KS型议程的行波解讨论

本文利用齐次平衡法确定了KS型方程双曲正切函数解的参数所满足的方程组，并且展开相关讨论，如给出了解的几种特殊情况及双曲正切函数法的适定条件等，为非线性发展方程的行波解（孤立波解）的求出给出了具有参考价值的理论与实例。所采用的方法具有普遍性。     

一类变系数高阶线性齐次微分方程解的探求(英文)

本文对系数全为多项式和广义多项式的n阶线性齐次微分方程引入特征方程的概念。给出了具有指数型解的充要条件，推广了经典的常系数线性方程和著名的Euler方程的的解法，为求解变系数线性微分方程提供了有效的方法。     

生产函数与成本函数的关系研究

本文给出了生产函数与成本函数之间的一般关系,并证明齐次生产函数与齐次成本函数之间存在着一种齐次对应关系:齐次生产函数在厂商的成本最小化决策下必然产生齐次的成本函数且齐次成本函数必然产生对应于齐次的生产函数。     

A new generalization of alpha-skew-normal distribution

In this paper, a new generalization of alpha-skew-normal distribution is considered. Some properties of this distribution, which is denoted by GASN(α, λ), including moments, maximum likelihood estimation of parameters, and some other properties are studied. Finally, using a real data set, we show that our new distribution is the best-fitted distribution for the used data among normal, skew normal, alpha-skew-normal, and skew-bimodal-normal distributions.     

Estimation and diagnostic analysis in skew-generalized-normal regression models

The skew-generalized-normal distribution [Arellano-Valle, RB, Gómez, HW, Quintana, FA. A new class of skew-normal distributions. Comm Statist Theory Methods 2004;33(7):1465–1480] is a class of asymmetric normal distributions, which contains the normal and skew-normal distributions as special cases. The main virtues of this distribution is that it is easy to simulate from and it also supplies a genuine expectation–maximization (EM) algorithm for maximum likelihood estimation. In this paper, we extend the EM algorithm for linear regression models assuming skew-generalized-normal random errors and we develop a diagnostics analyses via local influence and generalized leverage, following Zhu and Lee's approach. This is because Cook's well-known approach would be more complicated to use to obtain measures of local influence. Finally, results obtained for a real data set are reported, illustrating the usefulness of the proposed method.     

电子商务市场产品多元化战略的销售绩效及驱动因素研究

结合电子商务市场特征,从理论方面分析电子商务市场产品多元化战略的驱动因素及其销售绩效,利用正则表达式等数据挖掘技术形成电子商务市场的面板数据库,并对相关研究假设进行实证检验。结果表明：在电子商务市场上,产品多元化战略与企业销售绩效正相关,而市场同质化竞争程度与产品多元化战略负相关,企业经营时间、顾客好评率抑制了企业对产品多元化战略的选择。研究结论对于电子商务市场的企业产品多元化战略选择具有重要参考意义。     

Measurement error, skewness, and risk analysis: coping with the long tail of the distribution.

Probabilistic risk analyses often construct multistage chance trees to estimate the joint probability of compound events. If random measurement error is associated with some or all of the estimates, we show that resulting estimates of joint probability may be highly skewed. Joint probability estimates based on the analysis of multistage chance trees are more likely than not to be below the true probability of adverse events, but will sometimes substantially overestimate them. In contexts such as insurance markets for environmental risks, skewed distributions of risk estimates amplify the "winner's curse" so that the estimated risk premium for low-probability events is likely to be lower than the normative value. Skewness may result even in unbiased estimators of expected value from simple lotteries, if measurement error is associated with both the probability and pay-off terms. Further, skewness may occur even if the error associated with these two estimates is symmetrically distributed. Under certain circumstances, skewed estimates of expected value may result in risk-neutral decisionmakers exhibiting a tendency to choose a certainty equivalent over a lottery of equal expected value, or vice versa. We show that when distributions of estimates of expected value are, positively skewed, under certain circumstances it will be optimal to choose lotteries with nominal values lower than the value of apparently superior certainty equivalents. Extending the previous work of Goodman (1960), we provide an exact formula for the skewness of products.     

Skew-normal factor analysis models with incomplete data

Traditional factor analysis (FA) rests on the assumption of multivariate normality. However, in some practical situations, the data do not meet this assumption; thus, the statistical inference made from such data may be misleading. This paper aims at providing some new tools for the skew-normal (SN) FA model when missing values occur in the data. In such a model, the latent factors are assumed to follow a restricted version of multivariate SN distribution with additional shape parameters for accommodating skewness. We develop an analytically feasible expectation conditional maximization algorithm for carrying out parameter estimation and imputation of missing values under missing at random mechanisms. The practical utility of the proposed methodology is illustrated with two real data examples and the results are compared with those obtained from the traditional FA counterparts.     

Modeling healthcare costs in simultaneous presence of asymmetry,heteroscedasticity and correlation

Highly skewed outcome distributions observed across clusters are common in medical research. The aim of this paper is to understand how regression models widely used for accommodating asymmetry fit clustered data under heteroscedasticity. In a simulation study, we provide evidence on the performance of the Gamma Generalized Linear Mixed Model (GLMM) and log-Linear Mixed-Effect (LME) model under a variety of data-generating mechanisms. Two case studies from health expenditures literature, the cost of strategies after myocardial infarction randomized clinical trial on the cost of strategies after myocardial infarction and the European Pressure Ulcer Advisory Panel hospital prevalence survey of pressure ulcers, are analyzed and discussed. According to simulation results, the log-LME model for a Gamma response can lead to estimations that are biased by as much as 10% of the true value, depending on the error variance. In the Gamma GLMM, the bias never exceeds 1%, regardless of the extent of heteroscedasticity, and the confidence intervals perform as nominally stated under most conditions. The Gamma GLMM with a log link seems to be more robust to both Gamma and log-normal generating mechanisms than the log-LME model.     

Simultaneous bootstrap confidence bands in regression

We suggest pivotal methods for constructing simultaneous bootstrap confidence bands in regression. Most attention is given to the problem of simple linear regression, but our techniques admit trivial extension to other cases, including polynomial regression. The advantages of our bootstrap approach are twofold. Firstly, the bootstrap allows a very general distribution for the errors, and secondly, it admits a wide variety of shapes for the confidence band. In our technique the shape of each envelope of the band is determined by a general template, chosen by the experimenter, and bootstrap methods are used to select the scale of the template.