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Title: Computational Intelligence Characterization Method of Semiconductor Device |
Abstract: Characterization of semiconductor devices is used to gather as much data about the device as possible to determine weaknesses in design or trends in the manufacturing process. In this paper, we propose a novel multiple trip point characterization concept to overcome the constraint of single trip point concept... |
Title: A Conversation with Dorothy Gilford |
Abstract: In 1946, Public Law 588 of the 79th Congress established the Office of Naval Research (ONR). Its mission was to plan, foster and encourage scientific research in support of Naval problems. The establishment of ONR predates the National Science Foundation and initiated the refocusing of scientific infrastructu... |
Title: Node discovery problem for a social network |
Abstract: Methods to solve a node discovery problem for a social network are presented. Covert nodes refer to the nodes which are not observable directly. They transmit the influence and affect the resulting collaborative activities among the persons in a social network, but do not appear in the surveillance logs which... |
Title: The entropy of keys derived from laser speckle |
Abstract: Laser speckle has been proposed in a number of papers as a high-entropy source of unpredictable bits for use in security applications. Bit strings derived from speckle can be used for a variety of security purposes such as identification, authentication, anti-counterfeiting, secure key storage, random number ... |
Title: Struggles with Survey Weighting and Regression Modeling |
Abstract: The general principles of Bayesian data analysis imply that models for survey responses should be constructed conditional on all variables that affect the probability of inclusion and nonresponse, which are also the variables used in survey weighting and clustering. However, such models can quickly become ver... |
Title: Comment: Struggles with Survey Weighting and Regression Modeling |
Abstract: Comment: Struggles with Survey Weighting and Regression Modeling [arXiv:0710.5005] |
Title: Comment: Struggles with Survey Weighting and Regression Modeling |
Abstract: Comment: Struggles with Survey Weighting and Regression Modeling [arXiv:0710.5005] |
Title: Comment: Struggles with Survey Weighting and Regression Modeling |
Abstract: Comment: Struggles with Survey Weighting and Regression Modeling [arXiv:0710.5005] |
Title: Comment: Struggles with Survey Weighting and Regression Modeling |
Abstract: Comment: Struggles with Survey Weighting and Regression Modeling [arXiv:0710.5005] |
Title: Comment: Struggles with Survey Weighting and Regression Modeling |
Abstract: Comment: Struggles with Survey Weighting and Regression Modeling [arXiv:0710.5005] |
Title: Rejoinder: Struggles with Survey Weighting and Regression Modeling |
Abstract: Rejoinder: Struggles with Survey Weighting and Regression Modeling [arXiv:0710.5005] |
Title: The William Kruskal Legacy: 1919--2005 |
Abstract: William Kruskal (Bill) was a distinguished statistician who spent virtually his entire professional career at the University of Chicago, and who had a lasting impact on the Institute of Mathematical Statistics and on the field of statistics more broadly, as well as on many who came in contact with him. Bill p... |
Title: A Tribute to Bill Kruskal |
Abstract: Discussion of ``The William Kruskal Legacy: 1919--2005'' by Stephen E. Fienberg, Stephen M. Stigler and Judith M. Tanur [arXiv:0710.5063] |
Title: William H. Kruskal and the Development of Coordinate-Free Methods |
Abstract: Discussion of ``The William Kruskal Legacy: 1919--2005'' by Stephen E. Fienberg, Stephen M. Stigler and Judith M. Tanur [arXiv:0710.5063] |
Title: William Kruskal: My Scholarly and Scientific Model |
Abstract: Discussion of ``The William Kruskal Legacy: 1919--2005'' by Stephen E. Fienberg, Stephen M. Stigler and Judith M. Tanur [arXiv:0710.5063] |
Title: Working with Bill Kruskal: From 1950 Onward |
Abstract: Discussion of ``The William Kruskal Legacy: 1919--2005'' by Stephen E. Fienberg, Stephen M. Stigler and Judith M. Tanur [arXiv:0710.5063] |
Title: Bill Kruskal and the Committee on National Statistics |
Abstract: Discussion of ``The William Kruskal Legacy: 1919--2005'' by Stephen E. Fienberg, Stephen M. Stigler and Judith M. Tanur [arXiv:0710.5063] |
Title: William Kruskal Remembered |
Abstract: Discussion of ``The William Kruskal Legacy: 1919--2005'' by Stephen E. Fienberg, Stephen M. Stigler and Judith M. Tanur [arXiv:0710.5063] |
Title: William H. Kruskal, Mentor and Friend |
Abstract: Discussion of ``The William Kruskal Legacy: 1919--2005'' by Stephen E. Fienberg, Stephen M. Stigler and Judith M. Tanur [arXiv:0710.5063] |
Title: Particle Filters for Multiscale Diffusions |
Abstract: We consider multiscale stochastic systems that are partially observed at discrete points of the slow time scale. We introduce a particle filter that takes advantage of the multiscale structure of the system to efficiently approximate the optimal filter. |
Title: Combining haplotypers |
Abstract: Statistically resolving the underlying haplotype pair for a genotype measurement is an important intermediate step in gene mapping studies, and has received much attention recently. Consequently, a variety of methods for this problem have been developed. Different methods employ different statistical models, ... |
Title: A geometric approach to maximum likelihood estimation of the functional principal components from sparse longitudinal data |
Abstract: In this paper, we consider the problem of estimating the eigenvalues and eigenfunctions of the covariance kernel (i.e., the functional principal components) from sparse and irregularly observed longitudinal data. We approach this problem through a maximum likelihood method assuming that the covariance kernel ... |
Title: Some Reflections on the Task of Content Determination in the Context of Multi-Document Summarization of Evolving Events |
Abstract: Despite its importance, the task of summarizing evolving events has received small attention by researchers in the field of multi-document summariztion. In a previous paper (Afantenos et al. 2007) we have presented a methodology for the automatic summarization of documents, emitted by multiple sources, which ... |
Title: Parameter Estimation for Partially Observed Hypoelliptic Diffusions |
Abstract: Hypoelliptic diffusion processes can be used to model a variety of phenomena in applications ranging from molecular dynamics to audio signal analysis. We study parameter estimation for such processes in situations where we observe some components of the solution at discrete times. Since exact likelihoods for ... |
Title: Discriminated Belief Propagation |
Abstract: Near optimal decoding of good error control codes is generally a difficult task. However, for a certain type of (sufficiently) good codes an efficient decoding algorithm with near optimal performance exists. These codes are defined via a combination of constituent codes with low complexity trellis representat... |
Title: Code Similarity on High Level Programs |
Abstract: This paper presents a new approach for code similarity on High Level programs. Our technique is based on Fast Dynamic Time Warping, that builds a warp path or points relation with local restrictions. The source code is represented into Time Series using the operators inside programming languages that makes po... |
Title: An Elegant Method for Generating Multivariate Poisson Random Variable |
Abstract: Generating multivariate Poisson data is essential in many applications. Current simulation methods suffer from limitations ranging from computational complexity to restrictions on the structure of the correlation matrix. We propose a computationally efficient and conceptually appealing method for generating m... |
Title: Nonparametric Conditional Inference for Regression Coefficients with Application to Configural Polysampling |
Abstract: We consider inference procedures, conditional on an observed ancillary statistic, for regression coefficients under a linear regression setup where the unknown error distribution is specified nonparametrically. We establish conditional asymptotic normality of the regression coefficient estimators under regula... |
Title: On estimating covariances between many assets with histories of highly variable length |
Abstract: Quantitative portfolio allocation requires the accurate and tractable estimation of covariances between a large number of assets, whose histories can greatly vary in length. Such data are said to follow a monotone missingness pattern, under which the likelihood has a convenient factorization. Upon further ass... |
Title: 2-level fractional factorial designs which are the union of non trivial regular designs |
Abstract: Every fraction is a union of points, which are trivial regular fractions. To characterize non trivial decomposition, we derive a condition for the inclusion of a regular fraction as follows. Let $F = \sum_\alpha b_\alpha X^\alpha$ be the indicator polynomial of a generic fraction, see Fontana et al, JSPI 2000... |
Title: Implementing Quasi-Monte Carlo Simulations with Linear Transformations |
Abstract: Pricing exotic multi-asset path-dependent options requires extensive Monte Carlo simulations. In the recent years the interest to the Quasi-monte Carlo technique has been renewed and several results have been proposed in order to improve its efficiency with the notion of effective dimension. To this aim, Imai... |
Title: Some aspects of extreme value theory under serial dependence |
Abstract: On the occasion of Laurens de Haan's 70th birthday, we discuss two aspects of the statistical inference on the extreme value behavior of time series with a particular emphasis on his important contributions. First, the performance of a direct marginal tail analysis is compared with that of a model-based appro... |
Title: Supervised Machine Learning with a Novel Pointwise Density Estimator |
Abstract: This article proposes a novel density estimation based algorithm for carrying out supervised machine learning. The proposed algorithm features O(n) time complexity for generating a classifier, where n is the number of sampling instances in the training dataset. This feature is highly desirable in contemporary... |
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