Advances in Data Analysis and Classification最新文献

Editorial for ADAC issue 3 of volume 19 (2025) ADAC第19卷第3期（2025）社论

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2025-09-05 DOI: 10.1007/s11634-025-00652-7

Maurizio Vichi, Andrea Cerioli, Hans A. Kestler

引用次数: 0

Increasing biases can be more efficient than increasing weights 增加偏差可能比增加权重更有效

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2025-06-16 DOI: 10.1007/s11634-025-00649-2

Carlo Metta, Marco Fantozzi, Andrea Papini, Gianluca Amato, Matteo Bergamaschi, Andrea Fois, Silvia Giulia Galfrè, Alessandro Marchetti, Michelangelo Vegliò, Maurizio Parton, Francesco Morandin

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Special issue on “Advances in clustering, classification and related methods” 专题“聚类、分类及相关方法的进展”

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2025-05-29 DOI: 10.1007/s11634-025-00645-6

Paolo Giordani, Christian Hennig, Julien Jacques, Carla Rampichini

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Variational inference for estimating dynamic stochastic block models through an evolutionary algorithm 用进化算法估计动态随机块模型的变分推理

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2025-05-23 DOI: 10.1007/s11634-025-00634-9

Luca Brusa, Fulvia Pennoni

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Comparing flexible modelling approaches: the varying-thresholds model versus quantile regression 比较灵活的建模方法：变阈值模型与分位数回归

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2025-05-14 DOI: 10.1007/s11634-025-00635-8

Niccolò Ducci, Leonardo Grilli, Marta Pittavino

{"title":"Comparing flexible modelling approaches: the varying-thresholds model versus quantile regression","authors":"Niccolò Ducci, Leonardo Grilli, Marta Pittavino","doi":"10.1007/s11634-025-00635-8","DOIUrl":"10.1007/s11634-025-00635-8","url":null,"abstract":"<div><p>The varying-thresholds model (VTM) is a novel methodology proposed by Tutz ( Flexible predictive distributions from varying-thresholds modelling. https://doi.org/10.48550/arXiv.2103.13324, arXiv:2103.13324 2021) capable of estimating the whole conditional distribution of a response variable in a regression setting. It can be used for continuous, ordinal and count responses. In this study, conditional quantiles and prediction intervals estimated through VTM are compared with those of quantile regression. The comparison is based on a set of data-generating models to assess the performance of the two methodologies regarding the coverage and width of prediction intervals. The simulation study encompasses settings with several functional forms and types of errors. In addition, a discrete version of the continuous ranked probability score is proposed as a tool to choose the best link function for the binary models used in the fitting of VTM. In summary, the varying-thresholds model is a flexible methodology that can be broadly applied with light assumptions; it is advantageous over quantile regression when the conditional quantile function is misspecified.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"19 classification and related methods”","pages":"493 - 514"},"PeriodicalIF":1.3,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11634-025-00635-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145165120","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: 0

When non-response makes estimates from a census a small area estimation problem: the case of the survey on graduates’ employment status in Italy 当非回应从人口普查中进行估计时，一个小区域估计问题：以意大利毕业生就业状况调查为例

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2025-04-10 DOI: 10.1007/s11634-025-00630-z

Maria Giovanna Ranalli, Fulvia Pennoni, Francesco Bartolucci, Antonietta Mira

{"title":"When non-response makes estimates from a census a small area estimation problem: the case of the survey on graduates’ employment status in Italy","authors":"Maria Giovanna Ranalli, Fulvia Pennoni, Francesco Bartolucci, Antonietta Mira","doi":"10.1007/s11634-025-00630-z","DOIUrl":"10.1007/s11634-025-00630-z","url":null,"abstract":"<div><p>Since 1998, AlmaLaurea—a consortium of 80 Italian universities and a member of the Italian National Statistical System—has conducted an annual census on graduates’ employment status. The survey provides estimates of descriptive indicators at both the population level and for specific subpopulations (domains) of interest, such as degree programmes. Some domains have very few observations due to a small population size and non-response. In this paper, we address this estimation problem within a Small Area Estimation framework. Specifically, we propose using generalized linear mixed models that incorporate two variables as proxies for graduates’ response propensity, making the assumption of non-informative non-response more plausible. Degree programme estimates of employment rates are derived as (semi-parametric) empirical best predictions using a finite mixture of logistic regression models, with their mean squared error estimated via a second-order, bias-corrected, analytical estimator. Sensitivity analysis is conducted to assess the explanatory power of variables modelling response propensity and to evaluate potential correlations between area-specific random effects and observed heterogeneity.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"19 classification and related methods”","pages":"515 - 543"},"PeriodicalIF":1.3,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11634-025-00630-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145163429","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: 0

Editorial for ADAC issue 1 of volume 19 (2025) ADAC第19卷第1期（2025）社论

IF 1.4 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2025-02-28 DOI: 10.1007/s11634-025-00629-6

Maurizio Vichi, Andrea Cerioli, Hans A. Kestler

引用次数: 0

Random models for adjusting fuzzy rand index extensions 调整模糊指数扩展的随机模型

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2025-02-13 DOI: 10.1007/s11634-025-00625-w

Ryan DeWolfe, Jeffrey L. Andrews

引用次数: 0

A consensus-constrained parsimonious Gaussian mixture model for clustering hyperspectral images 高光谱图像聚类的共识约束简约高斯混合模型

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2025-02-06 DOI: 10.1007/s11634-025-00623-y

Ganesh Babu, Aoife Gowen, Michael Fop, Isobel Claire Gormley

{"title":"A consensus-constrained parsimonious Gaussian mixture model for clustering hyperspectral images","authors":"Ganesh Babu, Aoife Gowen, Michael Fop, Isobel Claire Gormley","doi":"10.1007/s11634-025-00623-y","DOIUrl":"10.1007/s11634-025-00623-y","url":null,"abstract":"<div><p>The use of hyperspectral imaging to investigate food samples has grown due to the improved performance and lower cost of instrumentation. Food engineers use hyperspectral images to classify the type and quality of a food sample, typically using classification methods. In order to train these methods, every pixel in each training image needs to be labelled. Typically, computationally cheap threshold-based approaches are used to label the pixels, and classification methods are trained based on those labels. However, threshold-based approaches are subjective and cannot be generalized across hyperspectral images taken in different conditions and of different foods. Here a consensus-constrained parsimonious Gaussian mixture model (ccPGMM) is proposed to label pixels in hyperspectral images using a model-based clustering approach. The ccPGMM utilizes information that is available on some pixels and specifies constraints on those pixels belonging to the same or different clusters while clustering the rest of the pixels in the image. A latent variable model is used to represent the high-dimensional data in terms of a small number of underlying latent factors. To ensure computational feasibility, a consensus clustering approach is employed, where the data are divided into multiple randomly selected subsets of variables and constrained clustering is applied to each data subset; the clustering results are then consolidated across all data subsets to provide a consensus clustering solution. The ccPGMM approach is applied to simulated datasets and real hyperspectral images of three types of puffed cereal, corn, rice, and wheat. Improved clustering performance and computational efficiency are demonstrated when compared to other current state-of-the-art approaches.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"19 classification and related methods”","pages":"323 - 359"},"PeriodicalIF":1.3,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11634-025-00623-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145162774","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: 0

Clustering and classification of spatio-temporal data using spatial dynamic panel data models 利用空间动态面板数据模型对时空数据进行聚类和分类

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-12-23 DOI: 10.1007/s11634-024-00620-7

Giuseppe Feo, Francesco Giordano, Sara Milito, Marcella Niglio, Maria Lucia Parrella

引用次数: 0