Environmental efficiency measurement and the materials balance condition

The materials balance condition is a fundamental adding up condition, which essentially says that: “what goes in must come out”. In this paper we argue that a number of the recently developed methods of incorporating pollution measures into standard productive efficiency models may be inconsistent with this fundamental condition. We propose an alternative method that involves the incorporation of the materials balance concept into the production model in a similar manner to which price information is normally incorporated. This produces a new environmental efficiency measure that can be decomposed into technical and allocative components, in a similar manner to the conventional cost efficiency decomposition. The approach is illustrated with the case of phosphorus emission on Belgian pig-finishing farms, using data envelopment analysis (DEA) methods. Our results indicate that a substantial proportion of nutrient pollution on these farms can be abated in a cost reducing manner.     

Robustness of normal theory statistics in structural equation models*

A condition is given by which optimal normal theory methods, such as the maximum likelihood methods, are robust against violation of the normality assumption in a general linear structural equation model. Specifically, the estimators and the goodness of fit test are robust. The estimator is efficient within some defined class, and its standard errors can be obtained by a correction formula applied to the inverse of the information matrix. Some special models, like the factor analysis model and path models, are discussed in more detail. A method for evaluating the robustness condition is given.     

An intelligent service matching method for mechanical equipment condition monitoring using the fibre Bragg grating sensor network

Due to the advantages of being able to function under harsh environmental conditions and serving as a distributed condition information source in a networked monitoring system, the fibre Bragg grating (FBG) sensor network has attracted considerable attention for equipment online condition monitoring. To provide an overall conditional view of the mechanical equipment operation, a networked service-oriented condition monitoring framework based on FBG sensing is proposed, together with an intelligent matching method for supporting monitoring service management. In the novel framework, three classes of progressive service matching approaches, including service-chain knowledge database service matching, multi-objective constrained service matching and workflow-driven human-interactive service matching, are developed and integrated with an enhanced particle swarm optimisation (PSO) algorithm as well as a workflow-driven mechanism. Moreover, the manufacturing domain ontology, FBG sensor network structure and monitoring object are considered to facilitate the automatic matching of condition monitoring services to overcome the limitations of traditional service processing methods. The experimental results demonstrate that FBG monitoring services can be selected intelligently, and the developed condition monitoring system can be re-built rapidly as new equipment joins the framework. The effectiveness of the service matching method is also verified by implementing a prototype system together with its performance analysis.     

Methods for computing marginal data densities from the Gibbs output

We introduce two estimators for estimating the Marginal Data Density (MDD) from the Gibbs output. Our methods are based on exploiting the analytical tractability condition, which requires that some parameter blocks can be analytically integrated out from the conditional posterior densities. This condition is satisfied by several widely used time series models. An empirical application to six-variate VAR models shows that the bias of a fully computational estimator is sufficiently large to distort the implied model rankings. One of the estimators is fast enough to make multiple computations of MDDs in densely parameterized models feasible.     

输电线路导线截面选择的优化设计

合理选择导线截面可以提高输电线路运行的经济性,促进降损节能。分析了按发热条件、按电压损耗条件、按经济电流密度等三种截面选择方法,并通过实例计算结果对比说明现行的按经济电流密度法选择的截面是偏大的。最后,设计了一种新的截面优化选择方法,实例计算结果验证了该方法是正确有效的。     

装备供应链价格补偿契约实施VMI分析

VMI作为供应链成员之间的一种合作方式,使供应链的库存管理以及订货管理更趋于合理,从而使供应链整体成本降低。针对军事装备供应链中军地通用装备的采购,研究了VMI实施的条件及相应的价格契约,设计了简单的价格补贴．最后通过算例对结果进行了验证。     

Procedures to determine optimum two-stage sampling plans by attributes

Summary In order to compare two sampling plans we use the minimax regret principle, i.e. the minimax principle applied to regret functions. It is shown that among all two-stage sampling plans there exists an optimum sampling plan which can be computed with the aid of a procedure presented in this paper; furthermore another procedure is described how to obtain an approximately optimum two-stage sampling plan in a more direct way. Finally only those two-stage sampling plans are regarded which satisfy an additional condition; among these sampling plans an optimum one exists and is to be determined, too.     

Millsian causation

The Directed Acyclic Graph (DAG) theory of causation is based on the assumption that randomly sampling the variables of a causal system will yield a joint probability distribution that satisfies the Markovian condition. It is shown here that this condition can be split into two parts, one of which is named the Millsian condition. It is further shown that the Millsian condition alone implies that causally unrelated sets of variables are conditionally independent given their common causes, very likely a key requirement stated by John Stuart Mill 150 years ago. In Millsian causation, unlike Markovian causation, it is possible for an indirect cause to be associated with its effect even when controlling for the intermediate direct causes. This phenomenon is explained by taking into account the existence of potential causal modulation.     

非线性需求下两层供应链的定价策略及协调机制研究

对基于单一制造商和单一零售商构成的供应链,在非线性需求函数的前提下,应用博弈理论对供应链系统的定价策略进行了分析,分别得出非合作博弈和合作博弈的均衡解,进一步对各种定价策略的效率进行了分析。另外,给出了便于实际操作的协调方法。     

浅谈兼并重组煤矿企业基础管理

基础管理是实现企业安全管理科学化、规范化的重要手段,是解决重组煤矿事故多发状况的重要途径。只有筑牢了基础管理,企业方能实现安全发展。针对兼并重组煤矿的特点,提出了加强基础管理的措施和方法。     

An empirical likelihood inference for the coefficient difference of a two-sample linear model with missing response data

In this paper, we use the empirical likelihood method to make inferences for the coefficient difference of a two-sample linear regression model with missing response data. The commonly used empirical likelihood ratio is not concave for this problem, so we append a natural and well-explained condition to the likelihood function and propose three types of restricted empirical likelihood ratios for constructing the confidence region of the parameter in question. It can be demonstrated that all three empirical likelihood ratios have, asymptotically, chi-squared distributions. Simulation studies are carried out to show the effectiveness of the proposed approaches in aspects of coverage probability and interval length. A real data set is analysed with our methods as an example.     

探析中小企业企业所得税的税收筹划

在市场经济条件下,税收筹划对企业的发展具有重要意义。中小企业应潜心钻研《企业所得税法》,用好政策给予的优惠条件,对企业进行纳税筹划。企业进行企业所得税税收筹划时,一方面可利用所得税优惠政策及准予扣除项目的限额进行税务筹划,另一方面可利用会计处理方法对企业纳税所得额进行调整。     

探析中小企业企业所得税的税收筹划

在市场经济条件下,税收筹划对企业的发展具有重要意义。中小企业应潜心钻研《企业所得税法》,用好政策给予的优惠条件,对企业进行纳税筹划。企业进行企业所得税税收筹划时,一方面可利用所得税优惠政策及准予扣除项目的限额进行税务筹划,另一方面可利用会计处理方法对企业纳税所得额进行调整。     

Exploring necessary conditions in HRM research: Fundamental issues and methodological implications

Although the notion that HRM activities are necessary conditions for achieving certain outcomes is widespread in HRM research, most empirical studies follow an additive sufficiency logic. That is, they analyse whether HRM activities increase an outcome, assuming that they can compensate for one another. However, this does not correspond to the idea of necessity where single HRM activities are required for an outcome to occur and cannot be compensated when absent. We discuss the differences between sufficiency and necessity logics in terms of theory, methodology, and practical relevance. Also, we suggest using a new approach and analysis technique in HRM research: necessary condition analysis. We illustrate the application of the method by analysing data on the relationship between ability‐, motivation‐, and opportunity‐enhancing HRM practices (i.e., high‐performance work practices) and employee performance. This illustrative example highlights that necessary conditions require particular theoretical attention and suitable empirical methods. The paper concludes by showing the value of studying necessary conditions, because such analyses allow straightforward recommendations to be given of high relevance for HRM practice, which differ from and add to recommendations based on additive sufficiency logic and analyses.     

Simultaneous Parameter Estimation in Exploratory Factor Analysis: An Expository Review

The classical exploratory factor analysis (EFA) finds estimates for the factor loadings matrix and the matrix of unique factor variances which give the best fit to the sample correlation matrix with respect to some goodness-of-fit criterion. Common factor scores can be obtained as a function of these estimates and the data. Alternatively to the classical EFA, the EFA model can be fitted directly to the data which yields factor loadings and common factor scores simultaneously. Recently, new algorithms were introduced for the simultaneous least squares estimation of all EFA model unknowns. The new methods are based on the numerical procedure for singular value decomposition of matrices and work equally well when the number of variables exceeds the number of observations. This paper provides an account that is intended as an expository review of methods for simultaneous parameter estimation in EFA. The methods are illustrated on Harman's five socio-economic variables data and a high-dimensional data set from genome research.     

金融机构基层行处如何开展“八小时”以外的员工行为排查

金融机构案件频发的主要因素是不能及时地掌握员工的思想状况,忽视了员工"八小时"以外的生活情趣,导致不能有效采取措施加以防范。本文通过剖析员工行为排查中存在的问题,探讨"八小时"以外行为排查的有效方法,以遏制案件的发生。     

Envelope condition method with an application to default risk models

We develop an envelope condition method (ECM) for dynamic programming problems – a tractable alternative to expensive conventional value function iteration (VFI). ECM has two novel features: first, to reduce the cost of iteration on Bellman equation, ECM constructs policy functions using envelope conditions which are simpler to analyze numerically than first-order conditions. Second, to increase the accuracy of solutions, ECM solves for derivatives of value function jointly with value function itself. We complement ECM with other computational techniques that are suitable for high-dimensional problems, such as simulation-based grids, monomial integration rules and derivative-free solvers. The resulting value-iterative ECM method can accurately solve models with at least up to 20 state variables and can successfully compete in accuracy and speed with state-of-the-art Euler equation methods. We also use ECM to solve a challenging default risk model with a kink in value and policy functions.     

Secondary Products,By-products and the Commodity Technology Assumption

A more general method for preparing commodity-by-commodity input–output tables under the commodity technology assumption is presented for the case when there are produced and non-produced by-products, originating in principal and in secondary production. Existing methods of the same family are shown to be special cases of the method presented here. An incorrect model specification is found to be a sufficient condition for unwarranted negative coefficients. Finally, the model is shown to satisfy desirable properties of an input–output system     

Bank capital and the macroeconomy: Policy considerations

We develop a macroeconomic model in which the balance sheet condition of financial institutions plays an important role in the determination of asset prices and economic activity. The financial intermediaries in our model are required to make investment commitments before a complete resolution of idiosyncratic funding risk that can be addressed only by costly refinancing, forcing them to behave in a risk-averse manner. The model shows that the balance sheet condition of intermediaries can drive asset values away from their fundamentals, causing aggregate investment and output to respond to shocks to intermediaries. We use this model to evaluate several public policies designed to address balance sheet problems at financial institutions. With regard to short-run policies, we find that capital injections conditioned upon voluntary recapitalization can be a more effective tool than asset purchases. With regard to long-run policies, we demonstrate that higher capital requirements can have sizable short-run effects on economic activity, and that a long transition period helps avoid undesirable side effects. Finally, we show that the marginal effects of policies can be larger during “crises” because of the nonlinear interactions between some financial frictions and policy actions.     

Convergence of stochastic approximation algorithms under irregular conditions

We consider a class of stochastic approximation (SA) algorithms for solving a system of estimating equations. The standard condition for the convergence of the SA algorithms is that the estimating functions are locally Lipschitz continuous. Here, we show that this condition can be relaxed to the extent that the estimating functions are bounded and continuous almost everywhere. As a consequence, the use of the SA algorithm can be extended to some problems with irregular estimating functions. Our theoretical results are illustrated by solving an estimation problem for exponential power mixture models.