论跨越历史阶段与中国社会主义发展

人类社会每一个历史阶段 ,都是以生产力的发展状况为主要特征的 ,不能主观地跨越历史阶段。但是 ,历史上曾有过跨越历史阶段发展的国家或民族 ,但都不是“纯粹”的跨越。中国的社会主义跨越式发展也不是“纯粹”的 ,它和资本主义生产力有着内在的必然联系 ,为了推动中国现实生产力的发展 ,使中国的社会主义变得更加完善 ,共产党制定出一系列的方针和政策 ,特别是加入WTO之后 ,中国的经济广泛地和世界经济融为一体 ,将谱写出世界史上前所未有的发展特色     

The multitype branching random walk,I

The limiting behaviour of the multitype branching random walk is studied. A limit theorem is proven for the supercritical process. Steady-state distributions are shown to exist for the subcritical process with immigration, and for the critical transient process beginning with Poisson random fields. An analogue of the exponential limit law is proven for the critical process whose migration process is Brownian motion in two dimensions.     

Estimation of sensitivity level of personal interview survey questions

In this paper, an optional randomized response model is proposed. The estimator of the mean of the stigmatized variable based on the optional randomized response sampling is shown to be more efficient than the usual estimator of the mean based on randomized response technique method. In addition to estimating population mean and variance, it has been shown that the optional randomized response technique is useful in quantifying the sensitivity levels of the questions in the personal interview surveys. An estimator for the sensitivity level of a question is proposed and an empirical study is carried out to show the validity of the proposed estimation technique. We also propose a test for the sensitivity level of the stigmatized variable.     

Parameter and quantile estimation for the three-parameter lognormal distribution based on statistics invariant to unknown location

Lognormal distribution is one of the popular distributions used for modelling positively skewed data, especially those encountered in economic and financial data. In this paper, we propose an efficient method for the estimation of parameters and quantiles of the three-parameter lognormal distribution, which avoids the problem of unbounded likelihood, by using statistics that are invariant to unknown location. Through a Monte Carlo simulation study, we then show that the proposed method performs well compared to other prominent methods in terms of both bias and mean-squared error. Finally, we present two illustrative examples.     

Moment identity for discrete random variable and its applications

In this paper, we obtain a moment identity applicable to a general class of discrete probability distributions. We then derive the corresponding identities for modified power series, Ord and Katz families. It is noted that the proposed identity has potential applications in different fields.     

On the Asymptotic Optimality of Empirical Likelihood for Testing Moment Restrictions

We show by example that empirical likelihood and other commonly used tests for moment restrictions are unable to control the (exponential) rate at which the probability of a Type I error tends to zero unless the possible distributions for the observed data are restricted appropriately. From this, it follows that for the optimality claim for empirical likelihood in Kitamura (2001) to hold, additional assumptions and qualifications are required. Under stronger assumptions than those in Kitamura (2001), we establish the following optimality result: (i) empirical likelihood controls the rate at which the probability of a Type I error tends to zero and (ii) among all procedures for which the probability of a Type I error tends to zero at least as fast, empirical likelihood maximizes the rate at which the probability of a Type II error tends to zero for most alternatives. This result further implies that empirical likelihood maximizes the rate at which the probability of a Type II error tends to zero for all alternatives among a class of tests that satisfy a weaker criterion for their Type I error probabilities.     

Inference for Parameters Defined by Moment Inequalities: A Recommended Moment Selection Procedure

This paper is concerned with tests and confidence intervals for parameters that are not necessarily point identified and are defined by moment inequalities. In the literature, different test statistics, critical‐value methods, and implementation methods (i.e., the asymptotic distribution versus the bootstrap) have been proposed. In this paper, we compare these methods. We provide a recommended test statistic, moment selection critical value, and implementation method. We provide data‐dependent procedures for choosing the key moment selection tuning parameter κ and a size‐correction factor η.     

Estimation of parameters of the gamma distribution in the presence of outliers

The maximum likelihood estimators and moment estimators are derived for samples from the Gamma distribution in the presence of outliers. These estimators are compared empirically when all the three parameters are unknown and when one of the three parameters is known; their bias and mean square error (MSE) are investigated with the help of numerical technique.     

Generalized gamma parameter estimation and moment evaluation

The generalized gamma distribution includes the exponential distribution, the gamma distribution, and the Weibull distribution as special cases. It also includes the log-normal distribution in the limit as one of its parameters goes to infinity. Prentice (1974) developed an estimation method that is effective even when the underlying distribution is nearly log-normal. He reparameterized the density function so that it achieved the limiting case in a smooth fashion relative to the new parameters. He also gave formulas for the second partial derivatives of the log-density function to be used in the nearly log-normal case. His formulas included infinite summations, and he did not estimate the error in approximating these summations.

We derive approximations for the log-density function and moments of the generalized gamma distribution that are smooth in the nearly log-normal case and involve only finite summations. Absolute error bounds for these approximations are included. The approximation for the first moment is applied to the problem of estimating the parameters of a generalized gamma distribution under the constraint that the distribution have mean one. This enables the development of a correspondence between the parameters in a mean one generalized gamma distribution and certain parameters in acoustic scattering theory.     

On the distributions of the inner distance functions in the multivariate normal populations with unequal covariance matrices

The sample distance functions between an observation and a population were deduced by the likelihood procedures for discrimination problem in the case of several normal populations with unequal covariance matrices(1986). The present paper gives the exact MGFs of the distance functions for the case that the observation and the sample come from the same population and the limiting distributions of the distance functions by using the MCFs.