家族式企业裂解动因的制度分析

我国家族企业“富不过三代”现象引起了人们的极大关注。笔者认为,家族式企业裂解的制度原因在于产权制度的排他性、委托代理的高风险性以及人才选拔的制度封闭性。     

家族企业管理模式变革的实证研究

文章简要回顾了国内外学术界对家族企业的研究成果 ,根据我国私营企业发展现状 ,提出了在现阶段我国家族企业发展的两个假设 ,即 :两权 (所有权和管理权 )所有的家族企业是私营企业创业时期的首选 ;制度环境 (市场体系完善、法律健全、文化先进等 )状况促进或阻碍家族企业两权分离的发生。并用理论和统计数据分析、实证了家族企业管理模式及其影响因素     

A spatiotemporal auto-regressive moving average model for solar radiation

Summary.  To investigate the variability in energy output from a network of photovoltaic cells, solar radiation was recorded at 10 sites every 10 min in the Pentland Hills to the south of Edinburgh. We identify spatiotemporal auto-regressive moving average models as the most appropriate to address this problem. Although previously considered computationally prohibitive to work with, we show that by approximating using toroidal space and fitting by matching auto-correlations, calculations can be substantially reduced. We find that a first-order spatiotemporal auto-regressive (STAR(1)) process with a first-order neighbourhood structure and a Matern noise process provide an adequate fit to the data, and we demonstrate its use in simulating realizations of energy output.     

将ISO9001标准应用于实习工厂质量管理的探研

文章探讨了用ISO90 0 0族标准在高校实习工厂建立质量管理体系的可行性和基本策略 ,明确相关术语和定义 ,确立质量方针与质量目标 ,概括描述了质量手册、程序文件、作业性指导文件和质量记录的内容与框架。     

Estimation of dependence between paired correlated failure times in the presence of covariate measurement error

Summary. In many biomedical studies, covariates are subject to measurement error. Although it is well known that the regression coefficients estimators can be substantially biased if the measurement error is not accommodated, there has been little study of the effect of covariate measurement error on the estimation of the dependence between bivariate failure times. We show that the dependence parameter estimator in the Clayton–Oakes model can be considerably biased if the measurement error in the covariate is not accommodated. In contrast with the typical bias towards the null for marginal regression coefficients, the dependence parameter can be biased in either direction. We introduce a bias reduction technique for the bivariate survival function in copula models while assuming an additive measurement error model and replicated measurement for the covariates, and we study the large and small sample properties of the dependence parameter estimator proposed.     

Conditional Likelihood Estimators for Hidden Markov Models and Stochastic Volatility Models

ABSTRACT.  This paper develops a new contrast process for parametric inference of general hidden Markov models, when the hidden chain has a non-compact state space. This contrast is based on the conditional likelihood approach, often used for ARCH-type models. We prove the strong consistency of the conditional likelihood estimators under appropriate conditions. The method is applied to the Kalman filter (for which this contrast and the exact likelihood lead to asymptotically equivalent estimators) and to the discretely observed stochastic volatility models.     

A Sparse Implementation of the Average Information Algorithm for Factor Analytic and Reduced Rank Variance Models

Factor analytic variance models have been widely considered for the analysis of multivariate data particularly in the psychometrics area. Recently Smith, Cullis & Thompson (2001) have considered their use in the analysis of multi‐environment data arising from plant improvement programs. For these data, the size of the problem and the complexity of the variance models chosen to account for spatial heterogeneity within trials implies that standard algorithms for fitting factor analytic models can be computationally expensive. This paper presents a sparse implementation of the average information algorithm (Gilmour, Thompson & Cullis, 1995) for fitting factor analytic and reduced rank variance models.     

Improved model selection criteria for SETAR time series models

The purpose of this paper is threefold. First, we obtain the asymptotic properties of the modified model selection criteria proposed by Hurvich et al. (1990. Improved estimators of Kullback-Leibler information for autoregressive model selection in small samples. Biometrika 77, 709–719) for autoregressive models. Second, we provide some highlights on the better performance of this modified criteria. Third, we extend the modification introduced by these authors to model selection criteria commonly used in the class of self-exciting threshold autoregressive (SETAR) time series models. We show the improvements of the modified criteria in their finite sample performance. In particular, for small and medium sample size the frequency of selecting the true model improves for the consistent criteria and the root mean square error (RMSE) of prediction improves for the efficient criteria. These results are illustrated via simulation with SETAR models in which we assume that the threshold and the parameters are unknown.     

Some results on optimality in models with heteroscedastic errors from partial optimum designs

This paper studies optimum designs for linear models when the errors are heteroscedastic. Sufficient conditions are given in order to obtainD-, A- andE-optimum designs for a complete regression model from partial optimum designs for some sub-parameters. A result about optimality for a complete model from the optimality for the submodels is included. Supported by Junta de Andalucía, research group FQM244.     

家庭档案及其教育功能探析

家庭档案在人们社会生活中的重要作用日益凸显;就教育功能而言,突出地表现在家庭档案可以提供德智体、美育、心育等方面的教育内容;同时,家庭档案也是一种积极有效的教育手段:能促进自主学习与自我教育,有利于家庭积极参与社区教育和社会主义精神文明建设;能促进学校的教育教学工作,有利于家教合作。档案部门、家庭、学校等有关方面应积极协作,加强宣传力度,加强公众的档案意识,向公众提供建设家庭档案的业务指导,加强家庭档案的建设,以充分发挥其教育功能。