• Stat. Comput. (IF 2.383) Pub Date : 2020-03-28
Thomas Grundy, Rebecca Killick, Gueorgui Mihaylov

Abstract 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

更新日期：2020-03-28
• Stat. Comput. (IF 2.383) Pub Date : 2019-10-05
Georg Hahn

Abstract 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

更新日期：2020-03-20
• Stat. Comput. (IF 2.383) Pub Date : 2019-10-04
Ziwen An, David J. Nott, Christopher Drovandi

Abstract 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

更新日期：2020-03-20
• Stat. Comput. (IF 2.383) Pub Date : 2019-11-05
Evelyn Buckwar, Massimiliano Tamborrino, Irene Tubikanec

Abstract 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:

更新日期：2020-03-20
• Stat. Comput. (IF 2.383) Pub Date : 2019-09-10
Michael B. Giles, Mateusz B. Majka, Lukasz Szpruch, Sebastian J. Vollmer, Konstantinos C. Zygalakis

Abstract 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

更新日期：2020-03-20
• Stat. Comput. (IF 2.383) Pub Date : 2019-11-15
Achmad Choiruddin, Francisco Cuevas-Pacheco, Jean-François Coeurjolly, Rasmus Waagepetersen

Abstract 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

更新日期：2020-03-20
• Stat. Comput. (IF 2.383) Pub Date : 2019-09-13
Mohamed Reda El Amri, Céline Helbert, Olivier Lepreux, Miguel Munoz Zuniga, Clémentine Prieur, Delphine Sinoquet

Abstract 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

更新日期：2020-03-20
• Stat. Comput. (IF 2.383) Pub Date : 2019-10-14
Alvin J. K. Chua

Abstract 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

更新日期：2020-03-20
• Stat. Comput. (IF 2.383) Pub Date : 2019-12-19
Fan Wang, Sach Mukherjee, Sylvia Richardson, Steven M. Hill

Abstract 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

更新日期：2020-03-20
• Stat. Comput. (IF 2.383) Pub Date : 2019-10-04

Abstract 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

更新日期：2020-03-20
• Stat. Comput. (IF 2.383) Pub Date : 2019-08-27
Panagiotis Papastamoulis

Abstract 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

更新日期：2020-03-20
• Stat. Comput. (IF 2.383) 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

更新日期：2020-03-20
• Stat. Comput. (IF 2.383) Pub Date : 2019-11-02
Sergey Dolgov, Karim Anaya-Izquierdo, Colin Fox, Robert Scheichl

Abstract 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

更新日期：2020-03-20
• Stat. Comput. (IF 2.383) Pub Date : 2019-12-18
Eliana Christou

Abstract 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

更新日期：2020-03-20
• Stat. Comput. (IF 2.383) Pub Date : 2019-12-26
Changye Wu, Christian P. Robert

Abstract 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

更新日期：2020-03-20
• Stat. Comput. (IF 2.383) Pub Date : 2020-03-16
Pierre Alquier, Karine Bertin, Paul Doukhan, Rémy Garnier

Abstract 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

更新日期：2020-03-20
• Stat. Comput. (IF 2.383) Pub Date : 2020-03-14
Rahul Mazumder, Diego Saldana, Haolei Weng

Abstract 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

更新日期：2020-03-20
• Stat. Comput. (IF 2.383) Pub Date : 2020-03-12
José L. Torrecilla, Carlos Ramos-Carreño, Manuel Sánchez-Montañés, Alberto Suárez

Abstract 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

更新日期：2020-03-20
• Stat. Comput. (IF 2.383) Pub Date : 2020-03-11
G. S. Rodrigues, David J. Nott, S. A. Sisson

Abstract 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

更新日期：2020-03-20
• Stat. Comput. (IF 2.383) Pub Date : 2020-03-11
Aijun Zhang, Hengtao Zhang, Guosheng Yin

Abstract 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

更新日期：2020-03-20
• Stat. Comput. (IF 2.383) Pub Date : 2020-03-06
Qiang Liu, Xin T. Tong

Abstract 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

更新日期：2020-03-20
• Stat. Comput. (IF 2.383) 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

更新日期：2019-11-01
• Stat. Comput. (IF 2.383) 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

更新日期：2019-11-01
• Stat. Comput. (IF 2.383) 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

更新日期：2019-11-01
• Stat. Comput. (IF 2.383) 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

更新日期：2019-11-01
• Stat. Comput. (IF 2.383) 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

更新日期：2019-11-01
• Stat. Comput. (IF 2.383) 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

更新日期：2019-11-01
• Stat. Comput. (IF 2.383) 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

更新日期：2019-11-01
• Stat. Comput. (IF 2.383) 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

更新日期：2019-11-01
• Stat. Comput. (IF 2.383) Pub Date : 2016-07-16

In truncated polynomial spline or B-spline models where the covariates are measured with error, a fully Bayesian approach to model fitting requires the covariates and model parameters to be sampled at every Markov chain Monte Carlo iteration. Sampling the unobserved covariates poses a major computational problem and usually Gibbs sampling is not possible. This forces the practitioner to use a Metropolis-Hastings

更新日期：2019-11-01
• Stat. Comput. (IF 2.383) Pub Date : 2016-02-24
Gertraud Malsiner-Walli,Sylvia Frühwirth-Schnatter,Bettina Grün

In the framework of Bayesian model-based clustering based on a finite mixture of Gaussian distributions, we present a joint approach to estimate the number of mixture components and identify cluster-relevant variables simultaneously as well as to obtain an identified model. Our approach consists in specifying sparse hierarchical priors on the mixture weights and component means. In a deliberately overfitting

更新日期：2019-11-01
• Stat. Comput. (IF 2.383) Pub Date : 2016-02-24

We propose two fast covariance smoothing methods and associated software that scale up linearly with the number of observations per function. Most available methods and software cannot smooth covariance matrices of dimension J > 500; a recently introduced sandwich smoother is an exception but is not adapted to smooth covariance matrices of large dimensions, such as J = 10, 000. We introduce two new

更新日期：2019-11-01
• Stat. Comput. (IF 2.383) Pub Date : 2015-09-29

Recent advances in Monte Carlo methods allow us to revisit work by de Finetti who suggested the use of approximate exchangeability in the analyses of contingency tables. This paper gives examples of computational implementations using Metropolis Hastings, Langevin and Hamiltonian Monte Carlo to compute posterior distributions for test statistics relevant for testing independence, reversible or three

更新日期：2019-11-01
• Stat. Comput. (IF 2.383) Pub Date : 2015-09-01
David I Hastie,Silvia Liverani,Sylvia Richardson

We consider the question of Markov chain Monte Carlo sampling from a general stick-breaking Dirichlet process mixture model, with concentration parameter [Formula: see text]. This paper introduces a Gibbs sampling algorithm that combines the slice sampling approach of Walker (Communications in Statistics - Simulation and Computation 36:45-54, 2007) and the retrospective sampling approach of Papaspiliopoulos

更新日期：2019-11-01
• Stat. Comput. (IF 2.383) Pub Date : 2015-07-04
Edoardo M Airoldi,Panos Toulis

Estimation with large amounts of data can be facilitated by stochastic gradient methods, in which model parameters are updated sequentially using small batches of data at each step. Here, we review early work and modern results that illustrate the statistical properties of these methods, including convergence rates, stability, and asymptotic bias and variance. We then overview modern applications where

更新日期：2019-11-01
• Stat. Comput. (IF 2.383) Pub Date : 2015-06-23
S R White,T Kypraios,S P Preston

Many modern statistical applications involve inference for complicated stochastic models for which the likelihood function is difficult or even impossible to calculate, and hence conventional likelihood-based inferential techniques cannot be used. In such settings, Bayesian inference can be performed using Approximate Bayesian Computation (ABC). However, in spite of many recent developments to ABC

更新日期：2019-11-01
• Stat. Comput. (IF 2.383) Pub Date : 2015-03-10
Patrick Breheny,Jian Huang

Penalized regression is an attractive framework for variable selection problems. Often, variables possess a grouping structure, and the relevant selection problem is that of selecting groups, not individual variables. The group lasso has been proposed as a way of extending the ideas of the lasso to the problem of group selection. Nonconvex penalties such as SCAD and MCP have been proposed and shown

更新日期：2019-11-01
• Stat. Comput. (IF 2.383) Pub Date : 2014-10-22
Limin Peng,Jinfeng Xu,Nancy Kutner

Varying covariate effects often manifest meaningful heterogeneity in covariate-response associations. In this paper, we adopt a quantile regression model that assumes linearity at a continuous range of quantile levels as a tool to explore such data dynamics. The consideration of potential non-constancy of covariate effects necessitates a new perspective for variable selection, which, under the assumed

更新日期：2019-11-01
• Stat. Comput. (IF 2.383) Pub Date : 2014-10-14
Dingfeng Jiang,Jian Huang

Recent studies have demonstrated theoretical attractiveness of a class of concave penalties in variable selection, including the smoothly clipped absolute deviation and minimax concave penalties. The computation of the concave penalized solutions in high-dimensional models, however, is a difficult task. We propose a majorization minimization by coordinate descent (MMCD) algorithm for computing the

更新日期：2019-11-01
• Stat. Comput. (IF 2.383) Pub Date : 2013-08-21
Matthias Chung,Qi Long,Brent A Johnson

The analysis of survival endpoints subject to right-censoring is an important research area in statistics, particularly among econometricians and biostatisticians. The two most popular semiparametric models are the proportional hazards model and the accelerated failure time (AFT) model. Rank-based estimation in the AFT model is computationally challenging due to optimization of a non-smooth loss function

更新日期：2019-11-01
• Stat. Comput. (IF 2.383) Pub Date : 2011-11-30
Kenneth Lo,Raphael Gottardo

Cluster analysis is the automated search for groups of homogeneous observations in a data set. A popular modeling approach for clustering is based on finite normal mixture models, which assume that each cluster is modeled as a multivariate normal distribution. However, the normality assumption that each component is symmetric is often unrealistic. Furthermore, normal mixture models are not robust against

更新日期：2019-11-01
• Stat. Comput. (IF 2.383) Pub Date : 2011-03-02
Hua Zhou,David Alexander,Kenneth Lange

In many statistical problems, maximum likelihood estimation by an EM or MM algorithm suffers from excruciatingly slow convergence. This tendency limits the application of these algorithms to modern high-dimensional problems in data mining, genomics, and imaging. Unfortunately, most existing acceleration techniques are ill-suited to complicated models involving large numbers of parameters. The squared

更新日期：2019-11-01
• Stat. Comput. (IF 2.383) Pub Date : 2010-04-01
Jinfeng Xu,Chenlei Leng,Zhiliang Ying

A rank-based variable selection procedure is developed for the semiparametric accelerated failure time model with censored observations where the penalized likelihood (partial likelihood) method is not directly applicable. The new method penalizes the rank-based Gehan-type loss function with the ℓ1 penalty. To correctly choose the tuning parameters, a novel likelihood-based χ2-type criterion is proposed

更新日期：2019-11-01
• Stat. Comput. (IF 2.383) Pub Date : 2008-10-07
Sourabh Bhattacharya,Alan E Gelfand,Kent E Holsinger

This paper presents a methodology for model fitting and inference in the context of Bayesian models of the type f(Y | X, theta)f(X | theta)f(theta), where Y is the (set of) observed data, theta is a set of model parameters and X is an unobserved (latent) stationary stochastic process induced by the first order transition model f(X((t+1)) | X((t)), theta), where X((t)) denotes the state of the process

更新日期：2019-11-01
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