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Title: The Margitron: A Generalised Perceptron with Margin |
Abstract: We identify the classical Perceptron algorithm with margin as a member of a broader family of large margin classifiers which we collectively call the Margitron. The Margitron, (despite its) sharing the same update rule with the Perceptron, is shown in an incremental setting to converge in a finite number of u... |
Title: The Remarkable Simplicity of Very High Dimensional Data: Application of Model-Based Clustering |
Abstract: An ultrametric topology formalizes the notion of hierarchical structure. An ultrametric embedding, referred to here as ultrametricity, is implied by a hierarchical embedding. Such hierarchical structure can be global in the data set, or local. By quantifying extent or degree of ultrametricity in a data set, w... |
Title: Sample Selection Bias Correction Theory |
Abstract: This paper presents a theoretical analysis of sample selection bias correction. The sample bias correction technique commonly used in machine learning consists of reweighting the cost of an error on each training point of a biased sample to more closely reflect the unbiased distribution. This relies on weight... |
Title: On the history and use of some standard statistical models |
Abstract: This paper tries to tell the story of the general linear model, which saw the light of day 200 years ago, and the assumptions underlying it. We distinguish three principal stages (ignoring earlier more isolated instances). The model was first proposed in the context of astronomical and geodesic observations, ... |
Title: Multivariate data analysis: The French way |
Abstract: This paper presents exploratory techniques for multivariate data, many of them well known to French statisticians and ecologists, but few well understood in North American culture. We present the general framework of duality diagrams which encompasses discriminant analysis, correspondence analysis and princip... |
Title: Learning Low-Density Separators |
Abstract: We define a novel, basic, unsupervised learning problem - learning the lowest density homogeneous hyperplane separator of an unknown probability distribution. This task is relevant to several problems in machine learning, such as semi-supervised learning and clustering stability. We investigate the question o... |
Title: High-dimensional subset recovery in noise: Sparsified measurements without loss of statistical efficiency |
Abstract: We consider the problem of estimating the support of a vector $\beta^* \in ^p$ based on observations contaminated by noise. A significant body of work has studied behavior of $\ell_1$-relaxations when applied to measurement matrices drawn from standard dense ensembles (e.g., Gaussian, Bernoulli). In this pape... |
Title: Multiple tests of association with biological annotation metadata |
Abstract: We propose a general and formal statistical framework for multiple tests of association between known fixed features of a genome and unknown parameters of the distribution of variable features of this genome in a population of interest. The known gene-annotation profiles, corresponding to the fixed features o... |
Title: Three months journeying of a Hawaiian monk seal |
Abstract: Hawaiian monk seals (Monachus schauinslandi) are endemic to the Hawaiian Islands and are the most endangered species of marine mammal that lives entirely within the jurisdiction of the United States. The species numbers around 1300 and has been declining owing, among other things, to poor juvenile survival wh... |
Title: Curse-of-dimensionality revisited: Collapse of the particle filter in very large scale systems |
Abstract: It has been widely realized that Monte Carlo methods (approximation via a sample ensemble) may fail in large scale systems. This work offers some theoretical insight into this phenomenon in the context of the particle filter. We demonstrate that the maximum of the weights associated with the sample ensemble c... |
Title: Projection pursuit for discrete data |
Abstract: This paper develops projection pursuit for discrete data using the discrete Radon transform. Discrete projection pursuit is presented as an exploratory method for finding informative low dimensional views of data such as binary vectors, rankings, phylogenetic trees or graphs. We show that for most data sets, ... |
Title: Objective Bayesian analysis under sequential experimentation |
Abstract: Objective priors for sequential experiments are considered. Common priors, such as the Jeffreys prior and the reference prior, will typically depend on the stopping rule used for the sequential experiment. New expressions for reference priors are obtained in various contexts, and computational issues involvin... |
Title: J. K. Ghosh's contribution to statistics: A brief outline |
Abstract: Professor Jayanta Kumar Ghosh has contributed massively to various areas of Statistics over the last five decades. Here, we survey some of his most important contributions. In roughly chronological order, we discuss his major results in the areas of sequential analysis, foundations, asymptotics, and Bayesian ... |
Title: Sequential tests and estimates after overrunning based on $p$-value combination |
Abstract: Often in sequential trials additional data become available after a stopping boundary has been reached. A method of incorporating such information from overrunning is developed, based on the ``adding weighted Zs'' method of combining $p$-values. This yields a combined $p$-value for the primary test and a medi... |
Title: A note on the ABC-PRC algorithm of Sissons et al. (2007) |
Abstract: This note describes the results of some tests of the ABC-PRC algorithm of Sissons et al. (PNAS, 2007), and demonstrates with a toy example that the method does not converge on the true posterior distribution. |
Title: Cognitive Architecture for Direction of Attention Founded on Subliminal Memory Searches, Pseudorandom and Nonstop |
Abstract: By way of explaining how a brain works logically, human associative memory is modeled with logical and memory neurons, corresponding to standard digital circuits. The resulting cognitive architecture incorporates basic psychological elements such as short term and long term memory. Novel to the architecture a... |
Title: Fuzzy sets in nonparametric Bayes regression |
Abstract: A simple Bayesian approach to nonparametric regression is described using fuzzy sets and membership functions. Membership functions are interpreted as likelihood functions for the unknown regression function, so that with the help of a reference prior they can be transformed to prior density functions. The un... |
Title: Statistical region-based active contours with exponential family observations |
Abstract: In this paper, we focus on statistical region-based active contour models where image features (e.g. intensity) are random variables whose distribution belongs to some parametric family (e.g. exponential) rather than confining ourselves to the special Gaussian case. Using shape derivation tools, our effort fo... |
Title: Region-based active contour with noise and shape priors |
Abstract: In this paper, we propose to combine formally noise and shape priors in region-based active contours. On the one hand, we use the general framework of exponential family as a prior model for noise. On the other hand, translation and scale invariant Legendre moments are considered to incorporate the shape prio... |
Title: Objective Bayes testing of Poisson versus inflated Poisson models |
Abstract: The Poisson distribution is often used as a standard model for count data. Quite often, however, such data sets are not well fit by a Poisson model because they have more zeros than are compatible with this model. For these situations, a zero-inflated Poisson (ZIP) distribution is often proposed. This article... |
Title: Consistent selection via the Lasso for high dimensional approximating regression models |
Abstract: In this article we investigate consistency of selection in regression models via the popular Lasso method. Here we depart from the traditional linear regression assumption and consider approximations of the regression function $f$ with elements of a given dictionary of $M$ functions. The target for consistenc... |
Title: Asymptotic optimality of a cross-validatory predictive approach to linear model selection |
Abstract: In this article we study the asymptotic predictive optimality of a model selection criterion based on the cross-validatory predictive density, already available in the literature. For a dependent variable and associated explanatory variables, we consider a class of linear models as approximations to the true ... |
Title: Risk and resampling under model uncertainty |
Abstract: In statistical exercises where there are several candidate models, the traditional approach is to select one model using some data driven criterion and use that model for estimation, testing and other purposes, ignoring the variability of the model selection process. We discuss some problems associated with t... |
Title: A Bayesian semi-parametric model for small area estimation |
Abstract: In public health management there is a need to produce subnational estimates of health outcomes. Often, however, funds are not available to collect samples large enough to produce traditional survey sample estimates for each subnational area. Although parametric hierarchical methods have been successfully use... |
Title: Compressing Binary Decision Diagrams |
Abstract: The paper introduces a new technique for compressing Binary Decision Diagrams in those cases where random access is not required. Using this technique, compression and decompression can be done in linear time in the size of the BDD and compression will in many cases reduce the size of the BDD to 1-2 bits per ... |
Title: A hierarchical Bayesian approach for estimating the origin of a mixed population |
Abstract: We propose a hierarchical Bayesian model to estimate the proportional contribution of source populations to a newly founded colony. Samples are derived from the first generation offspring in the colony, but mating may occur preferentially among migrants from the same source population. Genotypes of the newly ... |
Title: Kendall's tau in high-dimensional genomic parsimony |
Abstract: High-dimensional data models, often with low sample size, abound in many interdisciplinary studies, genomics and large biological systems being most noteworthy. The conventional assumption of multinormality or linearity of regression may not be plausible for such models which are likely to be statistically co... |
Title: Orthogonalized smoothing for rescaled spike and slab models |
Abstract: Rescaled spike and slab models are a new Bayesian variable selection method for linear regression models. In high dimensional orthogonal settings such models have been shown to possess optimal model selection properties. We review background theory and discuss applications of rescaled spike and slab models to... |
Title: An ensemble approach to improved prediction from multitype data |
Abstract: We have developed a strategy for the analysis of newly available binary data to improve outcome predictions based on existing data (binary or non-binary). Our strategy involves two modeling approaches for the newly available data, one combining binary covariate selection via LASSO with logistic regression and... |
Title: Sharp failure rates for the bootstrap particle filter in high dimensions |
Abstract: We prove that the maximum of the sample importance weights in a high-dimensional Gaussian particle filter converges to unity unless the ensemble size grows exponentially in the system dimension. Our work is motivated by and parallels the derivations of Bengtsson, Bickel and Li (2007); however, we weaken their... |
Title: Computational Representation of Linguistic Structures using Domain-Specific Languages |
Abstract: We describe a modular system for generating sentences from formal definitions of underlying linguistic structures using domain-specific languages. The system uses Java in general, Prolog for lexical entries and custom domain-specific languages based on Functional Grammar and Functional Discourse Grammar notat... |
Title: Design of Attitude Stability System for Prolate Dual-spin Satellite in Its Inclined Elliptical Orbit |
Abstract: In general, most of communication satellites were designed to be operated in geostationary orbit. And many of them were designed in prolate dual-spin configuration. As a prolate dual-spin vehicle, they have to be stabilized against their internal energy dissipation effect. Several countries that located in so... |
Title: Exploring a type-theoretic approach to accessibility constraint modelling |
Abstract: The type-theoretic modelling of DRT that [degroote06] proposed features continuations for the management of the context in which a clause has to be interpreted. This approach, while keeping the standard definitions of quantifier scope, translates the rules of the accessibility constraints of discourse referen... |
Title: Logic programming with social features |
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