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Title: Entropy Concentration and the Empirical Coding Game |
Abstract: We give a characterization of Maximum Entropy/Minimum Relative Entropy inference by providing two `strong entropy concentration' theorems. These theorems unify and generalize Jaynes' `concentration phenomenon' and Van Campenhout and Cover's `conditional limit theorem'. The theorems characterize exactly in wha... |
Title: A simulation study comparing likelihood and non-likelihood approaches in analyzing overdispersed count data |
Abstract: Overdispersed count data are modelled with likelihood and non-likelihood approaches. Likelihood approaches include the Poisson mixtures with three distributions, the gamma, the lognormal, and the inverse Gaussian distributions. Non-likelihood approaches include the robust sandwich estimator and quasilikelihoo... |
Title: Variable Neighborhood Search for the University Lecturer-Student Assignment Problem |
Abstract: The paper presents a study of local search heuristics in general and variable neighborhood search in particular for the resolution of an assignment problem studied in the practical work of universities. Here, students have to be assigned to scientific topics which are proposed and supported by members of staf... |
Title: Applications of Universal Source Coding to Statistical Analysis of Time Series |
Abstract: We show how universal codes can be used for solving some of the most important statistical problems for time series. By definition, a universal code (or a universal lossless data compressor) can compress any sequence generated by a stationary and ergodic source asymptotically to the Shannon entropy, which, in... |
Title: A New Framework of Multistage Estimation |
Abstract: In this paper, we have established a unified framework of multistage parameter estimation. We demonstrate that a wide variety of statistical problems such as fixed-sample-size interval estimation, point estimation with error control, bounded-width confidence intervals, interval estimation following hypothesis... |
Title: Necessary and Sufficient Conditions for Success of the Nuclear Norm Heuristic for Rank Minimization |
Abstract: Minimizing the rank of a matrix subject to constraints is a challenging problem that arises in many applications in control theory, machine learning, and discrete geometry. This class of optimization problems, known as rank minimization, is NP-HARD, and for most practical problems there are no efficient algor... |
Title: Predictive Hypothesis Identification |
Abstract: While statistics focusses on hypothesis testing and on estimating (properties of) the true sampling distribution, in machine learning the performance of learning algorithms on future data is the primary issue. In this paper we bridge the gap with a general principle (PHI) that identifies hypotheses with best ... |
Title: Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning |
Abstract: For supervised and unsupervised learning, positive definite kernels allow to use large and potentially infinite dimensional feature spaces with a computational cost that only depends on the number of observations. This is usually done through the penalization of predictor functions by Euclidean or Hilbertian ... |
Title: When is there a representer theorem? Vector versus matrix regularizers |
Abstract: We consider a general class of regularization methods which learn a vector of parameters on the basis of linear measurements. It is well known that if the regularizer is a nondecreasing function of the inner product then the learned vector is a linear combination of the input data. This result, known as the \... |
Title: ECOLANG - Communications Language for Ecological Simulations Network |
Abstract: This document describes the communication language used in one multiagent system environment for ecological simulations, based on EcoDynamo simulator application linked with several intelligent agents and visualisation applications, and extends the initial definition of the language. The agents actions and pe... |
Title: Agent-based Ecological Model Calibration - on the Edge of a New Approach |
Abstract: The purpose of this paper is to present a new approach to ecological model calibration -- an agent-based software. This agent works on three stages: 1- It builds a matrix that synthesizes the inter-variable relationships; 2- It analyses the steady-state sensitivity of different variables to different paramete... |
Title: A Regularized Method for Selecting Nested Groups of Relevant Genes from Microarray Data |
Abstract: Gene expression analysis aims at identifying the genes able to accurately predict biological parameters like, for example, disease subtyping or progression. While accurate prediction can be achieved by means of many different techniques, gene identification, due to gene correlation and the limited number of a... |
Title: Automatic Identification and Data Extraction from 2-Dimensional Plots in Digital Documents |
Abstract: Most search engines index the textual content of documents in digital libraries. However, scholarly articles frequently report important findings in figures for visual impact and the contents of these figures are not indexed. These contents are often invaluable to the researcher in various fields, for the pur... |
Title: Asymptotic tail properties of the distributions in the class of dispersion models |
Abstract: The class of dispersion models introduced by J\orgensen (1997b) covers many known distributions such as the normal, Student t, gamma, inverse Gaussian, hyperbola, von-Mises, among others. We study the small dispersion asymptotic (J\orgensen, 1987b) behavior of the probability density functions of dispersion m... |
Title: The distribution of Pearson residuals in generalized linear models |
Abstract: In general, the distribution of residuals cannot be obtained explicitly. We give an asymptotic formula for the density of Pearson residuals in continuous generalized linear models corrected to order $n^-1$, where $n$ is the sample size. We define corrected Pearson residuals for these models that, to this orde... |
Title: Explicit expressions for moments of the beta Weibull distribution |
Abstract: The beta Weibull distribution was introduced by Famoye et al. (2005) and studied by these authors. However, they do not give explicit expressions for the moments. We now derive explicit closed form expressions for the cumulative distribution function and for the moments of this distribution. We also give an a... |
Title: Some results for beta Fr\'echet distribution |
Abstract: Nadarajah and Gupta (2004) introduced the beta Fr\'echet (BF) distribution, which is a generalization of the exponentiated Fr\'echet (EF) and Fr\'echet distributions, and obtained the probability density and cumulative distribution functions. However, they do not investigated its moments and the order statist... |
Title: Improved estimators for a general class of beta regression models |
Abstract: In this paper we consider an extension of the beta regression model proposed by Ferrari and Cribari-Neto (2004). We extend their model in two different ways, first, we let the regression structure be nonlinear, second, we allow a regression structure for the precision parameter, moreover, this regression stru... |
Title: The Beta Generalized Exponential Distribution |
Abstract: We introduce the beta generalized exponential distribution that includes the beta exponential and generalized exponential distributions as special cases. We provide a comprehensive mathematical treatment of this distribution. We derive the moment generating function and the $r$th moment thus generalizing some... |
Title: A Generalization of the Exponential-Poisson Distribution |
Abstract: The two-parameter distribution known as exponential-Poisson (EP) distribution, which has decreasing failure rate, was introduced by Kus (2007). In this paper we generalize the EP distribution and show that the failure rate of the new distribution can be decreasing or increasing. The failure rate can also be u... |
Title: Randomized Distributed Configuration Management of Wireless Networks: Multi-layer Markov Random Fields and Near-Optimality |
Abstract: Distributed configuration management is imperative for wireless infrastructureless networks where each node adjusts locally its physical and logical configuration through information exchange with neighbors. Two issues remain open. The first is the optimality. The second is the complexity. We study these issu... |
Title: Low congestion online routing and an improved mistake bound for online prediction of graph labeling |
Abstract: In this paper, we show a connection between a certain online low-congestion routing problem and an online prediction of graph labeling. More specifically, we prove that if there exists a routing scheme that guarantees a congestion of $\alpha$ on any edge, there exists an online prediction algorithm with mista... |
Title: Clustered Multi-Task Learning: A Convex Formulation |
Abstract: In multi-task learning several related tasks are considered simultaneously, with the hope that by an appropriate sharing of information across tasks, each task may benefit from the others. In the context of learning linear functions for supervised classification or regression, this can be achieved by includin... |
Title: A randomized algorithm for principal component analysis |
Abstract: Principal component analysis (PCA) requires the computation of a low-rank approximation to a matrix containing the data being analyzed. In many applications of PCA, the best possible accuracy of any rank-deficient approximation is at most a few digits (measured in the spectral norm, relative to the spectral n... |
Title: Coupling Control Variates for Markov Chain Monte Carlo |
Abstract: We show that Markov couplings can be used to improve the accuracy of Markov chain Monte Carlo calculations in some situations where the steady-state probability distribution is not explicitly known. The technique generalizes the notion of control variates from classical Monte Carlo integration. We illustrate ... |
Title: Electricity Demand and Energy Consumption Management System |
Abstract: This project describes the electricity demand and energy consumption management system and its application to Southern Peru smelter. It is composed of an hourly demand-forecasting module and of a simulation component for a plant electrical system. The first module was done using dynamic neural networks with b... |
Title: Normalized Information Distance |
Abstract: The normalized information distance is a universal distance measure for objects of all kinds. It is based on Kolmogorov complexity and thus uncomputable, but there are ways to utilize it. First, compression algorithms can be used to approximate the Kolmogorov complexity if the objects have a string representa... |
Title: The Weibull-Geometric distribution |
Abstract: In this paper we introduce, for the first time, the Weibull-Geometric distribution which generalizes the exponential-geometric distribution proposed by Adamidis and Loukas (1998). The hazard function of the last distribution is monotone decreasing but the hazard function of the new distribution can take more ... |
Title: Algorithmic information theory |
Abstract: We introduce algorithmic information theory, also known as the theory of Kolmogorov complexity. We explain the main concepts of this quantitative approach to defining `information'. We discuss the extent to which Kolmogorov's and Shannon's information theory have a common purpose, and where they are fundament... |
Title: Predicting Abnormal Returns From News Using Text Classification |
Abstract: We show how text from news articles can be used to predict intraday price movements of financial assets using support vector machines. Multiple kernel learning is used to combine equity returns with text as predictive features to increase classification performance and we develop an analytic center cutting pl... |
Title: Stability Selection |
Abstract: Estimation of structure, such as in variable selection, graphical modelling or cluster analysis is notoriously difficult, especially for high-dimensional data. We introduce stability selection. It is based on subsampling in combination with (high-dimensional) selection algorithms. As such, the method is extre... |
Title: Finding links and initiators: a graph reconstruction problem |
Abstract: Consider a 0-1 observation matrix M, where rows correspond to entities and columns correspond to signals; a value of 1 (or 0) in cell (i,j) of M indicates that signal j has been observed (or not observed) in entity i. Given such a matrix we study the problem of inferring the underlying directed links between ... |
Title: Kinetostatic Performance of a Planar Parallel Mechanism with Variable Actuation |
Abstract: This paper deals with a new planar parallel mechanism with variable actuation and its kinetostatic performance. A drawback of parallel mechanisms is the non homogeneity of kinetostatic performance within their workspace. The common approach to solve this problem is the introduction of actuation redundancy, th... |
Title: Supervised Dictionary Learning |
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