An efficient scheduling algorithm for the hot coil making in the steel mini-mill

This paper investigates and suggests an efficient solution to the problem of scheduling the steel making line in the Mini Steel Mill, which consists of three major processes: molten steel making, continuous slab casting, and hot charged rolling. Careful synchronization of these processes is a key productivity factor, since a very limited amount of work-in-process inventory is allowed. Since each process must run in batch, the schedule for the Mini-Mill consists of grouping and sequencing of lots for each process. However, each process has its own criteria for judging the quality of its lot grouping, which often conflicts with other processes. An efficient scheduling algorithm for the Mini-Mill is proposed. Numerical experiments with real world data suggest that the proposed algorithm yield satisfactory schedules very efficiently. The algorithm is currently used for the actual scheduling of a Mini-Mill in Korea.     

The Structural Determinants of Mortality in Japanese Prefectures

This cross-sectional multivariate analysis of age- and sex-standardized mortality for the 47 Japanese prefectures explores and tests the hypotheses that structural pluralism and differentiation, interpreted as dimensions of "social problem solving capacity", determine lower mortality rates, controlling on the availability of medical facilities. The postulated explanatory principle is that participation in community problem-solving optimizes the biological functioning of the residents, which fosters better health. A factor analysis generated two factors (plus a third index of medical facilities) that measure the structural dimensions. Regression analysis showed that pluralism predicted lower mortality for both males and females, but differentiation gave contradictory results. Surprisingly, medical facilities predicted higher male mortality. A control on the more recently industrialized and the hyper-urbanized prefectures refined the analysis so that the differentiation factor predicted consistently and the spurious correlation between medical facilities and male mortality dissolved.     

Sampling schemes for generalized linear Dirichlet process random effects models

We evaluate MCMC sampling schemes for a variety of link functions in generalized linear models with Dirichlet process random effects. First, we find that there is a large amount of variability in the performance of MCMC algorithms, with the slice sampler typically being less desirable than either a Kolmogorov–Smirnov mixture representation or a Metropolis–Hastings algorithm. Second, in fitting the Dirichlet process, dealing with the precision parameter has troubled model specifications in the past. Here we find that incorporating this parameter into the MCMC sampling scheme is not only computationally feasible, but also results in a more robust set of estimates, in that they are marginalized-over rather than conditioned-upon. Applications are provided with social science problems in areas where the data can be difficult to model, and we find that the nonparametric nature of the Dirichlet process priors for the random effects leads to improved analyses with more reasonable inferences.     

Maximum likelihood estimation for directional conditionally autoregressive models

A spatial process observed over a lattice or a set of irregular regions is usually modeled using a conditionally autoregressive (CAR) model. The neighborhoods within a CAR model are generally formed using only the inter-distances or boundaries between the regions. To accommodate directional spatial variation, a new class of spatial models is proposed using different weights given to neighbors in different directions. The proposed model generalizes the usual CAR model by accounting for spatial anisotropy. Maximum likelihood estimators are derived and shown to be consistent under some regularity conditions. Simulation studies are presented to evaluate the finite sample performance of the new model as compared to the CAR model. Finally, the method is illustrated using a data set on the crime rates of Columbus, OH and on the elevated blood lead levels of children under the age of 72 months observed in Virginia in the year of 2000.     

Bayesian computation for the superposition of nonhomogeneous poisson processes

Bayesian inference for the superposition of nonhomogeneous Poisson processes is studied. A Markov-chain Monte Carlo method with data augmentation is developed to compute the features of the posterior distribution. For each observed failure epoch, a latent variable is introduced that indicates which component of the superposition model gives rise to the failure. This data-augmentation approach facilitates specification of the transitional kernel in the Markov chain. Moreover, new Bayesian tests are developed for the full superposition model against simpler submodels. Model determination by a predictive likelihood approach is studied. A numerical example based on a real data set is given.     

Penalized regression models with autoregressive error terms

Penalized regression methods have recently gained enormous attention in statistics and the field of machine learning due to their ability of reducing the prediction error and identifying important variables at the same time. Numerous studies have been conducted for penalized regression, but most of them are limited to the case when the data are independently observed. In this paper, we study a variable selection problem in penalized regression models with autoregressive (AR) error terms. We consider three estimators, adaptive least absolute shrinkage and selection operator, bridge, and smoothly clipped absolute deviation, and propose a computational algorithm that enables us to select a relevant set of variables and also the order of AR error terms simultaneously. In addition, we provide their asymptotic properties such as consistency, selection consistency, and asymptotic normality. The performances of the three estimators are compared with one another using simulated and real examples.