Improved estimation of the mean vector for student-t model

Improved James-Stein type estimation of the mean vector μ of a multovaroate Student-t population of dimension p with ν degrees of freedom is considered. In addition to the sample data, uncertain prior information on the value of the mean vector, in the form of a null hypothesis, is used for the estiamtion. The usual maximum liklihood estimator((mle) of μ is obtained and a test statistic for testing H₀:μ=μ₀ is derived. Based on the mle of μ and the tes statistic the preliminary test estimator (PTE), Stein-type shrinkage estimator (SE) and positive-rule shrinkage esiimator (PRSE) are defined. The bias and the quadratic risk of the estimators are evaiuated. The relative performances of the estimators are mvestigated by analyzing the risks under different condltlons It is observed that the FRSE dommates over he other three estimators, regardless of the vaiidity of the null hypothesis and the value ν.     

Krylov Sequences as a Tool for Analysing Iterated Regression Algorithms

Abstract.  We use Krylov sequences to analyse a class of regression methods based on successive identification of latent factors. Some results already proved for partial least squares regression (PLSR) are shown to hold for other methods also. We prove that the well-known peculiar pattern of alternating shrinkage and inflation of the principal components is not unique for PLSR. We also show that for any method in the class under study, the coefficient of determination is always at least as high as for principal components regression with the same number of factors.     

On shrinkage estimation of the exponential location parameter

Under the assumption that the exponential distribution is a reasonable model for a given population, some shrinkage estimators for the location parameter based on type 1 and type II censored samples have been derived. It is shown that these estimators dominate maximum likelihood estimators (MLE's) asymptotically under the mean squared error (MSE) criterion. A Monte Carlo study shows a significant improvement of our estimators over MLE's in terms of MSE for small samples.     

掺合料对混凝土体积稳定性的影响

以实际铁路大桥工程为背景,以解决大体积混凝土开裂问题为目的,通过试验分析了矿粉、粉煤灰对水泥化学收缩和干燥收缩的影响,结果显示：内掺50%的矿粉能够大幅度降低早期塑性阶段的化学收缩,对减少后期的化学收缩也有一定作用,矿粉与粉煤灰复合后对降低7 d以内的化学收缩都有显著作用,粉煤灰比例越高,作用越明显;单掺加矿粉会增加干燥收缩值,而采用矿粉与粉煤灰复掺的方法则能够有效提高体积稳定性。根据掺合料对混凝土强度影响的试验分析,并结合工程要求和化学收缩、干燥收缩试验结果与分析,提出了优化大桥原设计混凝土配合比的方案,经过实际施工检验,有效避免了大体积混凝土开裂的出现。     

混凝土干缩应力建模及试验研究

通过建立混凝土毛细孔液面水压力和相对湿度的关系,确定了计算理想状态的自由收缩混凝土和实际工程的全约束混凝土两类结构的干缩应力模型。采用自由收缩和全约束混凝土两个试件,对全约束试件两端施加拉力,且使试件经历加卸载循环,测量了自由收缩和全约束混凝土不同深度的干缩应力和干缩应力产生裂纹的深度,分析了两种试件干缩应力随龄期变化的规律。并通过比较两试件干缩应力图,分析了两类混凝土的干缩应力关系,得出了全约束混凝土的平均干缩应力值比自由收缩混凝土的平均干缩应力值高1MPa的结论。     

建构服务型政府的创新路径

中国社会转型的一个主要内容,就是由传统管制型政府模式转变为现代服务型政府模式,其主要理由在于:首先是建设现代民主法制国家的内在要求;其次是中国市场经济发展的必然选择;再次是提升政府效能的根本保证;第四是有效应对WTO的基础工程。建构现代服务型政府的主要创新路径是:政府职能在入世后不仅要有退却和收缩,而更需要转变和强化。政府职能转换、演化、创新和变迁的基本路径(path),应该是结构性退却,空间性收缩,功能性转换,公共性强化。只有如此,才能尽快建构起适应现代市场经济要求的行为规范、运转协调、公正透明、廉洁高效的政府体系,才能实现政府职能现代化,并在此引领下实现其他社会各领域的现代化。     

Multivariate Bayes Wavelet shrinkage and applications

In recent years, wavelet shrinkage has become a very appealing method for data de-noising and density function estimation. In particular, Bayesian modelling via hierarchical priors has introduced novel approaches for Wavelet analysis that had become very popular, and are very competitive with standard hard or soft thresholding rules. In this sense, this paper proposes a hierarchical prior that is elicited on the model parameters describing the wavelet coefficients after applying a Discrete Wavelet Transformation (DWT). In difference to other approaches, the prior proposes a multivariate Normal distribution with a covariance matrix that allows for correlations among Wavelet coefficients corresponding to the same level of detail. In addition, an extra scale parameter is incorporated that permits an additional shrinkage level over the coefficients. The posterior distribution for this shrinkage procedure is not available in closed form but it is easily sampled through Markov chain Monte Carlo (MCMC) methods. Applications on a set of test signals and two noisy signals are presented.     

Ridge Estimation under the Stochastic Restriction

In linear programming and modeling of an economic system, there may occur some linear stochastic artificial or unnatural manners, which may need serious attentions. These stochastic unusual uncertainty, say stochastic constraints, definitely cause some changes in the estimators under work and their behaviors. In this approach, we are basically concerned with the problem of multicollinearity, when it is suspected that the parameter space may be restricted to some stochastic restrictions. We develop the estimation strategy form unbiasedness to some improved biased adjustment. In this regard, we study the performance of shrinkage estimators under the assumption of elliptically contoured errors and derive the region of optimality of each one. Lastly, a numerical example is taken to determine the adequate ridge parameter for each given estimator.     

Bayesiam simultaneous estimation for several multinomial distributions

A Bayesian method is proposed for estimating the cell probabilities of several multinomial distributions. Parameters of different distributions are taken to be a priori exchangeable. The prior specification is based upon mixtures of a hierarchical distribution, referred to as the multivariate “Dirichlet-Dirichlet” distribution. The analysis is facilitated by a multinomial approximation relating to the multinomial-Dirichlet distribution. The posterior estimates depend upon measures of entropy for the various distributions and shrink the individual observed proportions towards values obtained by pooling the data across the distributions. As well as incorporating prior information they are particularly useful when some of the cell frequencies are zero. We use them to investigate a numerical classification of males of various vocations, according to cause of death.     

Shrinkage and penalized estimators in weighted least absolute deviations regression models

In this paper, we consider the estimation problem of the weighted least absolute deviation (WLAD) regression parameter vector when there are some outliers or heavy-tailed errors in the response and the leverage points in the predictors. We propose the pretest and James–Stein shrinkage WLAD estimators when some of the parameters may be subject to certain restrictions. We derive the asymptotic risk of the pretest and shrinkage WLAD estimators and show that if the shrinkage dimension exceeds two, the asymptotic risk of the shrinkage WLAD estimator is strictly less than the unrestricted WLAD estimator. On the other hand, the risk of the pretest WLAD estimator depends on the validity of the restrictions on the parameters. Furthermore, we study the WLAD absolute shrinkage and selection operator (WLAD-LASSO) and compare its relative performance with the pretest and shrinkage WLAD estimators. A simulation study is conducted to evaluate the performance of the proposed estimators relative to that of the unrestricted WLAD estimator. A real-life data example using body fat study is used to illustrate the performance of the suggested estimators.