Quality management, environmental management and firm performance: A review of empirical studies and issues of integration

Quality management (QM) and environmental management (EM) are two business practices that may affect firm performance. These practices are being increasingly introduced into firms, which often use them jointly owing to their similarities. As a result of these similarities, their integration has become a popular topic of research and practice. In the field of integration, the highest level of integration may be achieved by means of a single, full QM–EM system (QEM) in which QM and EM lose their independence. It is therefore desirable to identify dimensions from which to assess these management practices and their effects on performance. The aim of this paper is to carry out a literature review in order to propose and analyse dimensions for QM, EM, QEM and firm performance, as well as models of cause–effect relationships between these variables. The topics reviewed are the following: (1) the QM and EM dimensions; (2) the empirical studies about QM–performance and EM–performance links; and (3) issues of integration. The review suggests that the large body of QM research may inform EM and QEM. This is so because research on the QM side is more advanced and developed than that on the EM side.     

Asset Markets and Equilibrium Selection in Public Goods Games with Provision Points: An Experimental Study

We report experimental results on the effects that auctioning the right to play a public goods game with a provision point may have on equilibrium selection and individual behavior. Auctioning off such a right among a larger population of players strikingly enhances public good provision. Once public good provision is obtained, the auction price at the preliminary stage increases to its upper limit, dissipating all players' gains associated with the provision of the public good. Individual deviations from the equilibrium strategy are neither able to force lower market prices nor to affect provision of the public good in subsequent periods. (JEL C72 , C92 , H41 )     

Bivariate Continuous Distributions with Specified Conditional Hazard Functions

Analysis of covariance (ANCOVA) is the standard procedure for comparing several treatments when the response variable depends on one or more covariates. We consider the problem of testing the equality of treatment effects when the variances are not assumed to be equal. It is well known that classical F test is not robust with respect to the assumption of equal variances and may lead to misleading conclusions if the variances are not equal. Ananda (1998 Ananda , M. M. A. ( 1998 ). Bayesian and non-Bayesian solutions to analysis of covariance models under heteroscedasticity . J. Econometrics 86 : 177 – 192 .[Crossref], [Web of Science ®] , [Google Scholar]) developed a generalized F test for testing the equality of treatment effects. However, simulation studies show that the actual size of this test can be much higher than the nominal level when the sample sizes are small, particularly when the number of treatments is large. In this article, we develop a test using the parametric bootstrap approach of Krishnamoorthy et al. (2007 Krishnamoorthy , K. , Lu , F. , Mathew , T. ( 2007 ). A parametric bootstrap approach for ANOVA with unequal variances: Fixed and random models . Computat. Statist. Data Anal. 51 : 5731 – 5742 .[Crossref], [Web of Science ®] , [Google Scholar]). Our simulations show that the actual size of our proposed test is close to the nominal level, irrespective of the number of treatments and sample sizes. Our simulations also indicate that our proposed PB test is more robust, with respect to the assumption of normality, than the generalized F test. Therefore, our proposed PB test provides a satisfactory alternative to the generalized F test.     

An extension of a change-point problem

We consider a specific classification problem in the context of change-point detection. We present generalized classical maximum likelihood tests for homogeneity of the observed sample in a simple form which avoids the complex direct estimation of unknown parameters. This paper proposes a martingale approach to transformation of test statistics. For sequential and retrospective testing problems, we propose the adapted Shiryayev–Roberts statistics in order to obtain simple tests with asymptotic power one. An important application of the developed methods is in the analysis of exposure's measurements subject to limits of detection in occupational medicine.     

Total least squares and bootstrapping with applications in calibration

The solution to the errors-in-variables problem computed through total least squares is highly nonlinear. Because of this, many statistical procedures for constructing confidence intervals and testing hypotheses cannot be applied. One possible solution to this dilemma is bootstrapping. A nonparametric bootstrap technique could fail. Here, the proper nonparametric bootstrap procedure is provided and its correctness is proved. On the other hand, a residual bootstrap is not valid and suitable in this case. The results are illustrated through a simulation study. An application of this approach to calibration data is presented.     

Nonparametric-likelihood inference based on cost-effectively-sampled-data

Costs associated with the evaluation of biomarkers can restrict the number of relevant biological samples to be measured. This common problem has been dealt with extensively in the epidemiologic and biostatistical literature that proposes to apply different cost-efficient procedures, including pooling and random sampling strategies. The pooling design has been widely addressed as a very efficient sampling method under certain parametric assumptions regarding data distribution. When cost is not a main factor in the evaluation of biomarkers but measurement is subject to a limit of detection, a common instrument limitation on the measurement process, the pooling design can partially overcome this instrumental limitation. In certain situations, the pooling design can provide data that is less informative than a simple random sample; however this is not always the case. Pooled-data-based nonparametric inferences have not been well addressed in the literature. In this article, a distribution-free method based on the empirical likelihood technique is proposed to substitute the traditional parametric-likelihood approach, providing the true coverage, confidence interval estimation and powerful tests based on data obtained after the cost-efficient designs. We also consider several nonparametric tests to compare with the proposed procedure. We examine the proposed methodology via a broad Monte Carlo study and a real data example.     

Bayesian inference for the mean and standard deviation of a normal population when only the sample size,mean and range are observed

Consider a random sample X₁, X₂,…, X_n, from a normal population with unknown mean and standard deviation. Only the sample size, mean and range are recorded and it is necessary to estimate the unknown population mean and standard deviation. In this paper the estimation of the mean and standard deviation is made from a Bayesian perspective by using a Markov Chain Monte Carlo (MCMC) algorithm to simulate samples from the intractable joint posterior distribution of the mean and standard deviation. The proposed methodology is applied to simulated and real data. The real data refers to the sugar content (^oBRIX level) of orange juice produced in different countries.     

A Bayesian Approach for Multiple Response Surface Optimization in the Presence of Noise Variables

An approach for the multiple response robust parameter design problem based on a methodology by Peterson (2000) is presented. The approach is Bayesian, and consists of maximizing the posterior predictive probability that the process satisfies a set of constraints on the responses. In order to find a solution robust to variation in the noise variables, the predictive density is integrated not only with respect to the response variables but also with respect to the assumed distribution of the noise variables. The maximization problem involves repeated Monte Carlo integrations, and two different methods to solve it are evaluated. A Matlab code was written that rapidly finds an optimal (robust) solution in case it exists. Two examples taken from the literature are used to illustrate the proposed method.     

A basis approach to goodness-of-fit testing in recurrent event models

A class of tests for the hypothesis that the baseline hazard function in Cox's proportional hazards model and for a general recurrent event model belongs to a parametric family C identical with {lambda(0)(.; xi): xi in Xi} is proposed. Finite properties of the tests are examined via simulations, while asymptotic properties of the tests under a contiguous sequence of local alternatives are studied theoretically. An application of the tests to the general recurrent event model, which is an extended minimal repair model admitting covariates, is demonstrated. In addition, two real data sets are used to illustrate the applicability of the proposed tests.