Inferential Causal Models: Integrating Quality & Quantity

In-depth data analysis plus statistical modeling can produce inferentialcausal models. Their creation thus combines aspects of analysis by close inspection,that is, reason analysis and cross-tabular analysis, with statistical analysis procedures,especially those that are special cases of the generalized linear model (McCullaghand Nelder, 1989; Agresti, 1996; Lindsey, 1997). This paper explores some of the roots of this combined method and suggests some new directions. An exercise clarifies some limitations of classic reason analysis by showing how the cross tabulation of variables with controls for test factors may produce better inferences. Then, given the cross tabulation of several variables, by explicating Coleman effect parameters, logistic regressions, and Poisson log-linear models, it shows how generalized linear models provide appropriate measures of effects and tests of statistical significance. Finally, to address a weakness of reason analysis, a case-control design is proposed and an example is developed.     

The Multilevel Approach to Repeated Measures for Complete and Incomplete Data

Repeated measurements often are analyzed by multivariate analysis of variance (MANOVA). An alternative approach is provided by multilevel analysis, also called the hierarchical linear model (HLM), which makes use of random coefficient models. This paper is a tutorial which indicates that the HLM can be specified in many different ways, corresponding to different sets of assumptions about the covariance matrix of the repeated measurements. The possible assumptions range from the very restrictive compound symmetry model to the unrestricted multivariate model. Thus, the HLM can be used to steer a useful middle road between the two traditional methods for analyzing repeated measurements. Another important advantage of the multilevel approach to analyzing repeated measures is the fact that it can be easily used also if the data are incomplete. Thus it provides a way to achieve a fully multivariate analysis of repeated measures with incomplete data. This revised version was published online in June 2006 with corrections to the Cover Date.     

The Random Walk Hypothesis in the Emerging Indian Stock Market

This paper examines the random walk hypothesis in the emerging Indian stock market using daily data on individual stocks. The statistical evidence in this paper rejects the random walk hypothesis. The results suggest that daily returns earned by individual stocks and by an equally weighted portfolio show significant non–linear dependence and persistent volatility effects. The non–linear dependence takes the form of ARCH–type conditional heteroskedasticity and does not appear to be caused by nonstationarity of underlying economic variables. Though conditional volatility is time varying, it does not explain expected returns.     

On the Use of Value Efficiency Analysis and Some Further Developments

The purpose of this paper is to discuss the use of Value Efficiency Analysis (VEA) in efficiency evaluation when preference information is taken into account. Value efficiency analysis is an approach, which applies the ideas developed for Multiple Objective Linear Programming (MOLP) to Data Envelopment Analysis (DEA). Preference information is given through the desirable structure of input- and output-values. The same values can be used for all units under evaluation or the values can be specific for each unit. A decision-maker can specify the input- and output-values subjectively without any support or (s)he can use a multiple criteria support system to assist him/her to find those values on the efficient frontier. The underlying assumption is that the most preferred values maximize the decision-maker's implicitly known value function in a production possibility set or a subset. The purpose of value efficiency analysis is to estimate a need to increase outputs and/or decrease inputs for reaching the indifference contour of the value function at the optimum. In this paper, we briefly review the main ideas in value efficiency analysis and discuss practical aspects related to the use of value efficiency analysis. We also consider some extensions.     

清单计价背景下投标报价策略研究

在建筑市场推行无底招标和清单计价规范的背景下，探讨由业主给定工程量，投标单位填报单价，最后经过验方计价，以单价结算的合同下，投标单位如何根据业主给定工程量可能的变动趋势，基于运筹学的线性规划理论，进行不平衡报价，最大化其收益。     

基于应变数线性变换的约束广义G-M估计

考虑线性变换对约束线性模型回归系数的影响问题，证明了观测数据的线性变换对于约束模型的影响可以通过一个可用两步约束最小二乘法解决的约束回归问题进行了分析，得到了回归系数的约束可估函数的约束最佳线尾无偏估计不受变换影响的充要条件。     

Simulation driven inferences for multiply imputed longitudinal datasets*

In this article, we demonstrate by simulations that rich imputation models for incomplete longitudinal datasets produce more calibrated estimates in terms of reduced bias and higher coverage rates without duly deflating the efficiency. We argue that the use of supplementary variables that are thought to be potential causes or correlates of missingness or outcomes in the imputation process may lead to better inferential results in comparison to simpler imputation models. The liberal use of these variables is recommended as opposed to the conservative strategy.     

上市公司审计收费影响因素的实证研究

本文利用我国深沪两市A股上市公司2003年年报中披露的数据,对可能影响我国上市公司年度审计费用的主要因素,运用多元线性回归进行实证分析。研究结果发现,上市公司资产总额、纳入合并报表的控股子公司数目、其他应收款占总资产的比例、公司和事务所是否位于经济发达地区和董事会规模与年度审计费用显著正相关,而独立董事人数与年度审计费用显著负相关;应收账款占总资产的比例、存货占总资产的比例、审计意见、当年和前一年度的盈利状况等因素对年度审计费用的影响并不显著。因此,我国上市公司审计费用主要由审计的产品费用所决定,从而会计师事务所的审计收费主要反映了现实的审计成本,而审计风险等潜在风险成本并没有在审计收费中体现出来。     

Three Corrected Score Tests for Generalized Linear Models with Dispersion Covariates

We develop three corrected score tests for generalized linear models with dispersion covariates, thus generalizing the results of Cordeiro , Ferrari and Paula (1993) and Cribari-Neto and Ferrari (1995) . We present, in matrix notation, general formulae for the coefficients which define the corrected statistics. The formulae only require simple operations on matrices and can be used to obtain analytically closed-form corrections for score test statistics in a variety of special generalized linear models with dispersion covariates. They also have advantages for numerical purposes since our formulae are readily computable using a language supporting numerical linear algebra. Two examples, namely, iid sampling without covariates on the mean or dispersion parameter oand one-way classification models, are given. We also present some simulations where the three corrected tests perform better than the usual score test, the likelihood ratio test and its Bartlett corrected version. Finally, we present a numerical example for a data set discussed by Simonoff and Tsai (1994) .     

基于主成分分析的多元线性回归用水量模型研究——以安徽省为例

用水量的准确预测对水资源规划具有重要意义。根据统计资料,选取了八项主要影响用水量变化的指标。按照主成分分析法原理,运用少数综合因子对影响用水量变化的原有八项指标进行了综合和简化,并研究其贡献率及综合效应;分析了用水量增长的特点,选用Logistic方程对综合因子进行回归分析;结合多元线性回归方法,建立了安徽省用水量模型;拟合结果表明,模型具有较高的精度;运用该模型,对安徽省2010年的用水量进行了预测;为水资源规划与管理提供参考依据。