英文摘要

The establishment of China＇s basic economic system provides theoretical confidence for the compatibility between socialism and market economy. However, the institutional change from planning system to market system lacks the support of Marxist theoretical confidence. This article holds that the ownership theory of Marxism on productive relations should not undertake this task. However, productive relations require the theoretical support. Since it researches on the exchange and cooperation relationship among people and their order, which are positively consistent with the research object of modem institutional economics. Thus, the creation of Marxist institutional economics, taking the theoretical support for productive relations as the example, can help build Marxist theoretical confidence for the institutional changes in China.     

Accurate Confidence Interval Estimation of Small Area Parameters Under the Fay–Herriot Model

Small area estimation has long been a popular and important research topic due to its growing demand in public and private sectors. We consider here the basic area level model, popularly known as the Fay–Herriot model. Although much of current research is predominantly focused on second order unbiased estimation of mean squared prediction errors, we concentrate on developing confidence intervals (CIs) for the small area means that are second order correct. The corrected CI can be readily implemented, because it only requires quantities that are already estimated as part of the mean squared error estimation. We extend the approach to a CI for the difference of two small area means. The findings are illustrated with a simulation study.     

中间阶层的文化品格及社会功能

中间阶层的文化品格是其文化观念和行为方式的统一体.主要表现为文化自觉--自觉践行和主动追求的理性态度,即独立自主、平等交往、诚实信用、勤勉理性、锐意进取、崇尚民主等.中间阶层以其特殊的文化品格在整个社会中起着缓解社会矛盾、平息思想冲突、引导社会消费等作用,既是社会稳定的基础,也是定型现代化社会结构和利益格局的中坚力量.     

Some comments on a class of simultaneous inference procedures in ancova

Methods for constructing simultaneous confidence intervals for contrasts of treatment effects in analysis of covariance (ANCOVA) models are discussed and compared. A simple procedure is given by which, under a normality assumption made on the covariates, any method appropriate in an analysis of variance (ANOVA) may be applied to a corresponding ANCOVA by means of a simple adjustment of the required critical values. Some properties of this procedure are noted.     

Prediction intervals - a review

This review covers some known results on prediction intervals for univariate distributions. Results for parametric continuous and discrete distributions as well as those based on distribution-free methods are included. Prediction intervals based on Bayesian and sequential methods are not covered. Methods of construction of prediction intervals and other related problems are discussed.     

An alternative decision rule for quality control in sequential sampling plans

The conventional rule for acceptance sampling based on sequential samples is based on the rationale of hypothesis testing developed by Wald (1947). This type of decision rule tests the hypothesis of P=p₁ versus P=p₂ as a proxy for determining whether P>d or P<d with P₁<d<p₂. It requires a zone of indifference between the rejection and acceptance levels p₂ and p₁. In this note, we propose an alternative rule for making the decision based on the confidence level of a one-sided Bayesian interval estimate of the parameter. This method results in direct determination of whether the proportion of defects P in the population is greater or less than a prespecified level d, rather than test two points as proxy for the decision. We present a numerical illustration of the rule and an example of determining rejection and acceptance numbers. Ue also compare the results with two conventional rules.     

Diagnostics for identifying influential cases in manova

Measures of influence of multivariate cases on the estimated parameter matrix in MANOVA are developed. The development is based on the confidence region resulting from the likelihood ratio criterion, and is patterned after the development of Cook's univariate D_Imeasure. Influence measures corresponding to the other 3 common multivariate test criteria are presented. Relationships to the measures of Caroni (1987), and Barrett and Ling (1988) are noted. A numerical example is included.     

Bias in linear model power and sample size due to estimating variance

Planning a study using the General Linear Univariate Model often involves sample size calculation based on a variance estimated in an earlier study. Noncentrality, power, and sample size inherit the randomness. Additional complexity arises if the estimate has been censored. Left censoring occurs when only significant tests lead to a power calculation, while right censoring occurs when only non-significant tests lead to a power calculation. We provide simple expressions for straightforward computation of the distribution function, moments, and quantiles of the censored variance estimate, estimated noncentrality, power, and sample size. We also provide convenient approximations and evaluate their accuracy. The results allow demonstrating that ignoring right censoring falsely widens confidence intervals for noncentrality and power, while ignoring left censoring falsely narrows the confidence intervals. The new results allow assessing and avoiding the potentially substantial bias that censoring may create.     

On the distribution of the estimated mean from nonstandard mixtures of distributions

The distribution of the estimated mean of the nonstandard mixture of distributions that has a discrete probability mass at zero and a gamma distribution for positive values is derived. Furthermore, for the studied nonstandard mixture of distributions, the distribution of the standardized statistic (estimator - true mean)/standard deviation of estimator is derived. The results are used to study the accuracy of the confidence interval for the mean based on a large sample approximation. Quantiles for the standardized statistic are also calculated.     

Estimation from k independent randomly truncated samples: application to neutrino energies

We investigate estimation and testing procedures for the k-sample problem where each of the populations is subject to random truncation by possibly different but known truncation functions. Particular attention is focused on the two sample case which is motivated from the following important application. Neutrinos were detected from Supernova 1987A at two sites: the 1MB detector in Ohio (eight neutrinos observed) and the Kamiokande II detector in Japan (twelve observed). Each detector has different "trigger efficiencies", the chance of observing the flash of light produced by the neutrino knocking an electron loose from an atom. Thus, we have two independent samples of randomly truncated data. We assume a normal model for some power transformation of the data with the same power for each sample. We estimate the parameters of this distribution by maximum likelihood and find confidence regions for the parameters. A Monte Carlo study investigates the properties of the maximum likelihood estimators for this eutrino example.The simulations Show that approximate likelihood-based confidence regions provide coverages much closer to the nominal level than the regions based on asymptotic normal-theory.