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Title: Bandwidth selection for kernel estimation in mixed multi-dimensional spaces |
Abstract: Kernel estimation techniques, such as mean shift, suffer from one major drawback: the kernel bandwidth selection. The bandwidth can be fixed for all the data set or can vary at each points. Automatic bandwidth selection becomes a real challenge in case of multidimensional heterogeneous features. This paper pr... |
Title: Nonparametric estimation for L\'evy processes from low-frequency observations |
Abstract: We suppose that a L\'evy process is observed at discrete time points. A rather general construction of minimum-distance estimators is shown to give consistent estimators of the L\'evy-Khinchine characteristics as the number of observations tends to infinity, keeping the observation distance fixed. For a speci... |
Title: Uniform limit laws of the logarithm for nonparametric estimators of the regression function in presence of censored data |
Abstract: In this paper, we establish uniform-in-bandwidth limit laws of the logarithm for nonparametric Inverse Probability of Censoring Weighted (I.P.C.W.) estimators of the multivariate regression function under random censorship. A similar result is deduced for estimators of the conditional distribution function. T... |
Title: Toward Psycho-robots |
Abstract: We try to perform geometrization of psychology by representing mental states, <<ideas>>, by points of a metric space, <<mental space>>. Evolution of ideas is described by dynamical systems in metric mental space. We apply the mental space approach for modeling of flows of unconscious and conscious information... |
Title: Adjusted Viterbi training for hidden Markov models |
Abstract: To estimate the emission parameters in hidden Markov models one commonly uses the EM algorithm or its variation. Our primary motivation, however, is the Philips speech recognition system wherein the EM algorithm is replaced by the Viterbi training algorithm. Viterbi training is faster and computationally less... |
Title: Bootstrapping Deep Lexical Resources: Resources for Courses |
Abstract: We propose a range of deep lexical acquisition methods which make use of morphological, syntactic and ontological language resources to model word similarity and bootstrap from a seed lexicon. The different methods are deployed in learning lexical items for a precision grammar, and shown to each have strength... |
Title: Learning for Dynamic Bidding in Cognitive Radio Resources |
Abstract: In this paper, we model the various wireless users in a cognitive radio network as a collection of selfish, autonomous agents that strategically interact in order to acquire the dynamically available spectrum opportunities. Our main focus is on developing solutions for wireless users to successfully compete w... |
Title: Autoencoder, Principal Component Analysis and Support Vector Regression for Data Imputation |
Abstract: Data collection often results in records that have missing values or variables. This investigation compares 3 different data imputation models and identifies their merits by using accuracy measures. Autoencoder Neural Networks, Principal components and Support Vector regression are used for prediction and com... |
Title: Supervised Machine Learning with a Novel Kernel Density Estimator |
Abstract: In recent years, kernel density estimation has been exploited by computer scientists to model machine learning problems. The kernel density estimation based approaches are of interest due to the low time complexity of either O(n) or O(n*log(n)) for constructing a classifier, where n is the number of sampling ... |
Title: A note on calculating autocovariances of periodic ARMA models |
Abstract: An analytically simple and tractable formula for the start-up autocovariances of periodic ARMA (PARMA) models is provided. |
Title: Bayesian Classification and Regression with High Dimensional Features |
Abstract: This thesis responds to the challenges of using a large number, such as thousands, of features in regression and classification problems. There are two situations where such high dimensional features arise. One is when high dimensional measurements are available, for example, gene expression data produced by ... |
Title: On Birnbaum-Saunders Inference |
Abstract: The Birnbaum-Saunders distribution, also known as the fatigue-life distribution, is frequently used in reliability studies. We obtain adjustments to the Birnbaum--Saunders profile likelihood function. The modified versions of the likelihood function were obtained for both the shape and scale parameters, i.e.,... |
Title: Quasi-maximum likelihood estimation of periodic GARCH processes |
Abstract: This paper establishes the strong consistency and asymptotic normality of the quasi-maximum likelihood estimator (QMLE) for a GARCH process with periodically time-varying parameters. We first give a necessary and sufficient condition for the existence of a strictly periodically stationary solution for the per... |
Title: Simulated Annealing: Rigorous finite-time guarantees for optimization on continuous domains |
Abstract: Simulated annealing is a popular method for approaching the solution of a global optimization problem. Existing results on its performance apply to discrete combinatorial optimization where the optimization variables can assume only a finite set of possible values. We introduce a new general formulation of si... |
Title: Two polynomial representations of experimental design |
Abstract: In the context of algebraic statistics an experimental design is described by a set of polynomials called the design ideal. This, in turn, is generated by finite sets of polynomials. Two types of generating sets are mostly used in the literature: Groebner bases and indicator functions. We briefly describe the... |
Title: Supervised learning on graphs of spatio-temporal similarity in satellite image sequences |
Abstract: High resolution satellite image sequences are multidimensional signals composed of spatio-temporal patterns associated to numerous and various phenomena. Bayesian methods have been previously proposed in (Heas and Datcu, 2005) to code the information contained in satellite image sequences in a graph represent... |
Title: Low Dimensional Embedding of fMRI datasets |
Abstract: We propose a novel method to embed a functional magnetic resonance imaging (fMRI) dataset in a low-dimensional space. The embedding optimally preserves the local functional coupling between fMRI time series and provides a low-dimensional coordinate system for detecting activated voxels. To compute the embeddi... |
Title: A quantile-copula approach to conditional density estimation |
Abstract: We present a new non-parametric estimator of the conditional density of the kernel type. It is based on an efficient transformation of the data by quantile transform. By use of the copula representation, it turns out to have a remarkable product form. We study its asymptotic properties and compare its bias an... |
Title: Algebraic causality: Bayes nets and beyond |
Abstract: The relationship between algebraic geometry and the inferential framework of the Bayesian Networks with hidden variables has now been fruitfully explored and exploited by a number of authors. More recently the algebraic formulation of Causal Bayesian Networks has also been investigated in this context. After ... |
Title: The causal manipulation of chain event graphs |
Abstract: Discrete Bayesian Networks have been very successful as a framework both for inference and for expressing certain causal hypotheses. In this paper we present a class of graphical models called the chain event graph (CEG) models, that generalises the class of discrete BN models. It provides a flexible and expr... |
Title: Mutual information for the selection of relevant variables in spectrometric nonlinear modelling |
Abstract: Data from spectrophotometers form vectors of a large number of exploitable variables. Building quantitative models using these variables most often requires using a smaller set of variables than the initial one. Indeed, a too large number of input variables to a model results in a too large number of paramete... |
Title: Fast Algorithm and Implementation of Dissimilarity Self-Organizing Maps |
Abstract: In many real world applications, data cannot be accurately represented by vectors. In those situations, one possible solution is to rely on dissimilarity measures that enable sensible comparison between observations. Kohonen's Self-Organizing Map (SOM) has been adapted to data described only through their dis... |
Title: A Bayesian Approach to Network Modularity |
Abstract: We present an efficient, principled, and interpretable technique for inferring module assignments and for identifying the optimal number of modules in a given network. We show how several existing methods for finding modules can be described as variant, special, or limiting cases of our work, and how the meth... |
Title: Maximum Likelihood Estimation in Latent Class Models For Contingency Table Data |
Abstract: Statistical models with latent structure have a history going back to the 1950s and have seen widespread use in the social sciences and, more recently, in computational biology and in machine learning. Here we study the basic latent class model proposed originally by the sociologist Paul F. Lazarfeld for cate... |
Title: Locally Adaptive Nonparametric Binary Regression |
Abstract: A nonparametric and locally adaptive Bayesian estimator is proposed for estimating a binary regression. Flexibility is obtained by modeling the binary regression as a mixture of probit regressions with the argument of each probit regression having a thin plate spline prior with its own smoothing parameter and... |
Title: On The Density Estimation by Super-Parametric Method |
Abstract: The super-parametric density estimators and its related algorism were suggested by Y. -S. Tsai et al [7]. The number of parameters is unlimited in the super- parametric estimators and it is a general theory in sense of unifying or connecting nonparametric and parametric estimators. Before applying to numerica... |
Title: Une adaptation des cartes auto-organisatrices pour des donn\'ees d\'ecrites par un tableau de dissimilarit\'es |
Abstract: Many data analysis methods cannot be applied to data that are not represented by a fixed number of real values, whereas most of real world observations are not readily available in such a format. Vector based data analysis methods have therefore to be adapted in order to be used with non standard complex data... |
Title: Self-organizing maps and symbolic data |
Abstract: In data analysis new forms of complex data have to be considered like for example (symbolic data, functional data, web data, trees, SQL query and multimedia data, ...). In this context classical data analysis for knowledge discovery based on calculating the center of gravity can not be used because input are ... |
Title: Fast Selection of Spectral Variables with B-Spline Compression |
Abstract: The large number of spectral variables in most data sets encountered in spectral chemometrics often renders the prediction of a dependent variable uneasy. The number of variables hopefully can be reduced, by using either projection techniques or selection methods; the latter allow for the interpretation of th... |
Title: Resampling methods for parameter-free and robust feature selection with mutual information |
Abstract: Combining the mutual information criterion with a forward feature selection strategy offers a good trade-off between optimality of the selected feature subset and computation time. However, it requires to set the parameter(s) of the mutual information estimator and to determine when to halt the forward proced... |
Title: 1953: An unrecognized summit in human genetic linkage analysis |
Abstract: This paper summarizes and discusses the methodological research in human genetic linkage analysis, leading up to and following from the paper of C. A. B. Smith presented as a Royal Statistical Society discussion paper in 1953. This paper was given as the Fisher XXVII Memorial Lecture, in Cambridge, December 4... |
Title: Estimating copula measure using ranks and subsampling: a simulation study |
Abstract: We describe here a new method to estimate copula measure. From N observations of two variables X and Y, we draw a huge number m of subsamples (size n<N), and we compute the joint ranks in these subsamples. Then, for each bivariate rank (p,q) (0<p,q<n+1), we count the number of subsamples such that there exist... |
Title: Flexible least squares for temporal data mining and statistical arbitrage |
Abstract: A number of recent emerging applications call for studying data streams, potentially infinite flows of information updated in real-time. When multiple co-evolving data streams are observed, an important task is to determine how these streams depend on each other, accounting for dynamic dependence patterns wit... |
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