Of the 324 petroleum refineries operating in the U.S. in 1982, only 149 were still in the hands of their original owners in 2007. Using duration analysis, this paper explores why refineries change ownership or shut down. Plants are more likely to ‘survive’ with their original owners if they are older or larger, but less likely if the owner is a major integrated firm, or the refinery is a more technologically complex one. This latter result differs from existing research on the issue. This paper also presents a split population model to relax the general assumption of the duration model that all refiners will eventually close down; the empirical results show that the split population model converges on a standard hazard model; the log-logistic version fits best. Finally, a multinomial logit model is estimated to analyze the factors that influence the refinery plant's choices of staying open, closing, or changing ownership. Plant size, age and technology usage have positive impacts on the likelihood that a refinery will stay open, or change ownership (rather than close down). 相似文献
This paper considers the optimal design problem for multivariate mixed-effects logistic models with longitudinal data. A decomposition method of the binary outcome and the penalized quasi-likelihood are used to obtain the information matrix. The D-optimality criterion based on the approximate information matrix is minimized under different cost constraints. The results show that the autocorrelation coefficient plays a significant role in the design. To overcome the dependence of the D-optimal designs on the unknown fixed-effects parameters, the Bayesian D-optimality criterion is proposed. The relative efficiencies of designs reveal that both the cost ratio and autocorrelation coefficient play an important role in the optimal designs. 相似文献
The load-sharing model has been studied since the early 1940s to account for the stochastic dependence of components in a parallel system. It assumes that, as components fail one by one, the total workload applied to the system is shared by the remaining components and thus affects their performance. Such dependent systems have been studied in many engineering applications which include but are not limited to fiber composites, manufacturing, power plants, workload analysis of computing, software and hardware reliability, etc. Many statistical models have been proposed to analyze the impact of each redistribution of the workload; i.e., the changes on the hazard rate of each remaining component. However, they do not consider how long a surviving component has worked for prior to the redistribution. We name such load-sharing models as memoryless. To remedy this potential limitation, we propose a general framework for load-sharing models that account for the work history. Through simulation studies, we show that an inappropriate use of the memoryless assumption could lead to inaccurate inference on the impact of redistribution. Further, a real-data example of plasma display devices is analyzed to illustrate our methods.
We investigate the convergence rates of uniform bias-corrected confidence intervals for a smooth curve using local polynomial regression for both the interior and boundary region. We discuss the cases when the degree of the polynomial is odd and even. The uniform confidence intervals are based on the volume-of-tube formula modified for biased estimators. We empirically show that the proposed uniform confidence intervals attain, at least approximately, nominal coverage. Finally, we investigate the performance of the volume-of-tube based confidence intervals for independent non-Gaussian errors. 相似文献
In modeling disease transmission, contacts are assumed to have different infection rates. A proper simulation must model the heterogeneity in the transmission rates. In this article, we present a computationally efficient algorithm that can be applied to a population with heterogeneous transmission rates. We conducted a simulation study to show that the algorithm is more efficient than other algorithms for sampling the disease transmission in a subset of the heterogeneous population. We use a valid stochastic model of pandemic influenza to illustrate the algorithm and to estimate the overall infection attack rates of influenza A (H1N1) in a Canadian city. 相似文献
Quasi-likelihood nonlinear models (QLNMs) are an extension of generalized linear model and include a widen class of models as special cases. This article investigates some diagnostic methods in QLNMs. An equivalency between a case-deletion model and a mean-shift outlier model in QLNM is established. Two simulation study and a real dataset are used to illustrate the proposed diagnostic methods. 相似文献