1,336
1.3K

Oct 26, 2006
10/06

by
Joseph Lipka

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A theoretical treatment of what can be computed and how fast it can be done. Applications to compilers, string searching, and control circuit design will be discussed. The hierarchy of finite state machines, pushdown machines, context free grammars and Turing machines will be analyzed, along with their variations. The notions of decidability, complexity theory and a complete discussion of NP-Complete problems round out the course. Text: Introduction to the Theory of Computation, Michael Sipser....

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Topic: computation

9
9.0

Jun 27, 2018
06/18

by
Nicolas Chopin; James Ridgway; Mathieu Gerber; Omiros Papaspiliopoulos

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SMC$^2$ is an efficient algorithm for sequential estimation and state inference of state-space models. It generates $N_{\theta}$ parameter particles $\theta^{m}$, and, for each $\theta^{m}$, it runs a particle filter of size $N_{x}$ (i.e. at each time step, $N_{x}$ particles are generated in the state space $\mathcal{X}$). We discuss how to automatically calibrate $N_{x}$ in the course of the algorithm. Our approach relies on conditional Sequential Monte Carlo updates, monitoring the state of...

Topics: Statistics, Computation

Source: http://arxiv.org/abs/1506.00570

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Jun 28, 2018
06/18

by
Masaaki Imaizumi

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To solve discrete Markov decision models with a large number of dimensions is always difficult (and at times, impossible), because size of state space and computation cost increases exponentially with the number of dimensions. This phenomenon is called "The Curse of Dimensionality," and it prevents us from using models with many state variables. To overcome this problem, we propose a new approximation method, named statistical least square temporal difference (SLSTD) method, that can...

Topics: Statistics, Computation

Source: http://arxiv.org/abs/1506.06722

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7.0

Jun 28, 2018
06/18

by
Juha Ala-Luhtala; Nick Whiteley; Kari Heine; Robert Piche

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Twisted particle filters are a class of sequential Monte Carlo methods recently introduced by Whiteley and Lee to improve the efficiency of marginal likelihood estimation in state-space models. The purpose of this article is to extend the twisted particle filtering methodology, establish accessible theoretical results which convey its rationale, and provide a demonstration of its practical performance within particle Markov chain Monte Carlo for estimating static model parameters. We derive...

Topics: Statistics, Computation

Source: http://arxiv.org/abs/1509.09175

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5.0

Jun 29, 2018
06/18

by
Daniel Fischer; Karl Mosler; Jyrki Möttönen; Klaus Nordhausen; Oleksii Pokotylo; Daniel Vogel

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The Oja median is one of several extensions of the univariate median to the multivariate case. It has many nice properties, but is computationally demanding. In this paper, we first review the properties of the Oja median and compare it to other multivariate medians. Afterwards we discuss four algorithms to compute the Oja median, which are implemented in our R-package OjaNP. Besides these algorithms, the package contains also functions to compute Oja signs, Oja signed ranks, Oja ranks, and the...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1606.07620

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6.0

Jun 29, 2018
06/18

by
Daniel Sanz-Alonso

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Importance sampling approximates expectations with respect to a target measure by using samples from a proposal measure. The performance of the method over large classes of test functions depends heavily on the closeness between both measures. We derive a general bound that needs to hold for importance sampling to be successful, and relates the $f$-divergence between the target and the proposal to the sample size. The bound is deduced from a new and simple information theory paradigm for the...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1608.08814

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7.0

Jun 30, 2018
06/18

by
Alberto Caimo; Nial Friel

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The Bergm package provides a comprehensive framework for Bayesian inference using Markov chain Monte Carlo (MCMC) algorithms. It can also supply graphical Bayesian goodness-of-fit procedures that address the issue of model adequacy. The package is simple to use and represents an attractive way of analysing network data as it offers the advantage of a complete probabilistic treatment of uncertainty. Bergm is based on the ergm package and therefore it makes use of the same model set-up and...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1703.05144

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4.0

Jun 30, 2018
06/18

by
Alexander Y. Shestopaloff; Radford M. Neal

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We introduce an efficient MCMC sampling scheme to perform Bayesian inference in the M/G/1 queueing model given only observations of interdeparture times. Our MCMC scheme uses a combination of Gibbs sampling and simple Metropolis updates together with three novel "shift" and "scale" updates. We show that our novel updates improve the speed of sampling considerably, by factors of about 60 to about 180 on a variety of simulated data sets.

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1401.5548

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8.0

Jun 30, 2018
06/18

by
Bruno Sudret; Chu Van Mai

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In the field of computer experiments sensitivity analysis aims at quantifying the relative importance of each input parameter (or combinations thereof) of a computational model with respect to the model output uncertainty. Variance decomposition methods leading to the well-known Sobol' indices are recognized as accurate techniques, at a rather high computational cost though. The use of polynomial chaos expansions (PCE) to compute Sobol' indices has allowed to alleviate the computational burden...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1405.5740

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5.0

Jun 30, 2018
06/18

by
Branko Ristic; Ajith Gunatilaka; Ralph Gailis; Alex Skvortsov

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Localisation of a source of a toxic release of biochemical aerosols in the atmosphere is a problem of great importance for public safety. Two main practical difficulties are encountered in this problem: the lack of knowledge of the likelihood function of measurements collected by biochemical sensors, and the plethora of candidate dispersion models, developed under various assumptions (e.g. meteorological conditions, terrain). Aiming to overcome these two difficulties, the paper proposes a...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1405.6460

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5.0

Jun 30, 2018
06/18

by
Mathieu Gerber; Nicolas Chopin

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We derive and study SQMC (Sequential Quasi-Monte Carlo), a class of algorithms obtained by introducing QMC point sets in particle filtering. SQMC is related to, and may be seen as an extension of, the array-RQMC algorithm of L'Ecuyer et al. (2006). The complexity of SQMC is $O(N \log N)$, where $N$ is the number of simulations at each iteration, and its error rate is smaller than the Monte Carlo rate $O_P(N^{-1/2})$. The only requirement to implement SQMC is the ability to write the simulation...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1402.4039

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Jun 30, 2018
06/18

by
Alex Beskos; Dan Crisan; Ajay Jasra; Kengo Kamatani; Yan Zhou

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We consider the numerical approximation of the filtering problem in high dimensions, that is, when the hidden state lies in $\mathbb{R}^d$ with $d$ large. For low dimensional problems, one of the most popular numerical procedures for consistent inference is the class of approximations termed particle filters or sequential Monte Carlo methods. However, in high dimensions, standard particle filters (e.g. the bootstrap particle filter) can have a cost that is exponential in $d$ for the algorithm...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1412.3501

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3.0

Jun 29, 2018
06/18

by
Cristina Mollica; Luca Tardella

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Ranking data represent a peculiar form of multivariate ordinal data taking values in the set of permutations. Despite the numerous methodological contributions to increase the flexibility of ranked data modeling, the application of more sophisticated models is limited by the related computational issues. The PLMIX package offers a comprehensive framework aimed at endowing the R statistical environment with some recent methodological advances on model-based analysis of partially ranked data. The...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1612.08141

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3.0

Jun 30, 2018
06/18

by
Tim Salimans

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We recently proposed a general algorithm for approximating nonstandard Bayesian posterior distributions by minimization of their Kullback-Leibler divergence with respect to a more convenient approximating distribution. In this note we offer details on how to efficiently implement this algorithm in practice. We also suggest default choices for the form of the posterior approximation, the number of iterations, the step size, and other user choices. By using these defaults it becomes possible to...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1401.2135

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3.0

Jun 30, 2018
06/18

by
Clara Grazian; Brunero Liseo

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We propose a novel use of a recent new computational tool for Bayesian inference, namely the Approximate Bayesian Computation (ABC) methodology. ABC is a way to handle models for which the likelihood function may be intractable or even unavailable and/or too costly to evaluate; in particular, we consider the problem of eliminating the nuisance parameters from a complex statistical model in order to produce a likelihood function depending on the quantity of interest only. Given a proper prior...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1403.0387

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3.0

Jun 28, 2018
06/18

by
Pieralberto Guarniero; Adam M. Johansen; Anthony Lee

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We present an offline, iterated particle filter to facilitate statistical inference in general state space hidden Markov models. Given a model and a sequence of observations, the associated marginal likelihood L is central to likelihood-based inference for unknown statistical parameters. We define a class of "twisted" models: each member is specified by a sequence of positive functions psi and has an associated psi-auxiliary particle filter that provides unbiased estimates of L. We...

Topics: Statistics, Computation

Source: http://arxiv.org/abs/1511.06286

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3.0

Jun 29, 2018
06/18

by
Daniel Turek; Perry de Valpine; Christopher J. Paciorek

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Traditional Markov chain Monte Carlo (MCMC) sampling of hidden Markov models (HMMs) involves latent states underlying an imperfect observation process, and generates posterior samples for top-level parameters concurrently with nuisance latent variables. When potentially many HMMs are embedded within a hierarchical model, this can result in prohibitively long MCMC runtimes. We study combinations of existing methods, which are shown to vastly improve computational efficiency for these...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1601.02698

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8.0

Jun 29, 2018
06/18

by
Robert L. Grant; Daniel C. Furr; Bob Carpenter; Andrew Gelman

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Stata users have access to two easy-to-use implementations of Bayesian inference: Stata's native {\tt bayesmh} function and StataStan, which calls the general Bayesian engine Stan. We compare these on two models that are important for education research: the Rasch model and the hierarchical Rasch model. Stan (as called from Stata) fits a more general range of models than can be fit by {\tt bayesmh} and is also more scalable, in that it could easily fit models with at least ten times more...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1601.03443

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3.0

Jun 29, 2018
06/18

by
L. Martino; V. Elvira; F. Louzada

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The Effective Sample Size (ESS) is an important measure of efficiency of Monte Carlo methods such as Markov Chain Monte Carlo (MCMC) and Importance Sampling (IS) techniques. In the IS context, an approximation $\widehat{ESS}$ of the theoretical ESS definition is widely applied, involving the inverse of the sum of the squares of the normalized importance weights. This formula, $\widehat{ESS}$, has become an essential piece within Sequential Monte Carlo (SMC) methods, to assess the convenience of...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1602.03572

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Jun 27, 2018
06/18

by
Perry de Valpine; Daniel Turek; Christopher J. Paciorek; Clifford Anderson-Bergman; Duncan Temple Lang; Rastislav Bodik

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We describe NIMBLE, a system for programming statistical algorithms for general model structures within R. NIMBLE is designed to meet three challenges: flexible model specification, a language for programming algorithms that can use different models, and a balance between high-level programmability and execution efficiency. For model specification, NIMBLE extends the BUGS language and creates model objects, which can manipulate variables, calculate log probability values, generate simulations,...

Topics: Statistics, Computation

Source: http://arxiv.org/abs/1505.05093

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Jun 27, 2018
06/18

by
Felipe J. Medina-Aguayo; Anthony Lee; Gareth O. Roberts

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Pseudo-marginal Markov chain Monte Carlo methods for sampling from intractable distributions have gained recent interest and have been theoretically studied in considerable depth. Their main appeal is that they are exact, in the sense that they target marginally the correct invariant distribution. However, the pseudo-marginal Markov chain can exhibit poor mixing and slow convergence towards its target. As an alternative, a subtly different Markov chain can be simulated, where better mixing is...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1503.07066

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8.0

Jun 27, 2018
06/18

by
Stefano M. Iacus; Lorenzo Mercuri; Edit Rroji

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In this paper we show how to simulate and estimate a COGARCH(p,q) model in the R package yuima. Several routines for simulation and estimation are available. Indeed for the generation of a COGARCH(p,q) trajectory, the user can choose between two alternative schemes. The first is based on the Euler discretization of the stochastic differential equations that identifies a COGARCH(p,q) model while the second one considers the explicit solution of the variance process. Estimation is based on the...

Topics: Statistics, Computation

Source: http://arxiv.org/abs/1505.03914

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4.0

Jun 29, 2018
06/18

by
Axel Finke; Arnaud Doucet; Adam M. Johansen

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The embedded hidden Markov model (EHMM) sampling method is a Markov chain Monte Carlo (MCMC) technique for state inference in non-linear non-Gaussian state-space models which was proposed in Neal (2003); Neal et al. (2004) and extended in Shestopaloff and Neal (2016). An extension to Bayesian parameter inference was presented in Shestopaloff and Neal (2013). An alternative class of MCMC schemes addressing similar inference problems is provided by particle MCMC (PMCMC) methods (Andrieu et al....

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1610.08962

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3.0

Jun 30, 2018
06/18

by
Ming Teng; Farouk S. Nathoo; Timothy D. Johnson

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The Log-Gaussian Cox Process is a commonly used model for the analysis of spatial point patterns. Fitting this model is difficult because of its doubly-stochastic property, i.e., it is an hierarchical combination of a Poisson process at the first level and a Gaussian Process at the second level. Different methods have been proposed to estimate such a process, including traditional likelihood-based approaches as well as Bayesian methods. We focus here on Bayesian methods and several approaches...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1701.00857

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5.0

Jun 30, 2018
06/18

by
Alexander Gribov; Konstantin Krivoruchko

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We discuss how the kernel convolution approach can be used to accurately approximate the spatial covariance model on a sphere using spherical distances between points. A detailed derivation of the required formulas is provided. The proposed covariance model approximation can be used for non-stationary spatial prediction and simulation in the case when the dataset is large and the covariance model can be estimated separately in the data subsets.

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1701.03405

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4.0

Jun 30, 2018
06/18

by
Ba Ngu Vo; Ba Tuong Vo

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This paper proposes an efficient implementation of the multi-sensor generalized labeled multi-Bernoulli (GLMB) filter. The solution exploits the GLMB joint prediction and update together with a new technique for truncating the GLMB filtering density based on Gibbs sampling. The resulting algorithm has quadratic complexity in the number of hypothesized object and linear in the number of measurements of each individual sensors.

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1702.08849

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3.0

Jun 30, 2018
06/18

by
Rahman Farnoosh; Amirhossein Sobhani; Hamidreza Rezazadeh

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In this article, we present an orthogonal basis expansion method for solving stochastic differential equations with a path-independent solution of the form $X_{t}=\phi(t,W_{t})$. For this purpose, we define a Hilbert space and construct an orthogonal basis for this inner product space with the aid of 2D-Hermite polynomials. With considering $X_{t}$ as orthogonal basis expansion, this method is implemented and the expansion coefficients are obtained by solving a system of nonlinear...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1703.09658

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3.0

Jun 30, 2018
06/18

by
Jose A. Fioruci; Ricardo S. Ehlers; Francisco Louzada

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Multivariate GARCH models are important tools to describe the dynamics of multivariate times series of financial returns. Nevertheless, these models have been much less used in practice due to the lack of reliable software. This paper describes the {\tt R} package {\bf BayesDccGarch} which was developed to implement recently proposed inference procedures to estimate and compare multivariate GARCH models allowing for asymmetric and heavy tailed distributions.

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1412.2967

3
3.0

Jun 30, 2018
06/18

by
Jim Griffin; Krzysztof Latuszynski; Mark Steel

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The increasing size of data sets has lead to variable selection in regression becoming increasingly important. Bayesian approaches are attractive since they allow uncertainty about the choice of variables to be formally included in the analysis. The application of fully Bayesian variable selection methods to large data sets is computationally challenging. We describe an adaptive Markov chain Monte Carlo approach called Individual Adaptation which adjusts a general proposal to the data. We show...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1412.6760

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9.0

Jun 30, 2018
06/18

by
Peter W. Glynn; Chang-han Rhee

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We introduce a new class of Monte Carlo methods, which we call exact estimation algorithms. Such algorithms provide unbiased estimators for equilibrium expectations associated with real- valued functionals defined on a Markov chain. We provide easily implemented algorithms for the class of positive Harris recurrent Markov chains, and for chains that are contracting on average. We further argue that exact estimation in the Markov chain setting provides a significant theoretical relaxation...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1409.4302

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5.0

Jun 29, 2018
06/18

by
Enes Makalic; Daniel F. Schmidt

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Bayesian penalized regression techniques, such as the Bayesian lasso and the Bayesian horseshoe estimator, have recently received a significant amount of attention in the statistics literature. However, software implementing state-of-the-art Bayesian penalized regression, outside of general purpose Markov chain Monte Carlo platforms such as STAN, is relatively rare. This paper introduces bayesreg, a new toolbox for fitting Bayesian penalized regression models with continuous shrinkage prior...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1611.06649

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4.0

Jun 29, 2018
06/18

by
Matthew Friedlander

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Log-linear modeling is a popular method for the analysis of contingency table data. When the table is sparse, and the data falls on a proper face $F$ of the convex support, there are consequences on model inference and model selection. Knowledge of the cells determining $F$ is crucial to mitigating these effects. We introduce the R package (R Core Team (2016)) eMLEloglin for determining $F$ and passing that information on to the glm package to fit the model properly.

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1611.07505

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4.0

Jun 29, 2018
06/18

by
Kaylea Haynes; Paul Fearnhead; Idris A. Eckley

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In this paper we build on an approach proposed by Zou et al. (2014) for nonpara- metric changepoint detection. This approach defines the best segmentation for a data set as the one which minimises a penalised cost function, with the cost function defined in term of minus a non-parametric log-likelihood for data within each segment. Min- imising this cost function is possible using dynamic programming, but their algorithm had a computational cost that is cubic in the length of the data set. To...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1602.01254

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9.0

Jun 30, 2018
06/18

by
Marie Chavent; Vanessa Kuentz-Simonet; Amaury Labenne; Jérôme Saracco

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Mixed data type arise when observations are described by a mixture of numerical and categorical variables. The R package PCAmixdata extends standard multivariate analysis methods to incorporate this type of data. The key techniques included in the package are PCAmix (PCA of a mixture of numerical and categorical variables), PCArot (rotation in PCAmix) and MFAmix (multiple factor analysis with mixed type data within a dataset). This paper gives a synthetic presentation of the three algorithms...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1411.4911

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3.0

Jun 29, 2018
06/18

by
Antonio Punzo; Angelo Mazza; Paul D. McNicholas

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We introduce the R package ContaminatedMixt, conceived to disseminate the use of mixtures of multivariate contaminated normal distributions as a tool for robust clustering and classification under the common assumption of elliptically contoured groups. Thirteen variants of the model are also implemented to introduce parsimony. The expectation-conditional maximization algorithm is adopted to obtain maximum likelihood parameter estimates, and likelihood-based model selection criteria are used to...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1606.03766

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4.0

Jun 29, 2018
06/18

by
Alan Benson; Nial Friel

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We consider the problem of Bayesian inference for changepoints where the number and position of the changepoints are both unknown. In particular, we consider product partition models where it is possible to integrate out model parameters for the regime between each changepoint, leaving a posterior distribution over a latent vector indicating the presence or not of a changepoint at each observation. The same problem setting has been considered by Fearnhead (2006) where one can use filtering...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1606.09419

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Jun 27, 2018
06/18

by
Nicolas Chopin; Mathieu Gerber

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Sequential Monte Carlo algorithms (also known as particle filters) are popular methods to approximate filtering (and related) distributions of state-space models. However, they converge at the slow $1/\sqrt{N}$ rate, which may be an issue in real-time data-intensive scenarios. We give a brief outline of SQMC (Sequential Quasi-Monte Carlo), a variant of SMC based on low-discrepancy point sets proposed by Gerber and Chopin (2015), which converges at a faster rate, and we illustrate the greater...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1503.01631

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5.0

Jun 30, 2018
06/18

by
Waldemar W. Koczkodaj; Alicja Wolny-Dominiak

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This study presents an innovative method for reducing the number of rating scale items without predictability loss. The "area under the re- ceiver operator curve method" (AUC ROC) is used to implement in the RatingScaleReduction package posted on CRAN. Several cases have been used to illustrate how the stepwise method has reduced the number of rating scale items (variables).

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1703.06826

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Jun 28, 2018
06/18

by
Yolanda Hagar; Vanja Dukic

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We give an overview of eight different software packages and functions available in R for semi- or non-parametric estimation of the hazard rate for right-censored survival data. Of particular interest is the accuracy of the estimation of the hazard rate in the presence of covariates, as well as the user-friendliness of the packages. In addition, we investigate the ability to incorporate covariates under both the proportional and the non-proportional hazards assumptions. We contrast the...

Topics: Statistics, Computation

Source: http://arxiv.org/abs/1509.03253

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Jun 27, 2018
06/18

by
Luca Weihs; Mathias Drton; Dennis Leung

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In an extension of Kendall's $\tau$, Bergsma and Dassios (2014) introduced a covariance measure $\tau^*$ for two ordinal random variables that vanishes if and only if the two variables are independent. For a sample of size $n$, a direct computation of $t^*$, the empirical version of $\tau^*$, requires $O(n^4)$ operations. We derive an algorithm that computes the statistic using only $O(n^2\log(n))$ operations.

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1504.00964

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7.0

Jun 30, 2018
06/18

by
Jason Wyse; Nial Friel; Pierre Latouche

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We consider the task of simultaneous clustering of the two node sets involved in a bipartite network. The approach we adopt is based on use of the exact integrated complete likelihood for the latent block model. Using this allows one to infer the number of clusters as well as cluster memberships using a greedy search. This gives a model-based clustering of the node sets. Experiments on simulated bipartite network data show that the greedy search approach is vastly more scalable than competing...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1404.2911

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Jun 26, 2018
06/18

by
J. Derrac; S. García; F. Herrera

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Nonparametric statistical tests are useful procedures that can be applied in a wide range of situations, such as testing randomness or goodness of fit, one-sample, two-sample and multiple-sample analysis, association between bivariate samples or count data analysis. Their use is often preferred to parametric tests due to the fact that they require less restrictive assumptions about the population sampled. In this work, JavaNPST, an open source Java library implementing 40 nonparametric...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1501.04222

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Jun 26, 2018
06/18

by
Guillaume Damblin; Pierre Barbillon; Merlin Keller; Alberto Pasanisi; Eric Parent

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Making good predictions of a physical system using a computer code requires the inputs to be carefully specified. Some of these inputs called control variables have to reproduce physical conditions whereas other inputs, called parameters, are specific to the computer code and most often uncertain. The goal of statistical calibration consists in estimating these parameters with the help of a statistical model which links the code outputs with the field measurements. In a Bayesian setting, the...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1502.07252

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Jun 27, 2018
06/18

by
David Bolin; Jonas Wallin

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Covariance tapering is a popular approach for reducing the computational cost of spatial prediction and parameter estimation for Gaussian process models. However, tapering can have poor performance when the process is sampled at spatially irregular locations or when non-stationary covariance models are used. This work introduces an adaptive tapering method in order to improve the performance of tapering in these problematic cases. This is achieved by introducing a computationally convenient...

Topics: Statistics, Computation

Source: http://arxiv.org/abs/1506.03670

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Jun 30, 2018
06/18

by
Ajay Jasra

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In the following article we consider approximate Bayesian computation (ABC) for certain classes of time series models. In particular, we focus upon scenarios where the likelihoods of the observations and parameter are intractable, by which we mean that one cannot evaluate the likelihood even up-to a positive unbiased estimate. This paper reviews and develops a class of approximation procedures based upon the idea of ABC, but, specifically maintains the probabilistic structure of the original...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1401.0265

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4.0

Jun 30, 2018
06/18

by
Marcelo Hartmann; Ricardo Ehlers

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In this paper we propose to evaluate and compare Markov chain Monte Carlo (MCMC) methods to estimate the parameters in a generalized extreme value model. We employed the Bayesian approach using traditional Metropolis-Hastings methods, Hamiltonian Monte Carlo (HMC) and Riemann manifold HMC (RMHMC) methods to obtain the approximations to the posterior marginal distributions of interest. Applications to real datasets of maxima illustrate illustrate how HMC can be much more efficient...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1410.4534

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Jun 26, 2018
06/18

by
Francesco Bartolucci; Alessio Farcomeni; Silvia Pandolfi; Fulvia Pennoni

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Latent Markov (LM) models represent an important class of models for the analysis of longitudinal data (Bartolucci et. al., 2013), especially when response variables are categorical. These models have a great potential of application for the analysis of social, medical, and behavioral data as well as in other disciplines. We propose the R package LMest, which is tailored to deal with these types of model. In particular, we consider a general framework for extended LM models by including...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1501.04448

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Jun 28, 2018
06/18

by
Mathieu Cambou; Marius Hofert; Christiane Lemieux

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The present work addresses the question how sampling algorithms for commonly applied copula models can be adapted to account for quasi-random numbers. Besides sampling methods such as the conditional distribution method (based on a one-to-one transformation), it is also shown that typically faster sampling methods (based on stochastic representations) can be used to improve upon classical Monte Carlo methods when pseudo-random number generators are replaced by quasi-random number generators....

Topics: Statistics, Computation

Source: http://arxiv.org/abs/1508.03483

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8.0

Jun 29, 2018
06/18

by
Jean-Benoist Leger

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Analysis of the topology of a graph, regular or bipartite one, can be done by clustering for regular ones or co-clustering for bipartite ones. The Stochastic Block Model and the Latent Block Model are two models, which are very similar for respectively regular and bipartite graphs, based on probabilistic models. Initially developed for binary graphs, these models have been extended to valued networks with optional covariates on the edges. This paper present a implementation of a Variational EM...

Topics: Computation, Statistics

Source: http://arxiv.org/abs/1602.07587