关于普赖斯曲线方程中参数估计方法的改进

罗式胜在“关于普赖斯曲线方程中参数的讨论”一文中提出了估计普赖斯曲线方程中参数的两个方法,但具体计算中存在缺乏量化标准的缺陷。利用概率统计中的2 σ准则给出一个确定参数的改进方法,引入去除异常数据的量化标准,即使其更具有合理性和可操作性。     

中外合资企业跨文化管理的"钻石结构"模型研究--大亚湾核电跨文化管理的探索

本文在继承传统跨文化管理--从国家文化角度分析管理问题的基础上,重点从微观层面出发建立了一个跨文化管理的"钻石结构"模型,并运用这个结构模型来具体分析了大亚湾核电跨文化管理的实践活动,并对大亚湾核电跨文化管理的主要措施进行了探讨,希望这些结论能够对未来中外合资合作企业进行跨文化管理提供有益的参考和借鉴作用.     

Some characterizations of distributions of the exponential-type

We consider a particular subclass of the two-parameter exponential family with natural parameters γ₁, γ₂ and characterize those distributions of the family having a ratio of the mean value and the variance that is a linear function of γ₁ by the form of the moment generating function. As special cases we find the normal and the gamma distributions.     

清初江南文坛风尚与才子佳人小说的创作

清初才子佳人小说在江南盛行,该类型的小说创作多注重小说的叙事结构,这与当时江南文坛受八股制艺影响,热衷于探讨作品艺术结构的风尚密切相关。     

会计契约耦合与资本结构优化

会计等式所具有的契约内涵使其成为会计契约与资本结构的桥梁,企业资本结构由此成为各会计契约之间的耦合与延展,资本结构优化实际上是各会计契约之间的转换与权衡.中国特殊的国有企业资本结构形成原因决定了对资本结构的调整与优化应该着眼于债转股与股转债的阶段性结合运用,并考虑国有企业战略性退出的特定需要.     

Test for Parameter Change in Linear Processes Based on Whittle's Estimator

In this article, we develop a cusum test for testing for parameter changes in linear processes based on Whittle's estimator. It is shown that under regularity conditions, the test statistic converges to the sup of a Brownian bridge. The result is particularly useful in handling the change point test in stationary ARMA processes. A simulation result is provided for illustration.     

Invariance Theorems for Fisher Information

In multivariate and multi-parameter contexts, new expressions for Fisher Information are derived using the copula representation of the joint distribution of random variables. Invariance of Fisher Information to margins of the joint distribution is then demonstrated.     

Dynamic Copula-Based Markov Time Series

This article examines a test procedure for checking the constancy of serial dependence via copulas for Markov time series data. It also provides a copula-based modeling approach for the dynamic serial dependence. Various parametric families of copulas offering different dependent structures are investigated. A score test is proposed for checking the constancy of a copula parameter. The score test is constructed and its asymptotic null distribution established under a two-stage estimation procedure. The test does not require specification of the probability distribution for the copula parameter. To capture the dynamics of dependence structure over time, autoregressive moving average and exponential type models are proposed. Illustrations are given based on simulated data and historic coffee prices data.     

Robust Confidence Intervals for the Bernoulli Parameter

Despite the simplicity of the Bernoulli process, developing good confidence interval procedures for its parameter—the probability of success p—is deceptively difficult. The binary data yield a discrete number of successes from a discrete number of trials, n. This discreteness results in actual coverage probabilities that oscillate with the n for fixed values of p (and with p for fixed n). Moreover, this oscillation necessitates a large sample size to guarantee a good coverage probability when p is close to 0 or 1.

It is well known that the Wilson procedure is superior to many existing procedures because it is less sensitive to p than any other procedures, therefore it is less costly. The procedures proposed in this article work as well as the Wilson procedure when 0.1 ≤p ≤ 0.9, and are even less sensitive (i.e., more robust) than the Wilson procedure when p is close to 0 or 1. Specifically, when the nominal coverage probability is 0.95, the Wilson procedure requires a sample size 1, 021 to guarantee that the coverage probabilities stay above 0.92 for any 0.001 ≤ min {p, 1 ?p} <0.01. By contrast, our procedures guarantee the same coverage probabilities but only need a sample size 177 without increasing either the expected interval width or the standard deviation of the interval width.     

On Unimodal Regression in the Exponential Family

In this article, we discuss statistical methods for curve-estimation under the assumption of unimodality for variables with distributions belonging to the two-parameter exponential family with known or constant dispersion parameter. An important special case is a one-parameter distribution. We suggest a nonparametric method based on monotonicity properties. The method is applied to Swedish data on laboratory verified diagnoses of influenza and data on inflation from an episode of hyperinflation in Bulgaria.