Statistics and Computing最新文献_第6页

Insufficient Gibbs sampling 吉布斯采样不足

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-05-31 DOI: 10.1007/s11222-024-10423-7

Antoine Luciano, Christian P. Robert, Robin J. Ryder

引用次数: 0

Optimization of the generalized covariance estimator in noncausal processes 非因果过程中广义协方差估计器的优化

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-05-31 DOI: 10.1007/s11222-024-10437-1

Gianluca Cubadda, Francesco Giancaterini, Alain Hecq, Joann Jasiak

{"title":"Optimization of the generalized covariance estimator in noncausal processes","authors":"Gianluca Cubadda, Francesco Giancaterini, Alain Hecq, Joann Jasiak","doi":"10.1007/s11222-024-10437-1","DOIUrl":"https://doi.org/10.1007/s11222-024-10437-1","url":null,"abstract":"This paper investigates the performance of routinely used optimization algorithms in application to the Generalized Covariance estimator (GCov) for univariate and multivariate mixed causal and noncausal models. The GCov is a semi-parametric estimator with an objective function based on nonlinear autocovariances to identify causal and noncausal orders. When the number and type of nonlinear autocovariances included in the objective function are insufficient/inadequate, or the error density is too close to the Gaussian, identification issues can arise. These issues result in local minima in the objective function, which correspond to parameter values associated with incorrect causal and noncausal orders. Then, depending on the starting point and the optimization algorithm employed, the algorithm can converge to a local minimum. The paper proposes the Simulated Annealing (SA) optimization algorithm as an alternative to conventional numerical optimization methods. The results demonstrate that SA performs well in its application to mixed causal and noncausal models, successfully eliminating the effects of local minima. The proposed approach is illustrated by an empirical study of a bivariate series of commodity prices.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"2010 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141190197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A modified EM-type algorithm to estimate semi-parametric mixtures of non-parametric regressions 估计非参数回归半参数混合物的改进型 EM 算法

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-05-29 DOI: 10.1007/s11222-024-10435-3

Sphiwe B. Skhosana, Salomon M. Millard, Frans H. J. Kanfer

{"title":"A modified EM-type algorithm to estimate semi-parametric mixtures of non-parametric regressions","authors":"Sphiwe B. Skhosana, Salomon M. Millard, Frans H. J. Kanfer","doi":"10.1007/s11222-024-10435-3","DOIUrl":"https://doi.org/10.1007/s11222-024-10435-3","url":null,"abstract":"Semi-parametric Gaussian mixtures of non-parametric regressions (SPGMNRs) are a flexible extension of Gaussian mixtures of linear regressions (GMLRs). The model assumes that the component regression functions (CRFs) are non-parametric functions of the covariate(s) whereas the component mixing proportions and variances are constants. Unfortunately, the model cannot be reliably estimated using traditional methods. A local-likelihood approach for estimating the CRFs requires that we maximize a set of local-likelihood functions. Using the Expectation-Maximization (EM) algorithm to separately maximize each local-likelihood function may lead to label-switching. This is because the posterior probabilities calculated at the local E-step are not guaranteed to be aligned. The consequence of this label-switching is wiggly and non-smooth estimates of the CRFs. In this paper, we propose a unified approach to address label-switching and obtain sensible estimates. The proposed approach has two stages. In the first stage, we propose a model-based approach to address the label-switching problem. We first note that each local-likelihood function is a likelihood function of a Gaussian mixture model (GMM). Next, we reformulate the SPGMNRs model as a mixture of these GMMs. Lastly, using a modified version of the Expectation Conditional Maximization (ECM) algorithm, we estimate the mixture of GMMs. In addition, using the mixing weights of the local GMMs, we can automatically choose the local points where local-likelihood estimation takes place. In the second stage, we propose one-step backfitting estimates of the parametric and non-parametric terms. The effectiveness of the proposed approach is demonstrated on simulated data and real data analysis.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"62 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141166408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generalized fused Lasso for grouped data in generalized linear models 广义线性模型中分组数据的广义融合拉索（Generalized fused Lasso

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-05-25 DOI: 10.1007/s11222-024-10433-5

Mineaki Ohishi

引用次数: 0

Type I Tobit Bayesian Additive Regression Trees for censored outcome regression 用于删减结果回归的 I 类托比特贝叶斯加法回归树

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-05-24 DOI: 10.1007/s11222-024-10434-4

Eoghan O’Neill

引用次数: 0

Fused lasso nearly-isotonic signal approximation in general dimensions 一般维度下的融合套索近似等调信号近似法

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-05-22 DOI: 10.1007/s11222-024-10432-6

Vladimir Pastukhov

引用次数: 0

Bayesian cross-validation by parallel Markov chain Monte Carlo 通过并行马尔科夫链蒙特卡罗进行贝叶斯交叉验证

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-05-21 DOI: 10.1007/s11222-024-10404-w

Alex Cooper, Aki Vehtari, Catherine Forbes, Dan Simpson, Lauren Kennedy

{"title":"Bayesian cross-validation by parallel Markov chain Monte Carlo","authors":"Alex Cooper, Aki Vehtari, Catherine Forbes, Dan Simpson, Lauren Kennedy","doi":"10.1007/s11222-024-10404-w","DOIUrl":"https://doi.org/10.1007/s11222-024-10404-w","url":null,"abstract":"Brute force cross-validation (CV) is a method for predictive assessment and model selection that is general and applicable to a wide range of Bayesian models. Naive or ‘brute force’ CV approaches are often too computationally costly for interactive modeling workflows, especially when inference relies on Markov chain Monte Carlo (MCMC). We propose overcoming this limitation using massively parallel MCMC. Using accelerator hardware such as graphics processor units, our approach can be about as fast (in wall clock time) as a single full-data model fit. Parallel CV is flexible because it can easily exploit a wide range data partitioning schemes, such as those designed for non-exchangeable data. It can also accommodate a range of scoring rules. We propose MCMC diagnostics, including a summary of MCMC mixing based on the popular potential scale reduction factor ((widehat{textrm{R}})) and MCMC effective sample size ((widehat{textrm{ESS}})) measures. We also describe a method for determining whether an (widehat{textrm{R}}) diagnostic indicates approximate stationarity of the chains, that may be of more general interest for applications beyond parallel CV. Finally, we show that parallel CV and its diagnostics can be implemented with online algorithms, allowing parallel CV to scale up to very large blocking designs on memory-constrained computing accelerators.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"60 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141150196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spike and slab Bayesian sparse principal component analysis 尖峰和板块贝叶斯稀疏主成分分析

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-05-13 DOI: 10.1007/s11222-024-10430-8

Yu-Chien Bo Ning, Ning Ning

{"title":"Spike and slab Bayesian sparse principal component analysis","authors":"Yu-Chien Bo Ning, Ning Ning","doi":"10.1007/s11222-024-10430-8","DOIUrl":"https://doi.org/10.1007/s11222-024-10430-8","url":null,"abstract":"Sparse principal component analysis (SPCA) is a popular tool for dimensionality reduction in high-dimensional data. However, there is still a lack of theoretically justified Bayesian SPCA methods that can scale well computationally. One of the major challenges in Bayesian SPCA is selecting an appropriate prior for the loadings matrix, considering that principal components are mutually orthogonal. We propose a novel parameter-expanded coordinate ascent variational inference (PX-CAVI) algorithm. This algorithm utilizes a spike and slab prior, which incorporates parameter expansion to cope with the orthogonality constraint. Besides comparing to two popular SPCA approaches, we introduce the PX-EM algorithm as an EM analogue to the PX-CAVI algorithm for comparison. Through extensive numerical simulations, we demonstrate that the PX-CAVI algorithm outperforms these SPCA approaches, showcasing its superiority in terms of performance. We study the posterior contraction rate of the variational posterior, providing a novel contribution to the existing literature. The PX-CAVI algorithm is then applied to study a lung cancer gene expression dataset. The (textsf{R}) package (textsf{VBsparsePCA}) with an implementation of the algorithm is available on the Comprehensive R Archive Network (CRAN).","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"47 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140941321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A general model-checking procedure for semiparametric accelerated failure time models 半参数加速故障时间模型的一般模型检查程序

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-05-07 DOI: 10.1007/s11222-024-10431-7

Dongrak Choi, Woojung Bae, Jun Yan, Sangwook Kang

{"title":"A general model-checking procedure for semiparametric accelerated failure time models","authors":"Dongrak Choi, Woojung Bae, Jun Yan, Sangwook Kang","doi":"10.1007/s11222-024-10431-7","DOIUrl":"https://doi.org/10.1007/s11222-024-10431-7","url":null,"abstract":"We propose a set of goodness-of-fit tests for the semiparametric accelerated failure time (AFT) model, including an omnibus test, a link function test, and a functional form test. This set of tests is derived from a multi-parameter cumulative sum process shown to follow asymptotically a zero-mean Gaussian process. Its evaluation is based on the asymptotically equivalent perturbed version, which enables both graphical and numerical evaluations of the assumed AFT model. Empirical p-values are obtained using the Kolmogorov-type supremum test, which provides a reliable approach for estimating the significance of both proposed un-standardized and standardized test statistics. The proposed procedure is illustrated using the rank-based estimator but is general in the sense that it is directly applicable to some other popular estimators such as induced smoothed rank-based estimator or least-squares estimator that satisfies certain properties. Our proposed methods are rigorously evaluated using extensive simulation experiments that demonstrate their effectiveness in maintaining a Type I error rate and detecting departures from the assumed AFT model in practical sample sizes and censoring rates. Furthermore, the proposed approach is applied to the analysis of the Primary Biliary Cirrhosis data, a widely studied dataset in survival analysis, providing further evidence of the practical usefulness of the proposed methods in real-world scenarios. To make the proposed methods more accessible to researchers, we have implemented them in the R package afttest, which is publicly available on the Comprehensive R Archive Network.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"18 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140882172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A flexible Bayesian tool for CoDa mixed models: logistic-normal distribution with Dirichlet covariance 用于 CoDa 混合模型的灵活贝叶斯工具：具有 Dirichlet 协方差的逻辑正态分布

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-04-16 DOI: 10.1007/s11222-024-10427-3

Joaquín Martínez-Minaya, Haavard Rue

{"title":"A flexible Bayesian tool for CoDa mixed models: logistic-normal distribution with Dirichlet covariance","authors":"Joaquín Martínez-Minaya, Haavard Rue","doi":"10.1007/s11222-024-10427-3","DOIUrl":"https://doi.org/10.1007/s11222-024-10427-3","url":null,"abstract":"Compositional Data Analysis (CoDa) has gained popularity in recent years. This type of data consists of values from disjoint categories that sum up to a constant. Both Dirichlet regression and logistic-normal regression have become popular as CoDa analysis methods. However, fitting this kind of multivariate models presents challenges, especially when structured random effects are included in the model, such as temporal or spatial effects. To overcome these challenges, we propose the logistic-normal Dirichlet Model (LNDM). We seamlessly incorporate this approach into the R-INLA package, facilitating model fitting and model prediction within the framework of Latent Gaussian Models. Moreover, we explore metrics like Deviance Information Criteria, Watanabe Akaike information criterion, and cross-validation measure conditional predictive ordinate for model selection in R-INLA for CoDa. Illustrating LNDM through two simulated examples and with an ecological case study on Arabidopsis thaliana in the Iberian Peninsula, we underscore its potential as an effective tool for managing CoDa and large CoDa databases.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"128 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140613645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0