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Title: Discussion of Likelihood Inference for Models with Unobservables: Another View |
Abstract: Discussion of "Likelihood Inference for Models with Unobservables: Another View" by Youngjo Lee and John A. Nelder [arXiv:1010.0303] |
Title: Discussion of Likelihood Inference for Models with Unobservables: Another View |
Abstract: Discussion of "Likelihood Inference for Models with Unobservables: Another View" by Youngjo Lee and John A. Nelder [arXiv:1010.0303] |
Title: Decoding the H-likelihood |
Abstract: Discussion of "Likelihood Inference for Models with Unobservables: Another View" by Youngjo Lee and John A. Nelder [arXiv:1010.0303] |
Title: Rejoinder: Likelihood Inference for Models with Unobservables Another View |
Abstract: Rejoinder to "Likelihood Inference for Models with Unobservables: Another View" by Youngjo Lee and John A. Nelder [arXiv:1010.0303] |
Title: Tuning Tempered Transitions |
Abstract: The method of tempered transitions was proposed by Neal (1996) for tackling the difficulties arising when using Markov chain Monte Carlo to sample from multimodal distributions. In common with methods such as simulated tempering and Metropolis-coupled MCMC, the key idea is to utilise a series of successively ... |
Title: A Platform-independent Programming Environment for Robot Control |
Abstract: The development of robot control programs is a complex task. Many robots are different in their electrical and mechanical structure which is also reflected in the software. Specific robot software environments support the program development, but are mainly text-based and usually applied by experts in the fie... |
Title: Hidden Markov Models with Multiple Observation Processes |
Abstract: We consider a hidden Markov model with multiple observation processes, one of which is chosen at each point in time by a policy---a deterministic function of the information state---and attempt to determine which policy minimises the limiting expected entropy of the information state. Focusing on a special ca... |
Title: Damage spreading and coupling in Markov chains |
Abstract: In this paper, we relate the coupling of Markov chains, at the basis of perfect sampling methods, with damage spreading, which captures the chaotic nature of stochastic dynamics. For two-dimensional spin glasses and hard spheres we point out that the obstacle to the application of perfect-sampling schemes is ... |
Title: Analysis of 24-Hour Ambulatory Blood Pressure Monitoring Data using Orthonormal Polynomials in the Linear Mixed Model |
Abstract: The use of 24-hour ambulatory blood pressure monitoring (ABPM) in clinical practice and observational epidemiological studies has grown considerably in the past 25 years. ABPM is a very effective technique for assessing biological, environmental, and drug effects on blood pressure. In order to enhance the eff... |
Title: Mixed-Membership Stochastic Block-Models for Transactional Networks |
Abstract: Transactional network data can be thought of as a list of one-to-many communications(e.g., email) between nodes in a social network. Most social network models convert this type of data into binary relations between pairs of nodes. We develop a latent mixed membership model capable of modeling richer forms of... |
Title: Profile Based Sub-Image Search in Image Databases |
Abstract: Sub-image search with high accuracy in natural images still remains a challenging problem. This paper proposes a new feature vector called profile for a keypoint in a bag of visual words model of an image. The profile of a keypoint captures the spatial geometry of all the other keypoints in an image with resp... |
Title: Time Series Classification by Class-Specific Mahalanobis Distance Measures |
Abstract: To classify time series by nearest neighbors, we need to specify or learn one or several distance measures. We consider variations of the Mahalanobis distance measures which rely on the inverse covariance matrix of the data. Unfortunately --- for time series data --- the covariance matrix has often low rank. ... |
Title: Using parallel computation to improve Independent Metropolis--Hastings based estimation |
Abstract: In this paper, we consider the implications of the fact that parallel raw-power can be exploited by a generic Metropolis--Hastings algorithm if the proposed values are independent. In particular, we present improvements to the independent Metropolis--Hastings algorithm that significantly decrease the variance... |
Title: Algorithmic and Statistical Perspectives on Large-Scale Data Analysis |
Abstract: In recent years, ideas from statistics and scientific computing have begun to interact in increasingly sophisticated and fruitful ways with ideas from computer science and the theory of algorithms to aid in the development of improved worst-case algorithms that are useful for large-scale scientific and Intern... |
Title: Algorithms for nonnegative matrix factorization with the beta-divergence |
Abstract: This paper describes algorithms for nonnegative matrix factorization (NMF) with the beta-divergence (beta-NMF). The beta-divergence is a family of cost functions parametrized by a single shape parameter beta that takes the Euclidean distance, the Kullback-Leibler divergence and the Itakura-Saito divergence as... |
Title: A probabilistic top-down parser for minimalist grammars |
Abstract: This paper describes a probabilistic top-down parser for minimalist grammars. Top-down parsers have the great advantage of having a certain predictive power during the parsing, which takes place in a left-to-right reading of the sentence. Such parsers have already been well-implemented and studied in the case... |
Title: Infinite Hierarchical MMSB Model for Nested Communities/Groups in Social Networks |
Abstract: Actors in realistic social networks play not one but a number of diverse roles depending on whom they interact with, and a large number of such role-specific interactions collectively determine social communities and their organizations. Methods for analyzing social networks should capture these multi-faceted... |
Title: Multi-Objective Genetic Programming Projection Pursuit for Exploratory Data Modeling |
Abstract: For classification problems, feature extraction is a crucial process which aims to find a suitable data representation that increases the performance of the machine learning algorithm. According to the curse of dimensionality theorem, the number of samples needed for a classification task increases exponentia... |
Title: Testing for Parallelism Between Trends in Multiple Time Series |
Abstract: This paper considers the inference of trends in multiple, nonstationary time series. To test whether trends are parallel to each other, we use a parallelism index based on the L2-distances between nonparametric trend estimators and their average. A central limit theorem is obtained for the test statistic and ... |
Title: Hierarchical Multiclass Decompositions with Application to Authorship Determination |
Abstract: This paper is mainly concerned with the question of how to decompose multiclass classification problems into binary subproblems. We extend known Jensen-Shannon bounds on the Bayes risk of binary problems to hierarchical multiclass problems and use these bounds to develop a heuristic procedure for constructing... |
Title: Regions of Attraction for Hybrid Limit Cycles of Walking Robots |
Abstract: This paper illustrates the application of recent research in region-of-attraction analysis for nonlinear hybrid limit cycles. Three example systems are analyzed in detail: the van der Pol oscillator, the "rimless wheel", and the "compass gait", the latter two being simplified models of underactuated walking r... |
Title: The Lambert Way to Gaussianize heavy tailed data with the inverse of Tukey's h as a special case |
Abstract: I present a parametric, bijective transformation to generate heavy tail versions Y of arbitrary RVs X F. The tail behavior of the so-called 'heavy tail Lambert W x F' RV Y depends on a tail parameter delta >= 0: for delta = 0, Y = X, for delta > 0 Y has heavier tails than X. For X being Gaussian, this meta-... |
Title: Stochastic model selection for Mixtures of Matrix-Normals |
Abstract: Finite mixtures of matrix normal distributions are a powerful tool for classifying three-way data in unsupervised problems. The distribution of each component is assumed to be a matrix variate normal density. The mixture model can be estimated through the EM algorithm under the assumption that the number of c... |
Title: A factor mixture analysis model for multivariate binary data |
Abstract: The paper proposes a latent variable model for binary data coming from an unobserved heterogeneous population. The heterogeneity is taken into account by replacing the traditional assumption of Gaussian distributed factors by a finite mixture of multivariate Gaussians. The aim of the proposed model is twofold... |
Title: Learning Taxonomy for Text Segmentation by Formal Concept Analysis |
Abstract: In this paper the problems of deriving a taxonomy from a text and concept-oriented text segmentation are approached. Formal Concept Analysis (FCA) method is applied to solve both of these linguistic problems. The proposed segmentation method offers a conceptual view for text segmentation, using a context-driv... |
Title: Conservation Law of Utility and Equilibria in Non-Zero Sum Games |
Abstract: This short note demonstrates how one can define a transformation of a non-zero sum game into a zero sum, so that the optimal mixed strategy achieving equilibrium always exists. The transformation is equivalent to introduction of a passive player into a game (a player with a singleton set of pure strategies), ... |
Title: Optimal designs for Lasso and Dantzig selector using Expander Codes |
Abstract: We investigate the high-dimensional regression problem using adjacency matrices of unbalanced expander graphs. In this frame, we prove that the $\ell_2$-prediction error and the $\ell_1$-risk of the lasso and the Dantzig selector are optimal up to an explicit multiplicative constant. Thus we can estimate a hi... |
Title: A Unified Framework for High-Dimensional Analysis of M-Estimators with Decomposable Regularizers |
Abstract: High-dimensional statistical inference deals with models in which the the number of parameters p is comparable to or larger than the sample size n. Since it is usually impossible to obtain consistent procedures unless $p/n\rightarrow0$, a line of recent work has studied models with various types of low-dimens... |
Title: Combinatorial Continuous Maximal Flows |
Abstract: Maximum flow (and minimum cut) algorithms have had a strong impact on computer vision. In particular, graph cuts algorithms provide a mechanism for the discrete optimization of an energy functional which has been used in a variety of applications such as image segmentation, stereo, image stitching and texture... |
Title: Identification and well-posedness in a class of nonparametric problems |
Abstract: This is a companion note to Zinde-Walsh (2010), arXiv:1009.4217v1[MATH.ST], to clarify and extend results on identification in a number of problems that lead to a system of convolution equations. Examples include identification of the distribution of mismeasured variables, of a nonparametric regression functi... |
Title: Online Multiple Kernel Learning for Structured Prediction |
Abstract: Despite the recent progress towards efficient multiple kernel learning (MKL), the structured output case remains an open research front. Current approaches involve repeatedly solving a batch learning problem, which makes them inadequate for large scale scenarios. We propose a new family of online proximal alg... |
Title: A Bregman Extension of quasi-Newton updates II: Convergence and Robustness Properties |
Abstract: We propose an extension of quasi-Newton methods, and investigate the convergence and the robustness properties of the proposed update formulae for the approximate Hessian matrix. Fletcher has studied a variational problem which derives the approximate Hessian update formula of the quasi-Newton methods. We poi... |
Title: A Bregman Extension of quasi-Newton updates I: An Information Geometrical framework |
Abstract: We study quasi-Newton methods from the viewpoint of information geometry induced associated with Bregman divergences. Fletcher has studied a variational problem which derives the approximate Hessian update formula of the quasi-Newton methods. We point out that the variational problem is identical to optimizat... |
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