Estimation of the location and scale parameters of the extreme value distmbution based on multiply type-II censored samples

In this paper, we consider the problem of estimating the location and scale parameters of an extreme value distribution based on multiply Type-II censored samples. We first describe the best linear unbiased estimators and the maximum likelihood estimators of these parameters. After observing that the best linear unbiased estimators need the construction of some tables for its coefficients and that the maximum likelihood estimators do not exist in an explicit algebraic form and hence need to be found by numerical methods, we develop approximate maximum likelihood estimators by appropriately approximating the likelihood equations. In addition to being simple explicit estimators, these estimators turn out to be nearly as efficient as the best linear unbiased estimators and the maximum likelihood estimators. Next, we derive the asymptotic variances and covariance of these estimators in terms of the first two single moments and the product moments of order statistics from the standard extreme value distribution. Finally, we present an example in order to illustrate all the methods of estimation of parameters discussed in this paper.     

A property of coefficients of variation for ordered bivariate log-normal variables

This paper considers the maximum and minimum of a pair of log-normal variables with equal mean. It shows that either order statistic has a smaller coefficient of variation than the two original log-normal variables provided the latter are of equal variance. When the variances are unequal, as the variance ratio increases, the minimum (maximum), has a smaller coefficient of variation if the correlation coefficient of the log-normal variables is small (small) and the variances are large (small).     

Best linear predictors for factor scores

From the literature three types of predictors for factor scores are available. These are characterized by the constraints: linear, linear conditionally unbiased, and linear correlation preserving. Each of these constraints generates a class of predictors. Best predictors are defined in terms of Lowner's partial matrix order applied to matrices of mean square error of prediction. It is shown that within the first two classes a best predictor exists and that it does not exist in the third.     

Predictive inference for linear and multivariate linear models with ma(1) error processes

The prediction distributions of future responses from the linear and multivariate linear models with errors having a first order moving average (MA(1)) process have been derived. First, we obtained the marginal likelihood function for the moving average parameter 6 and from this likelihood function we estimate the maximum likelihood estimates (MLE) of θ. Using the estimated value θ, we have derived the prediction distributions as well as prediction regions for the future responses. An example has been included.     

Tolerance‐based error reduction in multi‐agent bioassays

Some degree of error is inevitable in multi‐agent bioassays regardless of design or measurement technology. Estimation error can be reduced post facto by exploiting the matrix partial ordering of the bioassay survival estimates. The standard method for this is order‐restricted regression (ORR). If the joint action of the bioassay agents admits a tolerance‐based interpretation, additional structure beyond matrix partial ordering is available, leading to a new method of error reduction. This tolerance‐based error reduction (TBER) procedure almost always outperforms ORR. Like ORR, TBER applies to complete factorial bioassay designs and, using weighting, to incomplete designs.     

Reversed preservation of stochastic orders for random minima and maxima with applications

In this paper, reversed preservation properties of right spread order, total time on test order and increasing convex (concave) order when taking random minima and maxima are developed. In this context, reversed preservation properties of some ageing concepts are investigated under parallel (series) systems which are composed of a random number of i.i.d. components. Some applications in reliability and economics are given.     

Adaptive sampling without replacement of clusters

In a common form of adaptive cluster sampling, an initial sample of units is selected by random sampling without replacement and, whenever the observed value of the unit is sufficiently high, its neighboring units are added to the sample, with the process of adding neighbors repeated if any of the added units are also high valued. In this way, an initial selection of a high-valued unit results in the addition of the entire network of surrounding high-valued units and some low-valued “edge” units where sampling stops. Repeat selections can occur when more than one initially selected unit is in the same network or when an edge unit is shared by more than one added network. Adaptive sampling without replacement of networks avoids some of this repeat selection by sequentially selecting initial sample units only from the part of the population not already in any selected network. The design proposed in this paper carries this step further by selecting initial units only from the population, exclusive of any previously selected networks or edge units.     

如何实行用人“订单”式教育与培训的新模式

订单式教育与培训,已成为各行业进行教育与培训的新模式,本文主要对建筑行业实行订单式教育与培训的具体方式进行探讨,并介绍我校实行订单式教育与培训的具体做法。     

自然人群的规模及其形成的一些属性 ———来自皖南山区村落人际关系的社会网络分析

人群的"量化"的元素和形式化的特征是社会研究的重点,这些问题在中国农村发展的实践领域如农民组织建设过程中却常被忽略了.文章以社会网络分析方法对自然村落中自然人群的规模和其一些基本构成属性进行研究,发现地缘、情趣、性别和年龄等基本属性在很大程度上影响个体交往的对象和规模,这些特征也是农村发展的干预过程中应该注意到的重要社会特征.     

现代政治文明视域下的公民有序政治参与

政治参与是社会主义政治文明的重要标志。社会主义政治文明体现为公民的有序政治参与。公民有序政治参与有助于社会主义民主政治的建设;有助于维护社会稳定,促进社会扣谐;有助于实现民主法制化的进程。扩大公民有序政治参与的途径表现为加强对公民政治参与的正确引导,创建良好的政治文化环境;积极推进政治体制改革,使公民政治参与制度化、程序化;扩大公民政治参与的直接民主领域,真正体现民主的真实内容。