27471022
Deletion measures for generalized linear mixed effects models
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.
39604886
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.
10228448
A Z Approach in Validating ORA-SS Data Models
The rapid growth of the World Wide Web has resulted in more data being accessed over the Internet. In turn there is an increase in the use of semistructured data, which plays a crucial role in many web applications particularly with the introduction of XML and its related technologies. This increase in use makes the design of good semistructured data structures essential. The Object Relationship Attribute model for Semistructured data (ORA-SS) is a graphical notation for designing and representing semistructured data. In this paper, we demonstrate an approach to formally validate the ORA-SS data models in order to enhance the correctness of semistructured data design. A mathematical semantics for the ORA-SS notation is defined using the Z formal language, and further validation processes are carried out to check the correctness of the semistructured data models at both the schema and instance levels.
7165920
SILENT: serialized low energy transmission coding for on-chip interconnection networks
On-chip source-synchronous serial communication has many advantages over multi-bit parallel communication in the aspects of skew, crosstalk area cost, wiring difficulty, and clock synchronization. However, the serial wire tends to dissipate more energy than parallel bus due to the bit multiplexing. We propose a coding method to reduce the transmission energy of the serial communication by minimizing the number of transitions on the serial wire. We demonstrate the significant energy saving in a multimedia application, 3D graphics. We also apply the coding technique to a CMOS SoC implementation which integrates various processing units with packet switched on-chip networks.
12406489
Integration of Hand- Held Devices into Collaborative Environments
We describe a collaborative environment Garnet designed around a universal event model using the publish-subscribe paradigm. We study the integration of personal digital assistants (PDA) into synchronous collaborative sessions. Our architecture uses a PDA adaptor that maps system events into a form suitable for the PDA and uses an optimized protocol that respects bandwidth limitations.
43679766
Blind signal separation and independent component analysis
Independent component analysis (ICA) is emerging as a new paradigm in signal processing and data analysis. ICA has been proposed as a solution to the blind source separation problem (BSS) in which sensory signals are given as mixtures of unknown source signals. Since neither the sources nor the mixing environment are known a priori there is no general analytical solution to this problem. However, since the last decade researchers have found good approximate solutions based on assumptions about the source statistics. They were able to formulate solutions or algorithms that were derived from well-known cost functions including maximum likelihood estimation, information maximization and higher-order cumulants. The ICA framework touches many theoretical research areas, and therefore receives attention in several research communities including machine learning, neural networks, statistical signal processing and Bayesian modeling. More recently, there have been numerous applications of ICA in adaptive 0ltering, speech signal processing, biomedical signal processing, computational neuroscience, image coding, text data modeling, and 0nancial data analysis. The standard ICA formulation has, in general, strong assumptions or simpli0cations such as: the number of sensors has to be equal or greater than the number of sources, there is no additive noise signal in the sensors, and the sources are modeled as random variables. However, when applying ICA to real-world problems it becomes evident that those restrictions are cumbersome and although the standard ICA algorithms give good approximate solutions, there is great need in going beyond the standard assumptions and develop new algorithms that can relax some of the assumptions. This special issue on ICA and BSS is a timely publication covering important recent 0ndings in ICA theory as well as new applications of ICA in data analysis. Leading researchers present their work to advance this 0eld beyond traditional borders and show how this 0eld can bridge disciplines and yield new methods that lead to mutual bene0ts and advances in science and engineering. This issue is composed of a review article and two parts: the 0rst part presents new theoretical 0ndings while the second part presents novel applications. Often, there is no such sharp division into these two parts since illustrative simulations support theoretical 0ndings and practical methods include algorithmic modi0cations to design and evaluate the application-speci0c methods. In “Frontiers of research in BSS=ICA”, V. David S: anchez A. formulates some of the basic concepts and problems in the areas of blind signal separation and independent component analysis. He then moves to a concise description of key methods of data preprocessing and algorithms that provide solutions to the instantaneous mixture and the
13015161
Neighbor supporting ad hoc multicast routing protocol
An ad hoc network is a multi-hop wireless network formed by a collection of mobile nodes without the intervention of fixed infrastructure. Limited bandwidth and a high degree of mobility require that routing protocols for ad hoc networks be robust, simple, and energy-conserving. This paper proposes a new ad hoc multicast routing protocol called Neighbor-Supporting Multicast Protocol (NSMP). NSMP adopts a mesh structure to enhance resilience against mobility. And NSMP utilizes node locality to reduce the overhead of route failure recovery and mesh maintenance. NSMP also attempts to improve route efficiency and reduce data transmissions. Our simulation results show that NSMP delivers packets efficiently while substantially reducing control overhead in various environments.