Computational Statistics最新文献_第2页

Generalized linear model based on latent factors and supervised components 基于潜在因素和监督成分的广义线性模型

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-09-02 DOI: 10.1007/s00180-024-01544-8

Julien Gibaud, Xavier Bry, Catherine Trottier

{"title":"Generalized linear model based on latent factors and supervised components","authors":"Julien Gibaud, Xavier Bry, Catherine Trottier","doi":"10.1007/s00180-024-01544-8","DOIUrl":"https://doi.org/10.1007/s00180-024-01544-8","url":null,"abstract":"In a context of component-based multivariate modeling we propose to model the residual dependence of the responses. Each response of a response vector is assumed to depend, through a Generalized Linear Model, on a set of explanatory variables. The vast majority of explanatory variables are partitioned into conceptually homogeneous variable groups, viewed as explanatory themes. Variables in themes are supposed many and some of them are highly correlated or even collinear. Thus, generalized linear regression demands dimension reduction and regularization with respect to each theme. Besides them, we consider a small set of “additional” covariates not conceptually linked to the themes, and demanding no regularization. Supervised Component Generalized Linear Regression proposed to both regularize and reduce the dimension of the explanatory space by searching each theme for an appropriate number of orthogonal components, which both contribute to predict the responses and capture relevant structural information in themes. In this paper, we introduce random latent variables (a.k.a. factors) so as to model the covariance matrix of the linear predictors of the responses conditional on the components. To estimate the model, we present an algorithm combining supervised component-based model estimation with factor model estimation. This methodology is tested on simulated data and then applied to an agricultural ecology dataset.","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186116","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

SpICE: an interpretable method for spatial data SpICE：空间数据的可解释方法

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-08-26 DOI: 10.1007/s00180-024-01538-6

Natalia da Silva, Ignacio Alvarez-Castro, Leonardo Moreno, Andrés Sosa

{"title":"SpICE: an interpretable method for spatial data","authors":"Natalia da Silva, Ignacio Alvarez-Castro, Leonardo Moreno, Andrés Sosa","doi":"10.1007/s00180-024-01538-6","DOIUrl":"https://doi.org/10.1007/s00180-024-01538-6","url":null,"abstract":"Statistical learning methods are widely utilised in tackling complex problems due to their flexibility, good predictive performance and ability to capture complex relationships among variables. Additionally, recently developed automatic workflows have provided a standardised approach for implementing statistical learning methods across various applications. However, these tools highlight one of the main drawbacks of statistical learning: the lack of interpretability of the results. In the past few years, a large amount of research has been focused on methods for interpreting black box models. Having interpretable statistical learning methods is necessary for obtaining a deeper understanding of these models. Specifically in problems in which spatial information is relevant, combining interpretable methods with spatial data can help to provide a better understanding of the problem and an improved interpretation of the results. This paper is focused on the individual conditional expectation plot (ICE-plot), a model-agnostic method for interpreting statistical learning models and combining them with spatial information. An ICE-plot extension is proposed in which spatial information is used as a restriction to define spatial ICE (SpICE) curves. Spatial ICE curves are estimated using real data in the context of an economic problem concerning property valuation in Montevideo, Uruguay. Understanding the key factors that influence property valuation is essential for decision-making, and spatial data play a relevant role in this regard.","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186117","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

Performance of evaluation metrics for classification in imbalanced data 不平衡数据分类评价指标的性能

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-08-24 DOI: 10.1007/s00180-024-01539-5

Alex de la Cruz Huayanay, Jorge L. Bazán, Cibele M. Russo

引用次数: 0

A theory of contrasts for modified Freeman–Tukey statistics and its applications to Tukey’s post-hoc tests for contingency tables 修正弗里曼-图基统计的对比理论及其在或然表图基事后检验中的应用

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-08-17 DOI: 10.1007/s00180-024-01537-7

Yoshio Takane, Eric J. Beh, Rosaria Lombardo

引用次数: 0

A novel nonconvex, smooth-at-origin penalty for statistical learning 用于统计学习的新型非凸、平滑原点罚则

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-08-07 DOI: 10.1007/s00180-024-01525-x

Majnu John, Sujit Vettam, Yihren Wu

引用次数: 0

Quantinar: a blockchain peer-to-peer ecosystem for modern data analytics Quantinar：用于现代数据分析的区块链点对点生态系统

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-08-06 DOI: 10.1007/s00180-024-01529-7

Raul Bag, Bruno Spilak, Julian Winkel, Wolfgang Karl Härdle

引用次数: 0

BARMPy: Bayesian additive regression models Python package BARMPy：贝叶斯加性回归模型 Python 软件包

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-08-04 DOI: 10.1007/s00180-024-01535-9

Danielle Van Boxel

引用次数: 0

Robust confidence intervals for meta-regression with interaction effects 具有交互效应的元回归的稳健置信区间

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-08-02 DOI: 10.1007/s00180-024-01530-0

Maria Thurow, Thilo Welz, Eric Knop, Tim Friede, Markus Pauly

{"title":"Robust confidence intervals for meta-regression with interaction effects","authors":"Maria Thurow, Thilo Welz, Eric Knop, Tim Friede, Markus Pauly","doi":"10.1007/s00180-024-01530-0","DOIUrl":"https://doi.org/10.1007/s00180-024-01530-0","url":null,"abstract":"Meta-analysis is an important statistical technique for synthesizing the results of multiple studies regarding the same or closely related research question. So-called meta-regression extends meta-analysis models by accounting for study-level covariates. Mixed-effects meta-regression models provide a powerful tool for evidence synthesis, by appropriately accounting for between-study heterogeneity. In fact, modelling the study effect in terms of random effects and moderators not only allows to examine the impact of the moderators, but often leads to more accurate estimates of the involved parameters. Nevertheless, due to the often small number of studies on a specific research topic, interactions are often neglected in meta-regression. In this work we consider the research questions (i) how moderator interactions influence inference in mixed-effects meta-regression models and (ii) whether some inference methods are more reliable than others. Here we review robust methods for confidence intervals in meta-regression models including interaction effects. These methods are based on the application of robust sandwich estimators of Hartung-Knapp-Sidik-Jonkman (HKSJ) or heteroscedasticity-consistent (HC)-type for estimating the variance-covariance matrix of the vector of model coefficients. Furthermore, we compare different versions of these robust estimators in an extensive simulation study. We thereby investigate coverage and width of seven different confidence intervals under varying conditions. Our simulation study shows that the coverage rates as well as the interval widths of the parameter estimates are only slightly affected by adjustment of the parameters. It also turned out that using the Satterthwaite approximation for the degrees of freedom seems to be advantageous for accurate coverage rates. In addition, different to previous analyses for simpler models, the (textbf{HKSJ})-estimator shows a worse performance in this more complex setting compared to some of the (textbf{HC})-estimators.","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141884981","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

Ordinal causal discovery based on Markov blankets 基于马尔可夫毛毯的序数因果发现

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-07-30 DOI: 10.1007/s00180-024-01513-1

Yu Du, Yi Sun, Luyao Tan

引用次数: 0

A Metropolis–Hastings Robbins–Monro algorithm via variational inference for estimating the multidimensional graded response model: a calculationally efficient estimation scheme to deal with complex test structures 通过变分推理估算多维分级响应模型的 Metropolis-Hastings Robbins-Monro 算法：处理复杂测试结构的高效计算估算方案

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-07-29 DOI: 10.1007/s00180-024-01533-x

Xue Wang, Jing Lu, Jiwei Zhang

引用次数: 0