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  • BayesProject: Fast computation of a projection direction for multivariate changepoint detection
    Stat. Comput. (IF 3.035) Pub Date : 2020-08-01
    Georg Hahn, Paul Fearnhead, Idris A. Eckley

    This article focuses on the challenging problem of efficiently detecting changes in mean within multivariate data sequences. Multivariate changepoints can be detected by projecting a multivariate series to a univariate one using a suitable projection direction that preserves a maximal proportion of signal information. However, for some existing approaches the computation of such a projection direction

  • Parallel sequential Monte Carlo for stochastic gradient-free nonconvex optimization
    Stat. Comput. (IF 3.035) Pub Date : 2020-07-29
    Ömer Deniz Akyildiz, Dan Crisan, Joaquín Míguez

    We introduce and analyze a parallel sequential Monte Carlo methodology for the numerical solution of optimization problems that involve the minimization of a cost function that consists of the sum of many individual components. The proposed scheme is a stochastic zeroth-order optimization algorithm which demands only the capability to evaluate small subsets of components of the cost function. It can

  • Sampling hierarchies of discrete random structures
    Stat. Comput. (IF 3.035) Pub Date : 2020-07-17
    Antonio Lijoi, Igor Prünster, Tommaso Rigon

    Hierarchical normalized discrete random measures identify a general class of priors that is suited to flexibly learn how the distribution of a response variable changes across groups of observations. A special case widely used in practice is the hierarchical Dirichlet process. Although current theory on hierarchies of nonparametric priors yields all relevant tools for drawing posterior inference, their

  • Imputation and low-rank estimation with Missing Not At Random data
    Stat. Comput. (IF 3.035) Pub Date : 2020-07-16
    Aude Sportisse, Claire Boyer, Julie Josse

    Missing values challenge data analysis because many supervised and unsupervised learning methods cannot be applied directly to incomplete data. Matrix completion based on low-rank assumptions are very powerful solution for dealing with missing values. However, existing methods do not consider the case of informative missing values which are widely encountered in practice. This paper proposes matrix

  • Multi-scale process modelling and distributed computation for spatial data
    Stat. Comput. (IF 3.035) Pub Date : 2020-07-16
    Andrew Zammit-Mangion, Jonathan Rougier

    Recent years have seen a huge development in spatial modelling and prediction methodology, driven by the increased availability of remote-sensing data and the reduced cost of distributed-processing technology. It is well known that modelling and prediction using infinite-dimensional process models is not possible with large data sets, and that both approximate models and, often, approximate-inference

  • Inference for cluster point processes with over- or under-dispersed cluster sizes
    Stat. Comput. (IF 3.035) Pub Date : 2020-07-14
    Claes Andersson, Tomáš Mrkvička

    Cluster point processes comprise a class of models that have been used for a wide range of applications. While several models have been studied for the probability density function of the offspring displacements and the parent point process, there are few examples of non-Poisson distributed cluster sizes. In this paper, we introduce a generalization of the Thomas process, which allows for the cluster

  • The turning arcs: a computationally efficient algorithm to simulate isotropic vector-valued Gaussian random fields on the d -sphere
    Stat. Comput. (IF 3.035) Pub Date : 2020-06-04
    Alfredo Alegría, Xavier Emery, Christian Lantuéjoul

    Random fields on the sphere play a fundamental role in the natural sciences. This paper presents a simulation algorithm parenthetical to the spectral turning bands method used in Euclidean spaces, for simulating scalar- or vector-valued Gaussian random fields on the d-dimensional unit sphere. The simulated random field is obtained by a sum of Gegenbauer waves, each of which is variable along a randomly

  • An information theoretic approach to post randomization methods under differential privacy
    Stat. Comput. (IF 3.035) Pub Date : 2020-06-01
    Fadhel Ayed, Marco Battiston, Federico Camerlenghi

    Post randomization methods are among the most popular disclosure limitation techniques for both categorical and continuous data. In the categorical case, given a stochastic matrix M and a specified variable, an individual belonging to category i is changed to category j with probability \(M_{i,j}\). Every approach to choose the randomization matrix M has to balance between two desiderata: (1) preserving

  • Model-based clustering with determinant-and-shape constraint
    Stat. Comput. (IF 3.035) Pub Date : 2020-05-29
    Luis Angel García-Escudero, Agustín Mayo-Iscar, Marco Riani

    Model-based approaches to cluster analysis and mixture modeling often involve maximizing classification and mixture likelihoods. Without appropriate constrains on the scatter matrices of the components, these maximizations result in ill-posed problems. Moreover, without constrains, non-interesting or “spurious” clusters are often detected by the EM and CEM algorithms traditionally used for the maximization

  • Bayesian estimation of the latent dimension and communities in stochastic blockmodels
    Stat. Comput. (IF 3.035) Pub Date : 2020-05-27
    Francesco Sanna Passino, Nicholas A. Heard

    Spectral embedding of adjacency or Laplacian matrices of undirected graphs is a common technique for representing a network in a lower dimensional latent space, with optimal theoretical guarantees. The embedding can be used to estimate the community structure of the network, with strong consistency results in the stochastic blockmodel framework. One of the main practical limitations of standard algorithms

  • Characterization of topic-based online communities by combining network data and user generated content
    Stat. Comput. (IF 3.035) Pub Date : 2020-05-22
    Mirai Igarashi, Nobuhiko Terui

    This study proposes a model for characterizing online communities by combining two types of data: network data and user-generated-content (UGC). The existing models for detecting the community structure of a network employ only network information. However, not all people connected in a network share the same interests. For instance, even if students belong to the same community of “school,” they may

  • The conditional censored graphical lasso estimator
    Stat. Comput. (IF 3.035) Pub Date : 2020-05-15
    Luigi Augugliaro, Gianluca Sottile, Veronica Vinciotti

    In many applied fields, such as genomics, different types of data are collected on the same system, and it is not uncommon that some of these datasets are subject to censoring as a result of the measurement technologies used, such as data generated by polymerase chain reactions and flow cytometer. When the overall objective is that of network inference, at possibly different levels of a system, information

  • Conditionally structured variational Gaussian approximation with importance weights
    Stat. Comput. (IF 3.035) Pub Date : 2020-04-28
    Linda S. L. Tan, Aishwarya Bhaskaran, David J. Nott

    We develop flexible methods of deriving variational inference for models with complex latent variable structure. By splitting the variables in these models into “global” parameters and “local” latent variables, we define a class of variational approximations that exploit this partitioning and go beyond Gaussian variational approximation. This approximation is motivated by the fact that in many hierarchical

  • Classification of periodic arrivals in event time data for filtering computer network traffic
    Stat. Comput. (IF 3.035) Pub Date : 2020-04-24
    Francesco Sanna Passino, Nicholas A. Heard

    Periodic patterns can often be observed in real-world event time data, possibly mixed with non-periodic arrival times. For modelling purposes, it is necessary to correctly distinguish the two types of events. This task has particularly important implications in computer network security; there, separating automated polling traffic and human-generated activity in a computer network is important for

  • Inhomogeneous higher-order summary statistics for point processes on linear networks
    Stat. Comput. (IF 3.035) Pub Date : 2020-04-24
    Ottmar Cronie, Mehdi Moradi, Jorge Mateu

    As a workaround for the lack of transitive transformations on linear network structures, which are required to consider different notions of distributional invariance, including stationarity, we introduce the notions of pseudostationarity and intensity reweighted moment pseudostationarity for point processes on linear networks. Moreover, using arbitrary so-called regular linear network distances, e

  • Optimal allocation of Monte Carlo simulations to multiple hypothesis tests
    Stat. Comput. (IF 3.035) Pub Date : 2019-10-05
    Georg Hahn

    Multiple hypothesis tests are often carried out in practice using p-value estimates obtained with bootstrap or permutation tests since the analytical p-values underlying all hypotheses are usually unknown. This article considers the allocation of a pre-specified total number of Monte Carlo simulations \(K \in \mathbb {N}\) (i.e., permutations or draws from a bootstrap distribution) to a given number

  • Robust Bayesian synthetic likelihood via a semi-parametric approach
    Stat. Comput. (IF 3.035) Pub Date : 2019-10-04
    Ziwen An, David J. Nott, Christopher Drovandi

    Bayesian synthetic likelihood (BSL) is now a well-established method for performing approximate Bayesian parameter estimation for simulation-based models that do not possess a tractable likelihood function. BSL approximates an intractable likelihood function of a carefully chosen summary statistic at a parameter value with a multivariate normal distribution. The mean and covariance matrix of this normal

  • Spectral density-based and measure-preserving ABC for partially observed diffusion processes. An illustration on Hamiltonian SDEs
    Stat. Comput. (IF 3.035) Pub Date : 2019-11-05
    Evelyn Buckwar, Massimiliano Tamborrino, Irene Tubikanec

    Approximate Bayesian computation (ABC) has become one of the major tools of likelihood-free statistical inference in complex mathematical models. Simultaneously, stochastic differential equations (SDEs) have developed to an established tool for modelling time-dependent, real-world phenomena with underlying random effects. When applying ABC to stochastic models, two major difficulties arise: First,

  • Multi-level Monte Carlo methods for the approximation of invariant measures of stochastic differential equations
    Stat. Comput. (IF 3.035) Pub Date : 2019-09-10
    Michael B. Giles, Mateusz B. Majka, Lukasz Szpruch, Sebastian J. Vollmer, Konstantinos C. Zygalakis

    We develop a framework that allows the use of the multi-level Monte Carlo (MLMC) methodology (Giles in Acta Numer. 24:259–328, 2015. https://doi.org/10.1017/S096249291500001X) to calculate expectations with respect to the invariant measure of an ergodic SDE. In that context, we study the (over-damped) Langevin equations with a strongly concave potential. We show that when appropriate contracting couplings

  • Regularized estimation for highly multivariate log Gaussian Cox processes
    Stat. Comput. (IF 3.035) Pub Date : 2019-11-15
    Achmad Choiruddin, Francisco Cuevas-Pacheco, Jean-François Coeurjolly, Rasmus Waagepetersen

    Statistical inference for highly multivariate point pattern data is challenging due to complex models with large numbers of parameters. In this paper, we develop numerically stable and efficient parameter estimation and model selection algorithms for a class of multivariate log Gaussian Cox processes. The methodology is applied to a highly multivariate point pattern data set from tropical rain forest

  • Data-driven stochastic inversion via functional quantization
    Stat. Comput. (IF 3.035) Pub Date : 2019-09-13
    Mohamed Reda El Amri, Céline Helbert, Olivier Lepreux, Miguel Munoz Zuniga, Clémentine Prieur, Delphine Sinoquet

    In this paper, we propose a new methodology for solving stochastic inversion problems through computer experiments, the stochasticity being driven by a functional random variables. This study is motivated by an automotive application. In this context, the simulator code takes a double set of simulation inputs: deterministic control variables and functional uncertain variables. This framework is characterized

  • Sampling from manifold-restricted distributions using tangent bundle projections
    Stat. Comput. (IF 3.035) Pub Date : 2019-10-14
    Alvin J. K. Chua

    A common problem in Bayesian inference is the sampling of target probability distributions at sufficient resolution and accuracy to estimate the probability density and to compute credible regions. Often by construction, many target distributions can be expressed as some higher-dimensional closed-form distribution with parametrically constrained variables, i.e., one that is restricted to a smooth submanifold

  • High-dimensional regression in practice: an empirical study of finite-sample prediction, variable selection and ranking
    Stat. Comput. (IF 3.035) Pub Date : 2019-12-19
    Fan Wang, Sach Mukherjee, Sylvia Richardson, Steven M. Hill

    Penalized likelihood approaches are widely used for high-dimensional regression. Although many methods have been proposed and the associated theory is now well developed, the relative efficacy of different approaches in finite-sample settings, as encountered in practice, remains incompletely understood. There is therefore a need for empirical investigations in this area that can offer practical insight

  • Local dimension reduction of summary statistics for likelihood-free inference
    Stat. Comput. (IF 3.035) Pub Date : 2019-10-04
    Jukka Sirén, Samuel Kaski

    Approximate Bayesian computation (ABC) and other likelihood-free inference methods have gained popularity in the last decade, as they allow rigorous statistical inference for complex models without analytically tractable likelihood functions. A key component for accurate inference with ABC is the choice of summary statistics, which summarize the information in the data, but at the same time should

  • Clustering multivariate data using factor analytic Bayesian mixtures with an unknown number of components
    Stat. Comput. (IF 3.035) Pub Date : 2019-08-27
    Panagiotis Papastamoulis

    Recent work on overfitting Bayesian mixtures of distributions offers a powerful framework for clustering multivariate data using a latent Gaussian model which resembles the factor analysis model. The flexibility provided by overfitting mixture models yields a simple and efficient way in order to estimate the unknown number of clusters and model parameters by Markov chain Monte Carlo sampling. The present

  • Sequential Monte Carlo with transformations.
    Stat. Comput. (IF 3.035) Pub Date : 2019-11-17
    Richard G Everitt,Richard Culliford,Felipe Medina-Aguayo,Daniel J Wilson

    This paper examines methodology for performing Bayesian inference sequentially on a sequence of posteriors on spaces of different dimensions. For this, we use sequential Monte Carlo samplers, introducing the innovation of using deterministic transformations to move particles effectively between target distributions with different dimensions. This approach, combined with adaptive methods, yields an

  • Approximation and sampling of multivariate probability distributions in the tensor train decomposition
    Stat. Comput. (IF 3.035) Pub Date : 2019-11-02
    Sergey Dolgov, Karim Anaya-Izquierdo, Colin Fox, Robert Scheichl

    General multivariate distributions are notoriously expensive to sample from, particularly the high-dimensional posterior distributions in PDE-constrained inverse problems. This paper develops a sampler for arbitrary continuous multivariate distributions that is based on low-rank surrogates in the tensor train format, a methodology that has been exploited for many years for scalable, high-dimensional

  • Central quantile subspace
    Stat. Comput. (IF 3.035) Pub Date : 2019-12-18
    Eliana Christou

    Quantile regression (QR) is becoming increasingly popular due to its relevance in many scientific investigations. There is a great amount of work about linear and nonlinear QR models. Specifically, nonparametric estimation of the conditional quantiles received particular attention, due to its model flexibility. However, nonparametric QR techniques are limited in the number of covariates. Dimension

  • Coordinate sampler: a non-reversible Gibbs-like MCMC sampler
    Stat. Comput. (IF 3.035) Pub Date : 2019-12-26
    Changye Wu, Christian P. Robert

    We derive a novel non-reversible, continuous-time Markov chain Monte Carlo sampler, called Coordinate Sampler, based on a piecewise deterministic Markov process, which is a variant of the Zigzag sampler of Bierkens et al. (Ann Stat 47(3):1288–1320, 2019). In addition to providing a theoretical validation for this new simulation algorithm, we show that the Markov chain it induces exhibits geometrical

  • Optimal non-negative forecast reconciliation
    Stat. Comput. (IF 3.035) Pub Date : 2020-04-08
    Shanika L. Wickramasuriya, Berwin A. Turlach, Rob J. Hyndman

    The sum of forecasts of disaggregated time series is often required to equal the forecast of the aggregate, giving a set of coherent forecasts. The least squares solution for finding coherent forecasts uses a reconciliation approach known as MinT, proposed by Wickramasuriya, Athanasopoulos, and Hyndman (2019). The MinT approach and its variants do not guarantee that the coherent forecasts are non-negative

  • A Laplace-based algorithm for Bayesian adaptive design
    Stat. Comput. (IF 3.035) Pub Date : 2020-04-05
    S. G. J. Senarathne, C. C. Drovandi, J. M. McGree

    This article presents a novel Laplace-based algorithm that can be used to find Bayesian adaptive designs under model and parameter uncertainty. Our algorithm uses Laplace importance sampling to provide a computationally efficient approach to undertake adaptive design and inference when compared to standard approaches such as those based on the sequential Monte Carlo (SMC) algorithm. Like the SMC approach

  • Estimating time-varying directed neural networks
    Stat. Comput. (IF 3.035) Pub Date : 2020-04-04
    Haixu Wang, Jiguo Cao

    Reconstructing the functional network of a neuron cluster is a fundamental step to reveal the complex interactions among neural systems of the brain. Current approaches to reconstruct a network of neurons or neural systems focus on establishing a static network by assuming the neural network structure does not change over time. To the best of our knowledge, this is the first attempt to build a time-varying

  • High-dimensional changepoint detection via a geometrically inspired mapping
    Stat. Comput. (IF 3.035) Pub Date : 2020-03-28
    Thomas Grundy, Rebecca Killick, Gueorgui Mihaylov

    High-dimensional changepoint analysis is a growing area of research and has applications in a wide range of fields. The aim is to accurately and efficiently detect changepoints in time series data when both the number of time points and dimensions grow large. Existing methods typically aggregate or project the data to a smaller number of dimensions, usually one. We present a high-dimensional changepoint

  • High-dimensional VAR with low-rank transition
    Stat. Comput. (IF 3.035) Pub Date : 2020-03-16
    Pierre Alquier, Karine Bertin, Paul Doukhan, Rémy Garnier

    We propose a vector auto-regressive model with a low-rank constraint on the transition matrix. This model is well suited to predict high-dimensional series that are highly correlated, or that are driven by a small number of hidden factors. While our model has formal similarities with factor models, its structure is more a way to reduce the dimension in order to improve the predictions, rather than

  • Matrix completion with nonconvex regularization: spectral operators and scalable algorithms
    Stat. Comput. (IF 3.035) Pub Date : 2020-03-14
    Rahul Mazumder, Diego Saldana, Haolei Weng

    In this paper, we study the popularly dubbed matrix completion problem, where the task is to “fill in” the unobserved entries of a matrix from a small subset of observed entries, under the assumption that the underlying matrix is of low rank. Our contributions herein enhance our prior work on nuclear norm regularized problems for matrix completion (Mazumder et al. in J Mach Learn Res 1532(11):2287–2322

  • Optimal classification of Gaussian processes in homo- and heteroscedastic settings
    Stat. Comput. (IF 3.035) Pub Date : 2020-03-12
    José L. Torrecilla, Carlos Ramos-Carreño, Manuel Sánchez-Montañés, Alberto Suárez

    A procedure to derive optimal discrimination rules is formulated for binary functional classification problems in which the instances available for induction are characterized by random trajectories sampled from different Gaussian processes, depending on the class label. Specifically, these optimal rules are derived as the asymptotic form of the quadratic discriminant for the discretely monitored trajectories

  • Likelihood-free approximate Gibbs sampling
    Stat. Comput. (IF 3.035) Pub Date : 2020-03-11
    G. S. Rodrigues, David J. Nott, S. A. Sisson

    Likelihood-free methods such as approximate Bayesian computation (ABC) have extended the reach of statistical inference to problems with computationally intractable likelihoods. Such approaches perform well for small-to-moderate dimensional problems, but suffer a curse of dimensionality in the number of model parameters. We introduce a likelihood-free approximate Gibbs sampler that naturally circumvents

  • Adaptive iterative Hessian sketch via A -optimal subsampling
    Stat. Comput. (IF 3.035) Pub Date : 2020-03-11
    Aijun Zhang, Hengtao Zhang, Guosheng Yin

    Iterative Hessian sketch (IHS) is an effective sketching method for modeling large-scale data. It was originally proposed by Pilanci and Wainwright (J Mach Learn Res 17(1):1842–1879, 2016) based on randomized sketching matrices. However, it is computationally intensive due to the iterative sketch process. In this paper, we analyze the IHS algorithm under the unconstrained least squares problem setting

  • Accelerating Metropolis-within-Gibbs sampler with localized computations of differential equations
    Stat. Comput. (IF 3.035) Pub Date : 2020-03-06
    Qiang Liu, Xin T. Tong

    Inverse problem is ubiquitous in science and engineering, and Bayesian methodologies are often used to infer the underlying parameters. For high-dimensional temporal-spatial models, classical Markov chain Monte Carlo methods are often slow to converge, and it is necessary to apply Metropolis-within-Gibbs (MwG) sampling on parameter blocks. However, the computation cost of each MwG iteration is typically

  • Bayesian nonparametric priors for hidden Markov random fields
    Stat. Comput. (IF 3.035) Pub Date : 2020-03-04
    Hongliang Lü, Julyan Arbel, Florence Forbes

    One of the central issues in statistics and machine learning is how to select an adequate model that can automatically adapt its complexity to the observed data. In the present paper, we focus on the issue of determining the structure of clustered data, both in terms of finding the appropriate number of clusters and of modeling the right dependence structure between the observations. Bayesian nonparametric

  • Variance reduction for Markov chains with application to MCMC
    Stat. Comput. (IF 3.035) Pub Date : 2020-02-28
    D. Belomestny, L. Iosipoi, E. Moulines, A. Naumov, S. Samsonov

    In this paper, we propose a novel variance reduction approach for additive functionals of Markov chains based on minimization of an estimate for the asymptotic variance of these functionals over suitable classes of control variates. A distinctive feature of the proposed approach is its ability to significantly reduce the overall finite sample variance. This feature is theoretically demonstrated by

  • Metrics and barycenters for point pattern data
    Stat. Comput. (IF 3.035) Pub Date : 2020-02-24
    Raoul Müller, Dominic Schuhmacher, Jorge Mateu

    We introduce the transport–transform and the relative transport–transform metrics between finite point patterns on a general space, which provide a unified framework for earlier point pattern metrics, in particular the generalized spike time and the normalized and unnormalized optimal subpattern assignment metrics. Our main focus is on barycenters, i.e., minimizers of a q-th-order Fréchet functional

  • Variational discriminant analysis with variable selection
    Stat. Comput. (IF 3.035) Pub Date : 2020-02-19
    Weichang Yu, John T. Ormerod, Michael Stewart

    A fast Bayesian method that seamlessly fuses classification and hypothesis testing via discriminant analysis is developed. Building upon the original discriminant analysis classifier, modelling components are added to identify discriminative variables. A combination of cake priors and a novel form of variational Bayes we call reverse collapsed variational Bayes gives rise to variable selection that

  • Random time step probabilistic methods for uncertainty quantification in chaotic and geometric numerical integration
    Stat. Comput. (IF 3.035) Pub Date : 2020-02-14
    Assyr Abdulle, Giacomo Garegnani

    A novel probabilistic numerical method for quantifying the uncertainty induced by the time integration of ordinary differential equations (ODEs) is introduced. Departing from the classical strategy to randomise ODE solvers by adding a random forcing term, we show that a probability measure over the numerical solution of ODEs can be obtained by introducing suitable random time steps in a classical time

  • Monte Carlo co-ordinate ascent variational inference
    Stat. Comput. (IF 3.035) Pub Date : 2020-02-14
    Lifeng Ye, Alexandros Beskos, Maria De Iorio, Jie Hao

    In variational inference (VI), coordinate-ascent and gradient-based approaches are two major types of algorithms for approximating difficult-to-compute probability densities. In real-world implementations of complex models, Monte Carlo methods are widely used to estimate expectations in coordinate-ascent approaches and gradients in derivative-driven ones. We discuss a Monte Carlo co-ordinate ascent

  • Incomplete-data Fisher scoring method with steplength adjustment
    Stat. Comput. (IF 3.035) Pub Date : 2020-02-05
    Keiji Takai

    An incomplete-data Fisher scoring method is proposed for parameter estimation in models where data are missing and in latent-variable models that can be formulated as a missing data problem. The convergence properties of the proposed method and an accelerated variant of this method are provided. The main features of this method are its ability to accelerate the rate of convergence by adjusting the

  • Efficient Bayesian shape-restricted function estimation with constrained Gaussian process priors
    Stat. Comput. (IF 3.035) Pub Date : 2020-01-30
    Pallavi Ray, Debdeep Pati, Anirban Bhattacharya

    This article revisits the problem of Bayesian shape-restricted inference in the light of a recently developed approximate Gaussian process that admits an equivalent formulation of the shape constraints in terms of the basis coefficients. We propose a strategy to efficiently sample from the resulting constrained posterior by absorbing a smooth relaxation of the constraint in the likelihood and using

  • Detecting anomalies in fibre systems using 3-dimensional image data
    Stat. Comput. (IF 3.035) Pub Date : 2020-01-28
    Denis Dresvyanskiy, Tatiana Karaseva, Vitalii Makogin, Sergei Mitrofanov, Claudia Redenbach, Evgeny Spodarev

    We consider the problem of detecting anomalies in the directional distribution of fibre materials observed in 3D images. We divide the image into a set of scanning windows and classify them into two clusters: homogeneous material and anomaly. Based on a sample of estimated local fibre directions, for each scanning window we compute several classification attributes, namely the coordinate wise means

  • A flexible particle Markov chain Monte Carlo method
    Stat. Comput. (IF 3.035) Pub Date : 2020-01-14
    Eduardo F. Mendes, Christopher K. Carter, David Gunawan, Robert Kohn

    Particle Markov Chain Monte Carlo methods are used to carry out inference in nonlinear and non-Gaussian state space models, where the posterior density of the states is approximated using particles. Current approaches usually perform Bayesian inference using either a particle marginal Metropolis–Hastings (PMMH) algorithm or a particle Gibbs (PG) sampler. This paper shows how the two ways of generating

  • Functional single-index quantile regression models
    Stat. Comput. (IF 3.035) Pub Date : 2020-01-13
    Peijun Sang, Jiguo Cao

    It is known that functional single-index regression models can achieve better prediction accuracy than functional linear models or fully nonparametric models, when the target is to predict a scalar response using a function-valued covariate. However, the performance of these models may be adversely affected by extremely large values or skewness in the response. In addition, they are not able to offer

  • A new mixture model on the simplex
    Stat. Comput. (IF 3.035) Pub Date : 2020-01-10
    Andrea Ongaro, Sonia Migliorati, Roberto Ascari

    This paper is meant to introduce a significant extension of the flexible Dirichlet (FD) distribution, which is a quite tractable special mixture model for compositional data, i.e. data representing vectors of proportions of a whole. The FD model displays several theoretical properties which make it suitable for inference, and fairly easy to handle from a computational viewpoint. However, the rigid

  • Mini-batch learning of exponential family finite mixture models
    Stat. Comput. (IF 3.035) Pub Date : 2020-01-10
    Hien D. Nguyen, Florence Forbes, Geoffrey J. McLachlan

    Mini-batch algorithms have become increasingly popular due to the requirement for solving optimization problems, based on large-scale data sets. Using an existing online expectation–maximization (EM) algorithm framework, we demonstrate how mini-batch (MB) algorithms may be constructed, and propose a scheme for the stochastic stabilization of the constructed mini-batch algorithms. Theoretical results

  • Bayesian Additive Regression Trees using Bayesian Model Averaging.
    Stat. Comput. (IF 3.035) Pub Date : 2018-11-20
    Belinda Hernández,Adrian E Raftery,Stephen R Pennington,Andrew C Parnell

    Bayesian Additive Regression Trees (BART) is a statistical sum of trees model. It can be considered a Bayesian version of machine learning tree ensemble methods where the individual trees are the base learners. However for datasets where the number of variables p is large the algorithm can become inefficient and computationally expensive. Another method which is popular for high dimensional data is

  • Optimal Bayesian estimators for latent variable cluster models.
    Stat. Comput. (IF 3.035) Pub Date : 2018-09-18
    Riccardo Rastelli,Nial Friel

    In cluster analysis interest lies in probabilistically capturing partitions of individuals, items or observations into groups, such that those belonging to the same group share similar attributes or relational profiles. Bayesian posterior samples for the latent allocation variables can be effectively obtained in a wide range of clustering models, including finite mixtures, infinite mixtures, hidden

  • Trajectory inference and parameter estimation in stochastic models with temporally aggregated data.
    Stat. Comput. (IF 3.035) Pub Date : 2018-08-28
    Maria Myrto Folia,Magnus Rattray

    Stochastic models are of fundamental importance in many scientific and engineering applications. For example, stochastic models provide valuable insights into the causes and consequences of intra-cellular fluctuations and inter-cellular heterogeneity in molecular biology. The chemical master equation can be used to model intra-cellular stochasticity in living cells, but analytical solutions are rare

  • Fast covariance estimation for sparse functional data.
    Stat. Comput. (IF 3.035) Pub Date : 2018-02-17
    Luo Xiao,Cai Li,William Checkley,Ciprian Crainiceanu

    Smoothing of noisy sample covariances is an important component in functional data analysis. We propose a novel covariance smoothing method based on penalized splines and associated software. The proposed method is a bivariate spline smoother that is designed for covariance smoothing and can be used for sparse functional or longitudinal data. We propose a fast algorithm for covariance smoothing using

  • Double-Parallel Monte Carlo for Bayesian Analysis of Big Data.
    Stat. Comput. (IF 3.035) Pub Date : 2017-11-27
    Jingnan Xue,Faming Liang

    This paper proposes a simple, practical and efficient MCMC algorithm for Bayesian analysis of big data. The proposed algorithm suggests to divide the big dataset into some smaller subsets and provides a simple method to aggregate the subset posteriors to approximate the full data posterior. To further speed up computation, the proposed algorithm employs the population stochastic approximation Monte

  • Hamiltonian Monte Carlo acceleration using surrogate functions with random bases.
    Stat. Comput. (IF 3.035) Pub Date : 2017-10-07
    Cheng Zhang,Babak Shahbaba,Hongkai Zhao

    For big data analysis, high computational cost for Bayesian methods often limits their applications in practice. In recent years, there have been many attempts to improve computational efficiency of Bayesian inference. Here we propose an efficient and scalable computational technique for a state-of-the-art Markov chain Monte Carlo methods, namely, Hamiltonian Monte Carlo. The key idea is to explore

  • A second-order iterated smoothing algorithm.
    Stat. Comput. (IF 3.035) Pub Date : 2017-09-02
    Dao Nguyen,Edward L Ionides

    Simulation-based inference for partially observed stochastic dynamic models is currently receiving much attention due to the fact that direct computation of the likelihood is not possible in many practical situations. Iterated filtering methodologies enable maximization of the likelihood function using simulation-based sequential Monte Carlo filters. Doucet et al. (2013) developed an approximation

  • Unbiased Bayesian inference for population Markov jump processes via random truncations.
    Stat. Comput. (IF 3.035) Pub Date : 2017-07-12
    Anastasis Georgoulas,Jane Hillston,Guido Sanguinetti

    We consider continuous time Markovian processes where populations of individual agents interact stochastically according to kinetic rules. Despite the increasing prominence of such models in fields ranging from biology to smart cities, Bayesian inference for such systems remains challenging, as these are continuous time, discrete state systems with potentially infinite state-space. Here we propose

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