Statistics and Computing
Volume 1 / 1991 - Volume 27 / 2017
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14,708 Result(s)
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Article
Approximate computations for binary Markov random fields and their use in Bayesian models
Discrete Markov random fields form a natural class of models to represent images and spatial datasets. The use of such models is, however, hampered by a computationally intractable normalising constant. This m...
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Article
Multiscale local polynomial decompositions using bandwidths as scales
The multiscale local polynomial transform, developped in this paper, combines the benefits from local polynomial smoothing with sparse multiscale decompositions. The contribution of the paper is twofold. First...
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Article
Quasi-random numbers for copula models
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 distribut...
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Article
Multiple Monte Carlo testing, with applications in spatial point processes
The rank envelope test (Myllymäki et al. in J R Stat Soc B, doi:10.1111/rssb.12172, 2016) is proposed as a solution to the multiple testing problem for Monte...
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Article
ABC Shadow algorithm: a tool for statistical analysis of spatial patterns
This paper presents an original ABC algorithm, ABC Shadow, that can be applied to sample posterior densities that are continuously differentiable. The proposed algorithm solves the main condition to be fulfilled ...
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Multi-dimensional functional principal component analysis
Functional principal component analysis is one of the most commonly employed approaches in functional and longitudinal data analysis and we extend it to analyze functional/longitudinal data observed on a general
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Exact Bayesian inference for off-line change-point detection in tree-structured graphical models
We consider the problem of change-point detection in multivariate time-series. The multivariate distribution of the observations is supposed to follow a graphical model, whose graph and parameters are affected...
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Article
An adaptive-to-model test for partially parametric single-index models
Residual marked empirical process-based tests are commonly used in regression models. However, they suffer from data sparseness in high-dimensional space when there are many covariates. This paper has three pu...
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Article
Erratum to: Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
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Article
Diagonal latent block model for binary data
This paper addresses the problem of co-clustering binary data in the latent block model framework with diagonal constraints for resulting data partitions. We consider the Bernoulli generative mixture model and...
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Article
Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
Leave-one-out cross-validation (LOO) and the widely applicable information criterion (WAIC) are methods for estimating pointwise out-of-sample prediction accuracy from a fitted Bayesian model using the log-lik...
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Article
Continuous monitoring for changepoints in data streams using adaptive estimation
Data streams are characterised by a potentially unending sequence of high-frequency observations which are subject to unknown temporal variation. Many modern streaming applications demand the capability to seq...
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Article
Fast parallel \(\alpha \) -stable distribution function evaluation and parameter estimation using OpenCL in GPGPUs
\(\alpha \) α ...
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Article
Penalized empirical likelihood inference for sparse additive hazards regression with a diverging number of covariates
High-dimensional sparse modeling with censored survival data is of great practical importance, as exemplified by applications in high-throughput genomic data analysis. In this paper, we propose a class of regu...
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Open Access This content is freely available online to anyone, anywhere at any time.
Article
A computationally efficient nonparametric approach for changepoint detection
In this paper we build on an approach proposed by Zou et al. (2014) for nonparametric changepoint detection. This approach defines the best segmentation for a data set as the one which minimises a penalised cost ...
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Journal
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Article
Stable prediction in high-dimensional linear models
We propose a Random Splitting Model Averaging procedure, RSMA, to achieve stable predictions in high-dimensional linear models. The idea is to use split training data to construct and estimate candidate models...
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Article
Restricted Indian buffet processes
Latent feature models are a powerful tool for modeling data with globally-shared features. Nonparametric distributions over exchangeable sets of features, such as the Indian Buffet Process, offer modeling flex...
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Article
Thermodynamic Bayesian model comparison
Thermodynamics have been shown to have direct applications in Bayesian model evaluation. Within a tempered transitions scheme, the Boltzmann–Gibbs distribution pertaining to different Hamiltonians is implement...
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Article
Improved multi-objective clustering with automatic determination of the number of clusters
The multi-objective clustering with automatic determination of the number of clusters (MOCK) approach is improved in this work by means of an empirical comparison of three multi-objective evolutionary algorith...
