Simple Robust Tests for Autocorrelated Errors in Time Series Design Intervention Models

A simple, robust test for the autocorrelation parameter in an intervention time-series model (AB design) is proposed. It is analogous to the traditional tests and can easily be computed by using the freeware R. In the same way as traditional tests of autocorrelation are based on least squares (LS) fits of a linear model, our robust test is based on the highly efficient Wilcoxon fit of the linear model. We present the results of a Monte Carlo study which show that our robust test inherits the good efficiency properties of this Wilcoxon fit. Its empirical power is only slightly less than the empirical power of the least squares test over situations with normally distributed errors while it exhibited much more power over situations with error distributions having tails heavier than those of a normal distribution. It also showed robustness of validity over all null situations simulated. We also present the results of the application of our test to a real data set which illustrates the robustness of our test.     

基于组织学习的客户关系管理研究

本文在对组织学习和客户关系管理进行概念界定的基础上,从知识转移角度切入,提出组织学习和客户关系是一种双向互动、良性循环的关系,具体论述了利用组织学习为客户关系增值,并提出了实施意见。     

Testing Goodness of Fit for Parametric Families of Copulas—Application to Financial Data

ABSTRACT

This article suggests a chi-square test of fit for parametric families of bivariate copulas. The marginal distribution functions are assumed to be unknown and are estimated by their empirical counterparts. Therefore, the standard asymptotic theory of the test is not applicable, but we derive a rule for the determination of the appropriate degrees of freedom in the asymptotic chi-square distribution. The behavior of the test under H ₀ and for selected alternatives is investigated by Monte Carlo simulation. The test is applied to investigate the dependence structure of daily German asset returns. It turns out that the Gauss copula is inappropriate to describe the dependencies in the data. A t _ν-copula with low degrees of freedom performs better.     

On the sample redundancy and a test for exponentiality

In this paper we obtain the. sampling properties of a scale-free estimator of "redundancy," an information theoretic measure, which is used by economists and communications engineers. We then propose a new test of exponentiality based upon the sample redun- dancy. This test is asymptotically unbiased against a large class of alternatives, and it performs a t least as well as a recently proposed test with respect to power and asymptotic relative effi- ciency.     

The exact risks of some pre-test and stein-type regression estimators umder balanced loss

In regression analysis we are often interested in using an estimator which is “precise” and which simultaneously provides a model with “good fit”, In this paper we consider the risk properties of several estimators of the regression coefficient vector "trader “balanced” loss, This loss function (Zellner, 1994) reflects both of the described attributes. Under a particular form of balanced loss, we derive the predictive risk of the pre-test estimator which results after a test for exact linear restrictions on the coefficient vector. The corresponding risks of Stein-rule and positive-part Stein-rale estimators are also established. The risks based on loss functions which allow only for estimation precision, or only for goodness of fit, are special cases of our results, and we draw appropriate comparisons, In particular, we show that some of the well-known results under (quadratic) precision-only loss are not robust to our generalization of the loss function     

组织承诺及其对民营企业管理的启示

回顾了组织承诺的研究历程,分析了我国民营企业组织承诺的影响因素,在此基础上,从组织制度、组织公平、组织支持和合作型组织4个方面探讨了组织承诺对民营企业管理的启示。     

家长式领导和组织价值观对成员知识活动的影响机理

根据知识基础的组织理论,从微观层面探索家长式领导和组织价值观对个体知识活动的影响机理。在理论上构建中介、交互和附加3种竞争性模型,提出家长式领导、组织价值观和个体知识活动三者之间的假设关系;采用问卷方式对191名医生进行调查,并采用层级回归分析对以上假设进行检验。实证结果表明,家长式领导和组织价值观对个体知识活动均产生影响,但二者地位取决于知识活动类型。对个体的知识创造活动,领导行为与价值观的影响是替代关系;对个体知识回避活动,二者是互补关系;对个体知识挪用活动,则相互独立。此外,家长式领导的威权领导与组织价值观的成员成长对知识活动同时存在消极和积极影响的悖论效应。     

A tool for systematically comparing the power of tests for normality

In this article, we describe a new approach to compare the power of different tests for normality. This approach provides the researcher with a practical tool for evaluating which test at their disposal is the most appropriate for their sampling problem. Using the Johnson systems of distribution, we estimate the power of a test for normality for any mean, variance, skewness, and kurtosis. Using this characterization and an innovative graphical representation, we validate our method by comparing three well-known tests for normality: the Pearson χ² test, the Kolmogorov–Smirnov test, and the D'Agostino–Pearson K ² test. We obtain such comparison for a broad range of skewness, kurtosis, and sample sizes. We demonstrate that the D'Agostino–Pearson test gives greater power than the others against most of the alternative distributions and at most sample sizes. We also find that the Pearson χ² test gives greater power than Kolmogorov–Smirnov against most of the alternative distributions for sample sizes between 18 and 330.     

An exact Kolmogorov–Smirnov test for the Poisson distribution with unknown mean

We develop an exact Kolmogorov–Smirnov goodness-of-fit test for the Poisson distribution with an unknown mean. This test is conditional, with the test statistic being the maximum absolute difference between the empirical distribution function and its conditional expectation given the sample total. Exact critical values are obtained using a new algorithm. We explore properties of the test, and we illustrate it with three examples. The new test seems to be the first exact Poisson goodness-of-fit test for which critical values are available without simulation or exhaustive enumeration.     

Inference for a leptokurtic symmetric family of distributions represented by the difference of two gamma variates

We introduce a family of leptokurtic symmetric distributions represented by the difference of two gamma variates. Properties of this family are discussed. The Laplace, sums of Laplace and normal distributions all arise as special cases of this family. We propose a two-step method for fitting data to this family. First, we perform a test of symmetry, and second, we estimate the parameters by minimizing the quadratic distance between the real parts of the empirical and theoretical characteristic functions. The quadratic distance estimator obtained is consistent, robust and asymptotically normally distributed. We develop a statistical test for goodness of fit and introduce a test of normality of the data. A simulation study is provided to illustrate the theory.