十六大报告对马克思主义发展观的新拓展

党的十六大报告全面阐述了关于“发展”的理论思想 ,深刻地揭示了马克思主义发展观的科学内涵 :“三个代表”重要思想是马克思主义发展观“质”的规定 ;“第一要务”是马克思主义发展观的“量”的规定 ;“与时俱进”是马克思主义发展观“度”的把握。同时 ,在人的全面发展是马克思主义发展观的核心思想 ,马克思主义发展观在方针政策和方法论意义上对我国“全面建设小康社会”的指导等理论点上 ,都拓展了马克思主义的发展观。     

反哺农业:寻求社会公平发展的有效途径

文章提出“三农”问题的实质是现代化过程中农民与其他社会利益主体之间的利益关系。目前,我国已进入工业反哺农业的时代。通过构建农村公共服务体系,实现城乡社会公平发展;依靠乡镇企业发展,实现劳动力转移促农,农产品加工和产业化经营促农;建农补农实现反哺促农;小城镇建设与乡镇企业发展相互促进实现以城带乡促农。     

A MODEL OF ETHNIC CONFLICT

We present a model of conflict in which discriminatory government policy or social intolerance is responsive to various forms of ethnic activism, including violence. It is this perceived responsiveness—captured by the probability that the government gives in and accepts a proposed change in ethnic policy—that induces individuals to mobilize, often violently, to support their cause. Yet, mobilization is costly and militants have to be compensated accordingly. The model allows for both financial and human contributions to conflict and allows for a variety of individual attitudes (“radicalism”) towards the cause. The main results concern the effects of within‐group heterogeneity in radicalism and income, as well as the correlation between radicalism and income, in precipitating conflict.     

On asymptotics of multivariate integrals with applications to records

Let { X _n ,n≥1} be a sequence of iid. Gaussian random vectors in R ^d , d≥2, with nonsingular distribution function F. In this paper the asymptotics for the sequence of integrals I _F,n (G _n )?n∫ _{R ^d} G _n ^n?1( X ) dF( X ) is considered with G _n some distribution function on R ^d . In the case G _n =F the integral I _F,n (F)/n is the probability that a record occurs in X ₁,…, X _n at index n. [1] Gnedin, A.V. 1998. Records from a Multivariate Normal Sample. Statist. Probab. Lett., 39: 11–15. [Crossref], [Web of Science ®] , [Google Scholar] obtained lower and upper asymptotic bounds for this case, whereas [2] Ledford, W.A. and Twan, A.J. 1998. On the Tail Concomitant Behaviour for Extremes. Adv. Appl. Probab., 30: 197–215. [Crossref], [Web of Science ®] , [Google Scholar] showed the rate of convergence if d=2. In this paper we derive the exact rate of convergence of I _F,n (G _n ) for d≥2 under some restrictions on the distribution function G _n . Some related results for multivariate Gaussian tails are discussed also.     

Principal components regression estimator and a test for the restrictions

In this article, we introduce restricted principal components regression (RPCR) estimator by combining the approaches followed in obtaining the restricted least squares estimator and the principal components regression estimator. The performance of the RPCR estimator with respect to the matrix and the generalized mean square error are examined. We also suggest a testing procedure for linear restrictions in principal components regression by using singly and doubly non-central F distribution.     

Selecting the Best Exponential Population When the Scale Parameters are Bounded

Consider k (≥ 2) independent exponential populations with different location and scale parameters. Call a population associated with largest of unknown location parameters as the best population. For the goal of selecting the best population, it is established that if the scale parameters are completely unknown, then the indifference-zone probability requirement can not be guaranteed by any single sample decision rule which is just and translation invariant. Under the assumption that the scale parameters are bounded above by a known constant, a single sample selection procedure is proposed for which the indifference-zone probability requirement can be guaranteed. Under the same assumption, 100P*% simultaneous upper confidence intervals for all distances from the largest location parameter are also obtained.     

Testing Hypotheses for Variance Components in Mixed Linear Models

In the paper the problem of testing hypotheses for variance components in mixed linear models is considered. It is assumed that covariance matrices commute after using the usual invariance procedure with respect to the group of translations. The test for vanishing of single variance component is based on the locally best quadratic unbiased estimator of this component and rejects hypothesis if the ratio of positive and negative part of this estimator is sufficiently large. The power of this test with powers of other four tests for two-way classification models corresponding to block design is compared.     

A Remedy for Kernel Estimation Under Random Design

Two common kernel-based methods for non-parametric regression estimation suffer from well-known drawbacks when the design is random. The Gasser-Müller estimator is inadmissible due to its high variance while the Nadaraya-Watson estimator has zero asymptotic efficiency because of poor bias behavior. Under asymptotic consideration, the local linear estimator avoids these two drawbacks of kernel estimators and achieves minimax optimality. However, when based on compact support kernels its finite sample behavior is disappointing because sudden kinks may show up in the estimate.

This paper proposes a modification of the kernel estimator, called the binned convolution estimator leading to a fast O(n) method. Provided the design density is continously differentiable and the conditional fourth moments exist the binned convolution estimator has asymptotic properties identical with those of the local linear estimator.     

Error rate for testing a contrast after a significant F test

In statistical applications an experimenter often tests a particular contrast after a significant F test for the equality of means. This paper evaluates the overall error rate for this testing .procedure.     

A system for computing joint probabilities from jensen's bivariate f distribution

A system of subroutines is presented for efficient computation of joint probabilities from Jensen's bivariate F distribution. Any valid set of parameters is permitted, whereas previous work was limited to the special case of equal numerator degrees of freedom and equal canonical correlations in the underlying multinormal distribution. The use of joint Probabilities from Jensen's bivariate F distribution is demonstrated via an application to two-way ANOVA without interaction.