Formal governance,interfirm coordination,and performance in partnerships: An empirical investigation of a mediation model

This study investigates the extent to which formal governance mechanisms such as contracts and equity ownership affect interfirm coordination and partnership performance. We analysed data from a survey of 301 Korean firms to show that interfirm coordination partially mediates the relationship between contract completeness, defined as the extent to which a contract specifies task operations and contingencies, and partnership performance. Furthermore, we find that more complete contracts can attenuate the negative effects of equity ownership on coordination, and that this effect is particularly strong when partner tasks are interdependent and relational norms are weak.     

改革所有制结构 促进山西产业结构调整

公有制经济比例过大 ,非公有制经济比例过小是山西所有制结构存在的主要问题 ,它对山西产业结构的调整有着一定的制约作用。发展非公有制经济 ,适度压缩国有经济比例 ,是促进山西产业结构调整的重要举措之一。     

实现公有制与市场经济有效结合的四大层次

因所有制关系是生产关系的总和,所有制必须实现在社会生产再生产的过程之中,因此,我们必须从四大层次实现公有制与市场经济的有效结合。一是所有制结构层次。保持公有制主体地位是保证社会主义市场经济体制有效运作的基础,要大力发展多种所有制经济。二是公有制实现形式层次。更新公有制的微观实现形式,更新公有制的宏观实现形式。三是企业制度层次。建立国有资产出资人制度,合理配置股权,强化所有者约束功能,加强治理结构中的权利组织建设,建立权责分明的组织体系。四是社会剩余产品分配层次。按劳分配与按生产要素分配相结合是通过三个层次实现的:微观经济领域的运行层次,微观经济领域中的所有制结构层次,宏观经济运行层次。     

Type I error rates from likelihood‐based repeated measures analyses of incomplete longitudinal data

The last observation carried forward (LOCF) approach is commonly utilized to handle missing values in the primary analysis of clinical trials. However, recent evidence suggests that likelihood‐based analyses developed under the missing at random (MAR) framework are sensible alternatives. The objective of this study was to assess the Type I error rates from a likelihood‐based MAR approach – mixed‐model repeated measures (MMRM) – compared with LOCF when estimating treatment contrasts for mean change from baseline to endpoint (Δ). Data emulating neuropsychiatric clinical trials were simulated in a 4 × 4 factorial arrangement of scenarios, using four patterns of mean changes over time and four strategies for deleting data to generate subject dropout via an MAR mechanism. In data with no dropout, estimates of Δ and SE_Δ from MMRM and LOCF were identical. In data with dropout, the Type I error rates (averaged across all scenarios) for MMRM and LOCF were 5.49% and 16.76%, respectively. In 11 of the 16 scenarios, the Type I error rate from MMRM was at least 1.00% closer to the expected rate of 5.00% than the corresponding rate from LOCF. In no scenario did LOCF yield a Type I error rate that was at least 1.00% closer to the expected rate than the corresponding rate from MMRM. The average estimate of SE_Δ from MMRM was greater in data with dropout than in complete data, whereas the average estimate of SE_Δ from LOCF was smaller in data with dropout than in complete data, suggesting that standard errors from MMRM better reflected the uncertainty in the data. The results from this investigation support those from previous studies, which found that MMRM provided reasonable control of Type I error even in the presence of MNAR missingness. No universally best approach to analysis of longitudinal data exists. However, likelihood‐based MAR approaches have been shown to perform well in a variety of situations and are a sensible alternative to the LOCF approach. MNAR methods can be used within a sensitivity analysis framework to test the potential presence and impact of MNAR data, thereby assessing robustness of results from an MAR method. Copyright © 2004 John Wiley & Sons, Ltd.     

Interpreting DNA Mixtures with Related Contributors in Subdivided Populations

Abstract.  In this paper, we study the statistical interpretation of forensic DNA mixtures with related contributors in subdivided populations. Compact general formulae for match probabilities are obtained for two situations: a relative of one tested person is an unknown contributor of a DNA mixture; and two related unknowns are contributors. The effect of kinship and population structure is illustrated using a real case example.     

Choice of the primary analysis in longitudinal clinical trials

Missing data, and the bias they can cause, are an almost ever‐present concern in clinical trials. The last observation carried forward (LOCF) approach has been frequently utilized to handle missing data in clinical trials, and is often specified in conjunction with analysis of variance (LOCF ANOVA) for the primary analysis. Considerable advances in statistical methodology, and in our ability to implement these methods, have been made in recent years. Likelihood‐based, mixed‐effects model approaches implemented under the missing at random (MAR) framework are now easy to implement, and are commonly used to analyse clinical trial data. Furthermore, such approaches are more robust to the biases from missing data, and provide better control of Type I and Type II errors than LOCF ANOVA. Empirical research and analytic proof have demonstrated that the behaviour of LOCF is uncertain, and in many situations it has not been conservative. Using LOCF as a composite measure of safety, tolerability and efficacy can lead to erroneous conclusions regarding the effectiveness of a drug. This approach also violates the fundamental basis of statistics as it involves testing an outcome that is not a physical parameter of the population, but rather a quantity that can be influenced by investigator behaviour, trial design, etc. Practice should shift away from using LOCF ANOVA as the primary analysis and focus on likelihood‐based, mixed‐effects model approaches developed under the MAR framework, with missing not at random methods used to assess robustness of the primary analysis. Copyright © 2004 John Wiley & Sons, Ltd.     

论香港地权法律制度的基本特点

作为地权制度核心的土地所有权制度,香港既没有承袭中国封建地主私有制,也没有效仿英国,而是根据自身的特殊性,建立了土地使用权与所有权相分离、土地只租不卖的基本制度。这个制度决定了港府在土地处置中拥有双重身份和权力,从而使土地发展利用的计划调节与市场调节得以比较完善的结合,为土地的经济、社会和环境效益全面实现提供了法律保障。香港经济与社会的发展和繁荣受惠于香港的地权法律制度。因此,分析研究香港地权法律制度,认识其基本特点,对“一国两制”的实现和借鉴香港经验,改革与完善我国地权法制都有相当价值。     

Creating a reference to a complex emergency situation using time series methods: war in Guinea-Bissau 1998-1999

Impacts of complex emergencies or relief interventions have often been evaluated by absolute mortality compared to international standardized mortality rates. A better evaluation would be to compare with local baseline mortality of the affected populations. A projection of population-based survival data into time of emergency or intervention based on information from before the emergency may create a local baseline reference. We find a log-transformed Gaussian time series model where standard errors of the estimated rates are included in the variance to have the best forecasting capacity. However, if time-at-risk during the forecasted period is known then forecasting might be done using a Poisson time series model with overdispersion. Whatever, the standard error of the estimated rates must be included in the variance of the model either in an additive form in a Gaussian model or in a multiplicative form by overdispersion in a Poisson model. Data on which the forecasting is based must be modelled carefully concerning not only calendar-time trends but also periods with excessive frequency of events (epidemics) and seasonal variations to eliminate residual autocorrelation and to make a proper reference for comparison, reflecting changes over time during the emergency. Hence, when modelled properly it is possible to predict a reference to an emergency-affected population based on local conditions. We predicted childhood mortality during the war in Guinea-Bissau 1998-1999. We found an increased mortality in the first half-year of the war and a mortality corresponding to the expected one in the last half-year of the war.     

论居民住房买卖所有权的取得

购买是我国城镇居民解决生活住房的主要手段,在买卖过程中,如何取得房屋的所有权至关重要也易生纠纷。房屋所有权登记的公信力以推定正确的效力重在保护善意交易人;在不存在善意第三人的条件下,真实所有人可以通过异议登记、更正登记取消房产证上登记房主的所有权,这在买卖共有房屋以及分期付款买房的交易中尤为重要。     

Generalized additive models for location, scale and shape

Summary.  A general class of statistical models for a univariate response variable is presented which we call the generalized additive model for location, scale and shape (GAMLSS). The model assumes independent observations of the response variable y given the parameters, the explanatory variables and the values of the random effects. The distribution for the response variable in the GAMLSS can be selected from a very general family of distributions including highly skew or kurtotic continuous and discrete distributions. The systematic part of the model is expanded to allow modelling not only of the mean (or location) but also of the other parameters of the distribution of y , as parametric and/or additive nonparametric (smooth) functions of explanatory variables and/or random-effects terms. Maximum (penalized) likelihood estimation is used to fit the (non)parametric models. A Newton–Raphson or Fisher scoring algorithm is used to maximize the (penalized) likelihood. The additive terms in the model are fitted by using a backfitting algorithm. Censored data are easily incorporated into the framework. Five data sets from different fields of application are analysed to emphasize the generality of the GAMLSS class of models.