Computational Statistics & Data Analysis最新文献_第2页

Boosted sliced regression for dimension reduction in binary classification 用于二分类降维的增强切片回归

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2026-06-01 Epub Date: 2026-01-17 DOI: 10.1016/j.csda.2026.108342

Qin Wang, Edmund Osei

引用次数: 0

Change-point detection for multivariate nonparametric regression with deep neural networks 基于深度神经网络的多元非参数回归变化点检测

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2026-06-01 Epub Date: 2025-12-25 DOI: 10.1016/j.csda.2025.108334

Houlin Zhou , Hanbing Zhu , Xuejun Wang

引用次数: 0

Pure error REML for analyzing data from multi-stratum designs 用于分析多层设计数据的纯误差REML

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2026-06-01 Epub Date: 2025-12-25 DOI: 10.1016/j.csda.2025.108322

Steven G. Gilmour , Peter Goos , Heiko Großmann

{"title":"Pure error REML for analyzing data from multi-stratum designs","authors":"Steven G. Gilmour , Peter Goos , Heiko Großmann","doi":"10.1016/j.csda.2025.108322","DOIUrl":"10.1016/j.csda.2025.108322","url":null,"abstract":"<div><div>Since the dawn of response surface methodology, it has been recommended that designs include replicate points, so that pure error estimates of variance can be obtained and used to provide reliable estimated standard errors of the effects of factors. In designs with more than one stratum, such as split-plot and split-split-plot designs, it is less obvious how pure error estimates of the variance components should be obtained, and no pure error estimates are given by the popular residual maximum likelihood (REML) method of estimation. A method of pure error REML estimation of the variance components, using the full treatment model, is obtained by treating each combination of factor levels as a discrete treatment. This method is easy to implement using standard software and improved estimated standard errors of the fixed effects estimates can be obtained by applying the Kenward-Roger correction based on the pure error REML estimates. The new method is illustrated using several data sets and the performance of pure error REML is compared with the standard REML method. The results are comparable when the assumed response surface model is correct, but the new method is considerably more robust in the case of model misspecification.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"218 ","pages":"Article 108322"},"PeriodicalIF":1.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Conditional independence test in factor models via projection correlation 基于投影相关的因子模型条件独立性检验

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2026-06-01 Epub Date: 2026-01-09 DOI: 10.1016/j.csda.2026.108339

Xilin Zhang , Hongxia Xu , Guoliang Fan , Liping Zhu

引用次数: 0

Multi-population sufficient dimension reduction 多种群充分降维

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2026-05-01 Epub Date: 2025-12-24 DOI: 10.1016/j.csda.2025.108321

Xuerong Meggie Wen , Yuexiao Dong , Li-Xing Zhu

引用次数: 0

Debiased quantile significance testing with machine learning methods 用机器学习方法进行去偏分位数显著性检验

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2026-05-01 Epub Date: 2025-12-05 DOI: 10.1016/j.csda.2025.108319

Jiarong Ding , Yanmei Shi , Niwen Zhou , Mei Yao , Xu Guo

引用次数: 0

Seasonal ARIMA models with a random period 随机周期的季节性ARIMA模型

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2026-05-01 Epub Date: 2025-12-09 DOI: 10.1016/j.csda.2025.108320

Abdelhakim Aknouche , Stefanos Dimitrakopoulos , Nadia Rabehi

{"title":"Seasonal ARIMA models with a random period","authors":"Abdelhakim Aknouche , Stefanos Dimitrakopoulos , Nadia Rabehi","doi":"10.1016/j.csda.2025.108320","DOIUrl":"10.1016/j.csda.2025.108320","url":null,"abstract":"<div><div>A general class of seasonal autoregressive integrated moving average models (SARIMA), whose period is an independent and identically distributed random process valued in a finite set, is proposed. This class of models is named random period seasonal ARIMA (SARIMAR). Attention is focused on three subsets of them: the random period seasonal autoregressive (SARR) models, the random period seasonal moving average (SMAR) models and the random period seasonal autoregressive moving average (SARMAR) models. First, the causality, invertibility, and autocovariance shape of these models are revealed. Then, the estimation of the model components (coefficients, innovation variance, probability distribution of the period, (unobserved) sample-path of the random period) is carried out using the Expectation-Maximization algorithm. In addition, a procedure for random elimination of seasonality is developed. A simulation study is conducted to assess the estimation accuracy of the proposed algorithmic scheme. Finally, the usefulness of the proposed methodology is illustrated with two applications about the annual Wolf sunspot numbers and the Canadian lynx data.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"217 ","pages":"Article 108320"},"PeriodicalIF":1.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145798821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Expectile periodogram Expectile周期图

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2026-05-01 Epub Date: 2025-12-31 DOI: 10.1016/j.csda.2025.108337

Tianbo Chen , Ta-Hsin Li , Hanbing Zhu , Wenwu Gao

引用次数: 0

Random multiplication versus random sum: Autoregressive-like models with integer-valued random inputs 随机乘法与随机和：具有整数值随机输入的自回归模型

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2026-05-01 Epub Date: 2025-12-27 DOI: 10.1016/j.csda.2025.108323

Abdelhakim Aknouche , Sónia Gouveia , Manuel G. Scotto

{"title":"Random multiplication versus random sum: Autoregressive-like models with integer-valued random inputs","authors":"Abdelhakim Aknouche , Sónia Gouveia , Manuel G. Scotto","doi":"10.1016/j.csda.2025.108323","DOIUrl":"10.1016/j.csda.2025.108323","url":null,"abstract":"<div><div>A common approach to analyze time series of counts is to fit models based on random sum operators. As an alternative, this paper introduces time series models based on a random multiplication operator, which is simply the multiplication of a variable operand by an integer-valued random coefficient, whose mean is the constant operand. Such an operation is endowed into autoregressive-like models with integer-valued random inputs, addressed as RMINAR. Two special variants are studied, namely the <span><math><msub><mi>N</mi><mn>0</mn></msub></math></span>-valued random coefficient autoregressive model and the <span><math><msub><mi>N</mi><mn>0</mn></msub></math></span>-valued random coefficient multiplicative error model. Furthermore, <span><math><mi>Z</mi></math></span>-valued extensions are also considered. The dynamic structure of the proposed models is studied in detail. In particular, their corresponding solutions are everywhere strictly stationary and ergodic, which is not common in either the literature on integer-valued time series models or real-valued random coefficient autoregressive models. Therefore, RMINAR model parameters are estimated using a four-stage weighted least squares estimator, with consistency and asymptotic normality established everywhere in the parameter space. Finally, the performance of the new RMINAR models is illustrated with simulated and empirical examples.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"217 ","pages":"Article 108323"},"PeriodicalIF":1.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Nonparametric density estimation on complex domains using manifold-aware Bayesian additive tree models 基于流形感知贝叶斯加性树模型的复域非参数密度估计

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2026-05-01 Epub Date: 2025-12-25 DOI: 10.1016/j.csda.2025.108335

Isaac Diaz-Ray , Huiyan Sang , Guanyu Hu , Ligang Lu

{"title":"Nonparametric density estimation on complex domains using manifold-aware Bayesian additive tree models","authors":"Isaac Diaz-Ray , Huiyan Sang , Guanyu Hu , Ligang Lu","doi":"10.1016/j.csda.2025.108335","DOIUrl":"10.1016/j.csda.2025.108335","url":null,"abstract":"<div><div>Density or intensity function estimation for point pattern data observed on complex domains finds wide applications in spatial data analysis. However, many existing popular density estimation methods face challenges when domains have irregular boundaries, line network structures, sharp concavities, or interior holes. A nonparametric Bayesian additive ensemble of spanning trees model is developed to model the distribution of event occurrences on complex domains. This model uses a random spanning tree weak learner, which can produce flexible and contiguous domain partitions while respecting its geometry and constraints. The method has the advantage of capturing both varying smoothness and sharp changes in density functions. An efficient exact likelihood-based Bayesian inference algorithm is proposed to estimate the density function with uncertainty measures, leveraging a data thinning strategy combined with Poisson-Gamma conjugacy. Simulation studies on various complex domains demonstrate the advantages of the proposed model over competing methods. The method is further applied to the analysis of basketball shot data and crime locations on a road network.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"217 ","pages":"Article 108335"},"PeriodicalIF":1.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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