Maximum likelihood estimation and model comparison of nonlinear structural equation models with continuous and polytomous variables
Recently, it is recognized that nonlinear relationships among latent variables in a structural equation model are important. In this article, maximum likelihood (ML) analysis of a general nonlinear structural equation model that contains fixed covariates in the measurement equation and the nonlinear structural equation is investigated. A MCEM algorithm is implemented to obtain the ML estimates, in which the E-step is completed with the help of a hybrid algorithm that combines the Gibbs sampler and the Metropolis–Hastings algorithm whilst the M-step is completed by conditional maximization. The importance sampling is employed to compute the observed-data likelihood in the Bayesian Information Criterion for model comparison. The methodology is illustrated with a simulation study and a real example.

title: Drug risk assessment with determining the number of sub-populations under finite mixture normal models
abstract: In some biological experiments, it is quite common that laboratory subjects may be different in their patterns of susceptibility to a treatment. In these situations, finite mixture model analysis becomes a useful tool. Under a finite mixture normal model, drug risk assessment problems are studied under the assumption that the population at risk consists of multiple sub-types of different susceptibilities. The EM algorithm and its extension are utilized to obtain the maximum likelihood estimates of the model. Particular discussion is given to the model selection problem based on Bayes factor, which is approximated by the Schwarz criterion. Meantime, the practical significance of the proposed method is illustrated with an actual dose–response data set.

title: Deletion measures for generalized linear mixed effects models
abstract: Generalized linear mixed models (GLMMs) have wide applications in practice. Similar to other data analyses, the identification of influential observations that may be potential outliers is an important step beyond estimation in GLMMs. Since the pioneering work of Cook in 1977, deletion measures have been applied to many statistical models for identifying influential observations. However, as this well-known approach is based on the observed-data likelihood, it is very difficult to apply it to developing diagnostic measures for GLMMs due to the complexity of the observed-data likelihood that involves multidimensional integrals. The objective of this article is to develop diagnostic measures for identifying influential observations. Deletion measures are developed on the basis of the conditional expectation of the complete-data log-likelihood at the E-step of a stochastic approximation Markov chain Monte Carlo algorithm. Making use of by-products of the estimation to compute building blocks of the proposed diagnostic measures and activating appropriate approximations, the proposed methods require little additional computation. The performance of the methods is illustrated by an artificial example, a real example, and some simulation studies.

title: Contention free embedding of complete binary trees into 3D meshes in half and full-duplexed network models
abstract: We consider the problem of dimension-ordered embedding of complete binary trees into 3-dimensional meshes without link contention. In this paper, the embedding problems are studied under two different network models: half-duplexed networks and full-duplexed networks. Gibbons and Paterson showed that a complete binary tree T/sub p/ could be disjointedly embedded into a full-duplexed 2-dimensional mesh of optimum size without dimension-ordered routing. Using the dimension-ordered routing, the authors showed that T/sub p/ could be embedded into a 3-dimensional mesh of optimum size with link congestion two. This paper presents the dimension-ordered embedding algorithms of the complete binary trees into the wormhole routed 3-dimensional meshes without link contention achieving expansion of no larger than 1.125 of optimum in half-duplexed network model and optimum in full-duplexed network model.

title: A multiple criteria model for the location-allocation problem
abstract: This study presents the application of integer goal programming to the facility location and products allocation problem with multiple, competing objectives. The proposed model describes the situation in which a firm evaluates six potential production plant location sites in four states that would serve four distribution centers. The model considers multiple objectives including several qualitative factors of location analysis and cost-related objectives have been considered in the model. The result of the model indicates that integer goal programming is one of the promising techniques which are applicable to the real-world location/allocation problems.

title: ICA-based Clustering of Genes from Microarray Expression Data
abstract: We propose an unsupervised methodology using independent component analysis (ICA) to cluster genes from DNA microarray data. Based on an ICA mixture model of genomic expression patterns, linear and nonlinear ICA finds components that are specific to certain biological processes. Genes that exhibit significant up-regulation or down-regulation within each component are grouped into clusters. We test the statistical significance of enrichment of gene annotations within each cluster. ICA-based clustering outperformed other leading methods in constructing functionally coherent clusters on various datasets. This result supports our model of genomic expression data as composite effect of independent biological processes. Comparison of clustering performance among various ICA algorithms including a kernel-based nonlinear ICA algorithm shows that nonlinear ICA performed the best for small datasets and natural-gradient maximization-likelihood worked well for all the datasets.

title: Low-power network-on-chip for high-performance SoC design
abstract: An energy-efficient network-on-chip (NoC) is presented for possible application to high-performance system-on-chip (SoC) design. It incorporates heterogeneous intellectual properties (IPs) such as multiple RISCs and SRAMs, a reconfigurable logic array, an off-chip gateway, and a 1.6-GHz phase-locked loop (PLL). Its hierarchically-star-connected on-chip network provides the integrated IPs, which operate at different clock frequencies, with packet-switched serial-communication infrastructure. Various low-power techniques such as low-swing signaling, partially activated crossbar, serial link coding, and clock frequency scaling are devised, and applied to achieve the power-efficient on-chip communications. The 5 /spl times/5 mm/sup 2/ chip containing all the above features is fabricated by 0.18-/spl mu/m CMOS process and successfully measured and demonstrated on a system evaluation board where multimedia applications run. The fabricated chip can deliver 11.2-GB/s aggregated bandwidth at 1.6-GHz signaling frequency. The chip consumes 160 mW and the on-chip network dissipates less than 51 mW.

title: NodeWiz: peer-to-peer resource discovery for grids
abstract: Efficient resource discovery based on dynamic attributes such as CPU utilization and available bandwidth is a crucial problem in the deployment of computing grids. Existing solutions are either centralized or unable to answer advanced resource queries (e.g., range queries) efficiently. We present the design of NodeWiz, a grid information service (CIS) that allows multi-attribute range queries to be performed efficiently in a distributed manner. This is obtained by aggregating the directory services of individual organizations in a peer-to-peer information service.