Computational Statistics最新文献

Bayes estimation of ratio of scale-like parameters for inverse Gaussian distributions and applications to classification 贝叶斯估计反高斯分布的比例类参数比率及其在分类中的应用

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-09-19 DOI: 10.1007/s00180-024-01554-6

Ankur Chakraborty, Nabakumar Jana

引用次数: 0

Multivariate approaches to investigate the home and away behavior of football teams playing football matches 研究足球队主客场比赛行为的多元方法

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-09-17 DOI: 10.1007/s00180-024-01553-7

Antonello D’Ambra, Pietro Amenta, Antonio Lucadamo

引用次数: 0

Kendall correlations and radar charts to include goals for and goals against in soccer rankings 肯德尔相关性和雷达图，在足球排名中纳入进球数和失球数

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-09-17 DOI: 10.1007/s00180-024-01542-w

Roy Cerqueti, Raffaele Mattera, Valerio Ficcadenti

引用次数: 0

Bayesian adaptive lasso quantile regression with non-ignorable missing responses 具有不可忽略的缺失响应的贝叶斯自适应套索量化回归

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-09-16 DOI: 10.1007/s00180-024-01546-6

Ranran Chen, Mai Dao, Keying Ye, Min Wang

引用次数: 0

Statistical visualisation of tidy and geospatial data in R via kernel smoothing methods in the eks package 通过eks软件包中的核平滑方法在R语言中实现整洁数据和地理空间数据的统计可视化

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-09-14 DOI: 10.1007/s00180-024-01543-9

Tarn Duong

引用次数: 0

Using the Krylov subspace formulation to improve regularisation and interpretation in partial least squares regression 利用克雷洛夫子空间公式改进偏最小二乘回归中的正则化和解释能力

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-09-12 DOI: 10.1007/s00180-024-01545-7

Tommy Löfstedt

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引用次数: 0

Robust matrix factor analysis method with adaptive parameter adjustment using Cauchy weighting 利用考奇加权进行自适应参数调整的稳健矩阵因子分析方法

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-09-12 DOI: 10.1007/s00180-024-01548-4

Junchen Li

{"title":"Robust matrix factor analysis method with adaptive parameter adjustment using Cauchy weighting","authors":"Junchen Li","doi":"10.1007/s00180-024-01548-4","DOIUrl":"https://doi.org/10.1007/s00180-024-01548-4","url":null,"abstract":"<p>In recent years, high-dimensional matrix factor models have been widely applied in various fields. However, there are few methods that effectively handle heavy-tailed data. To address this problem, we introduced a smooth Cauchy loss function and established an optimization objective through norm minimization, deriving a Cauchy version of the weighted iterative estimation method. Unlike the Huber loss weighted estimation method, the weight calculation in this method is a smooth function rather than a piecewise function. It also considers the need to update parameters in the Cauchy loss function with each iteration during estimation. Ultimately, we propose a weighted estimation method with adaptive parameter adjustment. Subsequently, this paper analyzes the theoretical properties of the method, proving that it has a fast convergence rate. Through data simulation, our method demonstrates significant advantages. Thus, it can serve as a better alternative to other existing estimation methods. Finally, we analyzed a dataset of regional population movements between cities, demonstrating that our proposed method offers estimations with excellent interpretability compared to other methods.</p>","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 precise and efficient exceedance-set algorithm for detecting environmental extremes 用于检测极端环境的精确高效超限集算法

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-09-06 DOI: 10.1007/s00180-024-01540-y

Thomas Suesse, Alexander Brenning

引用次数: 0

Change point estimation for Gaussian time series data with copula-based Markov chain models 利用基于共轭的马尔科夫链模型对高斯时间序列数据进行变化点估计

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-09-05 DOI: 10.1007/s00180-024-01541-x

Li-Hsien Sun, Yu-Kai Wang, Lien-Hsi Liu, Takeshi Emura, Chi-Yang Chiu

引用次数: 0

INet for network integration 用于网络集成的 INet

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-09-04 DOI: 10.1007/s00180-024-01536-8

Valeria Policastro, Matteo Magnani, Claudia Angelini, Annamaria Carissimo

引用次数: 0