Journal of Classification最新文献_第10页

On Finite Mixture Modeling of Change-point Processes 变点过程的有限混合建模

IF 2 4区计算机科学

Journal of Classification Pub Date : 2021-05-10 DOI: 10.1007/s00357-021-09385-6

Xuwen Zhu, Yana Melnykov

引用次数: 5

Matrix Normal Cluster-Weighted Models 矩阵正规聚类加权模型

IF 2 4区计算机科学

Journal of Classification Pub Date : 2021-04-24 DOI: 10.1007/s00357-021-09389-2

S. Tomarchio, P. McNicholas, A. Punzo

引用次数: 11

A Comparison of Reliability Coefficients for Ordinal Rating Scales 普通评定量表可靠性系数的比较

IF 2 4区计算机科学

Journal of Classification Pub Date : 2021-04-22 DOI: 10.1007/s00357-021-09386-5

Alexandra de Raadt, M. Warrens, R. Bosker, H. Kiers

引用次数: 28

A Unified Theory of the Completeness of Q-Matrices for the DINA Model DINA模型Q矩阵完备性的统一理论

IF 2 4区计算机科学

Journal of Classification Pub Date : 2021-04-01 DOI: 10.1007/s00357-021-09384-7

Hans-Friedrich Köhn, Chia-Yi Chiu

引用次数: 1

Ordinal Trees and Random Forests: Score-Free Recursive Partitioning and Improved Ensembles 有序树和随机森林:无分数递归划分和改进集成

IF 2 4区计算机科学

Journal of Classification Pub Date : 2021-01-31 DOI: 10.1007/s00357-021-09406-4

G. Tutz

引用次数: 4

Editorial: Journal of Classification Vol. 38-3. 社论:分类杂志Vol. 38-3。

IF 2 4区计算机科学

Journal of Classification Pub Date : 2021-01-01 Epub Date: 2021-12-11 DOI: 10.1007/s00357-021-09404-6

Paul D McNicholas

引用次数: 0

Explicit Agreement Extremes for a 2 × 2 Table with Given Marginals 具有给定边际的2 × 2表的显合极值

IF 2 4区计算机科学

Journal of Classification Pub Date : 2021-01-01 DOI: 10.1007/s00357-020-09375-0

José Enrique Chacón Durán

引用次数: 0

Co-clustering of Time-Dependent Data via the Shape Invariant Model. 基于形状不变模型的时变数据共聚类。

IF 2 4区计算机科学

Journal of Classification Pub Date : 2021-01-01 Epub Date: 2021-10-06 DOI: 10.1007/s00357-021-09402-8

Alessandro Casa, Charles Bouveyron, Elena Erosheva, Giovanna Menardi

{"title":"Co-clustering of Time-Dependent Data via the Shape Invariant Model.","authors":"Alessandro Casa, Charles Bouveyron, Elena Erosheva, Giovanna Menardi","doi":"10.1007/s00357-021-09402-8","DOIUrl":"https://doi.org/10.1007/s00357-021-09402-8","url":null,"abstract":"Multivariate time-dependent data, where multiple features are observed over time for a set of individuals, are increasingly widespread in many application domains. To model these data, we need to account for relations among both time instants and variables and, at the same time, for subject heterogeneity. We propose a new co-clustering methodology for grouping individuals and variables simultaneously, designed to handle both functional and longitudinal data. Our approach borrows some concepts from the curve registration framework by embedding the shape invariant model in the latent block model, estimated via a suitable modification of the SEM-Gibbs algorithm. The resulting procedure allows for several user-defined specifications of the notion of cluster that can be chosen on substantive grounds and provides parsimonious summaries of complex time-dependent data by partitioning data matrices into homogeneous blocks. Along with the explicit modelling of time evolution, these aspects allow for an easy interpretation of the clusters, from which also low-dimensional settings may benefit.","PeriodicalId":50241,"journal":{"name":"Journal of Classification","volume":"38 3","pages":"626-649"},"PeriodicalIF":2.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8494170/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39514866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

A Gibbs Sampling Algorithm with Monotonicity Constraints for Diagnostic Classification Models 诊断分类模型的单调性约束Gibbs抽样算法

IF 2 4区计算机科学

Journal of Classification Pub Date : 2020-10-06 DOI: 10.31234/osf.io/undcv

K. Yamaguchi, J. Templin

引用次数: 6

ROC and AUC with a Binary Predictor: a Potentially Misleading Metric. 二元预测器的ROC和AUC:一个潜在的误导性度量。

IF 2 4区计算机科学

Journal of Classification Pub Date : 2020-10-01 Epub Date: 2019-12-23 DOI: 10.1007/s00357-019-09345-1

John Muschelli

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引用次数: 103