等矩阵的几个等价描述.     

Hidden Markov model for discrete circular–linear wind data time series

ABSTRACT

In this work, we deal with a bivariate time series of wind speed and direction. Our observed data have peculiar features, such as informative missing values, non-reliable measures under a specific condition and interval-censored data, that we take into account in the model specification. We analyse the time series with a non-parametric Bayesian hidden Markov model, introducing a new emission distribution, suitable to model our data, based on the invariant wrapped Poisson, the Poisson and the hurdle density. The model is estimated on simulated datasets and on the real data example that motivated this work.     

On the Properties of the SAVE Directions

In quadratic discriminant analysis, the use of SAVE (Cook and Weisberg, 1991 Cook, R.D., Weisberg, S. (1991). Discussion of Li (1991). J. Amer. Statist. Assoc. 86:328–32.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]; Pardoe et al., 2007 Pardoe, I., Yin, X., Cook, R. (2007). Graphical tools for quadratic discriminant analysis. Technometrics 49:172–183.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) is often recommended for dimension-reduction purposes. However, the associated directions tend to over-emphasize the differences of the groups in dispersion, ignoring at the same time those in location. This behavior makes often the plots of the corresponding canonical coordinates difficult to interpret. In this article, the properties of SAVE are investigated and related to those of the SIR and SIRII components. Applications with real data are presented. Comparisons with previous work in this area are also discussed.