Assessing local influence for nonlinear structural equation models with ignorable missing data
A method is proposed in this paper to assess the local influence of minor perturbations for a nonlinear structural equation model with missing data that are missing at random. The main idea is to apply Zhu and Lee's ( (2001) 111) approach to the conditional expectation of the complete-data log-likelihood function in the corresponding EM algorithm for deriving the conformal normal curvature. Building blocks for achieving the diagnostic measures are computed via latent variables that are generated by the Gibbs sampler and Metropolis-Hastings algorithm. It is shown that the proposed methodology is feasible for a wide variety of perturbation schemes. To illustrate the methodology, results that are obtained from analyses of some artificial examples, a simulation study, and a real example are presented.

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: Maximum likelihood estimation and model comparison of nonlinear structural equation models with continuous and polytomous variables
abstract: 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: An Efficient Neighbor Knowledge Based Broadcasting for Mobile Ad Hoc Networks
abstract: Although a flooding is the most commonly used method for one-to-all communications, it often results in huge traffic in mobile ad hoc networks. Several optimization schemes have been proposed to alleviate this problem. However, none of them offers a complete solution and has their own limitations, such as, lack of reliability, high latency or excessive control packet overhead. In this paper, we propose the FONIAH (Flooding based on One- hop Neighbor Information and Adaptive Holding) scheme to overcome these limitations.

title: A new neuro-fuzzy identification model of nonlinear dynamic systems
abstract: Multilayer neural networks with error back-propagation learning algorithms have the capability of learning an arbitrary continuous nonlinear function with examples of input and output sample pairs and a great potential for identifying nonlinear dynamic systems with unknown characteristics. A fuzzy system is composed of fuzzification of input, reasoning (or inference) by fuzzy rules, and defuzzification of fuzzy output. In general, there are some difficulties in finding suitable fuzzification and defuzzification methods and fuzzy rules. Formation of fuzzy rules with complex input-output relationships can be replaced by building neural networks with input and output sample pairs. A neuro-fuzzy identifier is proposed to have a cascaded structure of fuzzification, neural network, and defuzzification and additionally is shown to be able to compensate for fuzzification and defuzzification error of fuzzy logic. Computer simulation shows that neuro-fuzzy identification is very effective in modeling the fuzzy system whose fuzzy rules can not be obtained easily.

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: An Accurate Instruction-Level Energy Consumption Model for Embedded RISC Processors
abstract: Energy consumption of software is becoming an increasingly important issue in designing mobile embedded systems where batteries are used as the main power source. As a consequence, recently, a number of promising techniques have been proposed to optimize software for reduced energy consumption. Such low-power software techniques require an energy consumption model that can be used to estimate or predict the energy consumed by software. We propose a technique to derive an accurate energy consumption model at the instruction level, combining an empirical method and a statistical analysis technique. The result of the proposed approach is given by a model equation that characterizes energy behavior of software based on the properties of the instructions. Experimental results show that the model equation can accurately estimate the energy consumption of random instruction sequences, with an average error of 2.5%

title: Integer-Magic Spectra Of Amalgamations Of Stars And Cycles
abstract: For any positive integer k; a graph G = (V;E) is said to be ZZ k-magic if there exists a labeling l : E(G) i! ZZ k i f0g such that the induced vertex set labeling l + : V (G) i! ZZ k