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Title: Dynamic Modeling and Statistical Analysis of Event Times |
Abstract: This review article provides an overview of recent work in the modeling and analysis of recurrent events arising in engineering, reliability, public health, biomedicine and other areas. Recurrent event modeling possesses unique facets making it different and more difficult to handle than single event settings... |
Title: Threshold Regression for Survival Analysis: Modeling Event Times by a Stochastic Process Reaching a Boundary |
Abstract: Many researchers have investigated first hitting times as models for survival data. First hitting times arise naturally in many types of stochastic processes, ranging from Wiener processes to Markov chains. In a survival context, the state of the underlying process represents the strength of an item or the he... |
Title: Advances in Data Combination, Analysis and Collection for System Reliability Assessment |
Abstract: The systems that statisticians are asked to assess, such as nuclear weapons, infrastructure networks, supercomputer codes and munitions, have become increasingly complex. It is often costly to conduct full system tests. As such, we present a review of methodology that has been proposed for addressing system r... |
Title: On the Statistical Modeling and Analysis of Repairable Systems |
Abstract: We review basic modeling approaches for failure and maintenance data from repairable systems. In particular we consider imperfect repair models, defined in terms of virtual age processes, and the trend-renewal process which extends the nonhomogeneous Poisson process and the renewal process. In the case where ... |
Title: A Review of Accelerated Test Models |
Abstract: Engineers in the manufacturing industries have used accelerated test (AT) experiments for many decades. The purpose of AT experiments is to acquire reliability information quickly. Test units of a material, component, subsystem or entire systems are subjected to higher-than-usual levels of one or more acceler... |
Title: A Conversation With Harry Martz |
Abstract: Harry F. Martz was born June 16, 1942 and grew up in Cumberland, Maryland. He received a Bachelor of Science degree in mathematics (with a minor in physics) from Frostburg State University in 1964, and earned a Ph.D. in statistics at Virginia Polytechnic Institute and State University in 1968. He started his ... |
Title: Virtual Manufacturing : Tools for improving Design and Production |
Abstract: The research area "Virtual Manufacturing" can be defined as an integrated manufacturing environment which can enhance one or several levels of decision and control in manufacturing process. Several domains can be addressed: Product and Process Design, Process and Production Planning, Machine Tool, Robot and M... |
Title: A preliminary analysis on metaheuristics methods applied to the Haplotype Inference Problem |
Abstract: Haplotype Inference is a challenging problem in bioinformatics that consists in inferring the basic genetic constitution of diploid organisms on the basis of their genotype. This information allows researchers to perform association studies for the genetic variants involved in diseases and the individual resp... |
Title: Real-time control and monitoring system for LIPI's Public Cluster |
Abstract: We have developed a monitoring and control system for LIPI's Public Cluster. The system consists of microcontrollers and full web-based user interfaces for daily operation. It is argued that, due to its special natures, the cluster requires fully dedicated and self developed control and monitoring system. We ... |
Title: Structure or Noise? |
Abstract: We show how rate-distortion theory provides a mechanism for automated theory building by naturally distinguishing between regularity and randomness. We start from the simple principle that model variables should, as much as possible, render the future and past conditionally independent. From this, we construc... |
Title: A multivariate central limit theorem for randomized orthogonal array sampling designs in computer experiments |
Abstract: Let $f:[0,1)^d \to \mathbb R$ be an integrable function. An objective of many computer experiments is to estimate $\int_[0,1)^d f(x) dx$ by evaluating f at a finite number of points in [0,1)^d. There is a design issue in the choice of these points and a popular choice is via the use of randomized orthogonal a... |
Title: Reconstruction of Protein-Protein Interaction Pathways by Mining Subject-Verb-Objects Intermediates |
Abstract: The exponential increase in publication rate of new articles is limiting access of researchers to relevant literature. This has prompted the use of text mining tools to extract key biological information. Previous studies have reported extensive modification of existing generic text processors to process biol... |
Title: Convergence of adaptive mixtures of importance sampling schemes |
Abstract: In the design of efficient simulation algorithms, one is often beset with a poor choice of proposal distributions. Although the performance of a given simulation kernel can clarify a posteriori how adequate this kernel is for the problem at hand, a permanent on-line modification of kernels causes concerns abo... |
Title: Modeling Visual Information Processing in Brain: A Computer Vision Point of View and Approach |
Abstract: We live in the Information Age, and information has become a critically important component of our life. The success of the Internet made huge amounts of it easily available and accessible to everyone. To keep the flow of this information manageable, means for its faultless circulation and effective handling ... |
Title: A flexible Bayesian generalized linear model for dichotomous response data with an application to text categorization |
Abstract: We present a class of sparse generalized linear models that include probit and logistic regression as special cases and offer some extra flexibility. We provide an EM algorithm for learning the parameters of these models from data. We apply our method in text classification and in simulated data and show that... |
Title: Estimating the proportion of differentially expressed genes in comparative DNA microarray experiments |
Abstract: DNA microarray experiments, a well-established experimental technique, aim at understanding the function of genes in some biological processes. One of the most common experiments in functional genomics research is to compare two groups of microarray data to determine which genes are differentially expressed. ... |
Title: Empirical Bayes methods for controlling the false discovery rate with dependent data |
Abstract: False discovery rate (FDR) has been widely used as an error measure in large scale multiple testing problems, but most research in the area has been focused on procedures for controlling the FDR based on independent test statistics or the properties of such procedures for test statistics with certain types of... |
Title: A smoothing model for sample disclosure risk estimation |
Abstract: When a sample frequency table is published, disclosure risk arises when some individuals can be identified on the basis of their values in certain attributes in the table called key variables, and then their values in other attributes may be inferred, and their privacy is violated. On the basis of the sample ... |
Title: An Interval Analysis Based Study for the Design and the Comparison of 3-DOF Parallel Kinematic Machines |
Abstract: This paper addresses an interval analysis based study that is applied to the design and the comparison of 3-DOF parallel kinematic machines. Two design criteria are used, (i) a regular workspace shape and, (ii) a kinetostatic performance index that needs to be as homogeneous as possible throughout the workspa... |
Title: Deconvolution by simulation |
Abstract: Given samples (x_1,...,x_m) and (z_1,...,z_n) which we believe are independent realizations of random variables X and Z respectively, where we further believe that Z=X+Y with Y independent of X, the problem is to estimate the distribution of Y. We present a new method for doing this, involving simulation. Exp... |
Title: A comparison of the accuracy of saddlepoint conditional cumulative distribution function approximations |
Abstract: Consider a model parameterized by a scalar parameter of interest and a nuisance parameter vector. Inference about the parameter of interest may be based on the signed root of the likelihood ratio statistic R. The standard normal approximation to the conditional distribution of R typically has error of order O... |
Title: Statistical inverse problems in active network tomography |
Abstract: The analysis of computer and communication networks gives rise to some interesting inverse problems. This paper is concerned with active network tomography where the goal is to recover information about quality-of-service (QoS) parameters at the link level from aggregate data measured on end-to-end network pa... |
Title: Using data network metrics, graphics, and topology to explore network characteristics |
Abstract: Yehuda Vardi introduced the term network tomography and was the first to propose and study how statistical inverse methods could be adapted to attack important network problems (Vardi, 1996). More recently, in one of his final papers, Vardi proposed notions of metrics on networks to define and measure distanc... |
Title: Functional analysis via extensions of the band depth |
Abstract: The notion of data depth has long been in use to obtain robust location and scale estimates in a multivariate setting. The depth of an observation is a measure of its centrality, with respect to a data set or a distribution. The data depths of a set of multivariate observations translates to a center-outward ... |
Title: A Practical Ontology for the Large-Scale Modeling of Scholarly Artifacts and their Usage |
Abstract: The large-scale analysis of scholarly artifact usage is constrained primarily by current practices in usage data archiving, privacy issues concerned with the dissemination of usage data, and the lack of a practical ontology for modeling the usage domain. As a remedy to the third constraint, this article prese... |
Title: Cost-minimising strategies for data labelling : optimal stopping and active learning |
Abstract: Supervised learning deals with the inference of a distribution over an output or label space $\CY$ conditioned on points in an observation space $\CX$, given a training dataset $D$ of pairs in $\CX \times \CY$. However, in a lot of applications of interest, acquisition of large amounts of observations is easy... |
Title: Pathwise coordinate optimization |
Abstract: We consider ``one-at-a-time'' coordinate-wise descent algorithms for a class of convex optimization problems. An algorithm of this kind has been proposed for the $L_1$-penalized regression (lasso) in the literature, but it seems to have been largely ignored. Indeed, it seems that coordinate-wise algorithms ar... |
Title: Defensive forecasting for optimal prediction with expert advice |
Abstract: The method of defensive forecasting is applied to the problem of prediction with expert advice for binary outcomes. It turns out that defensive forecasting is not only competitive with the Aggregating Algorithm but also handles the case of "second-guessing" experts, whose advice depends on the learner's predi... |
Title: A Data-Parallel Version of Aleph |
Abstract: This is to present work on modifying the Aleph ILP system so that it evaluates the hypothesised clauses in parallel by distributing the data-set among the nodes of a parallel or distributed machine. The paper briefly discusses MPI, the interface used to access message- passing libraries for parallel computers... |
Title: Learning Phonotactics Using ILP |
Abstract: This paper describes experiments on learning Dutch phonotactic rules using Inductive Logic Programming, a machine learning discipline based on inductive logical operators. Two different ways of approaching the problem are experimented with, and compared against each other as well as with related work on the t... |
Title: Markov Chain Modelling for Reliability Estimation of Engineering Systems at Different Scales - Some Considerations |
Abstract: The concepts of probability, statistics and stochastic theory are being successfully used in structural engineering. Markov Chain modelling is a simple stochastic process model that has found its application in both describing stochastic evolution of system and in system reliability estimation. The recent dev... |
Title: Optimal Causal Inference: Estimating Stored Information and Approximating Causal Architecture |
Abstract: We introduce an approach to inferring the causal architecture of stochastic dynamical systems that extends rate distortion theory to use causal shielding---a natural principle of learning. We study two distinct cases of causal inference: optimal causal filtering and optimal causal estimation. Filtering corres... |
Title: Updating Probabilities with Data and Moments |
Abstract: We use the method of Maximum (relative) Entropy to process information in the form of observed data and moment constraints. The generic "canonical" form of the posterior distribution for the problem of simultaneous updating with data and moments is obtained. We discuss the general problem of non-commuting con... |
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