Advances in Data Analysis and Classification最新文献_第4页

Modelling time-inhomogeneous incomplete records of point processes using variants of hidden Markov models 利用隐马尔可夫模型的变体对点过程的时间非齐次不完全记录进行建模

IF 2.1 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2025-04-23 DOI: 10.1007/s11634-025-00632-x

Amina Shahzadi, Ting Wang, Matthew Parry, Mark Bebbington

{"title":"Modelling time-inhomogeneous incomplete records of point processes using variants of hidden Markov models","authors":"Amina Shahzadi, Ting Wang, Matthew Parry, Mark Bebbington","doi":"10.1007/s11634-025-00632-x","DOIUrl":"10.1007/s11634-025-00632-x","url":null,"abstract":"<div><p>Many point processes such as earthquakes or volcanic eruptions have incomplete records with the degree of incompleteness varying over time. For these point processes, the number of missing events between each pair of consecutively observed events can be a random variable that may depend on time, effecting the estimation of parameters or hazard. Such incomplete point processes can be modelled by compound renewal processes where the sum of renewal processes is a random variable because of random variable number of missing events. We propose shifted compound Poisson-Gamma and time-dependent shifted compound Poisson-Gamma renewal processes. Since the number of missing events can be regarded as an unobserved process, the proposed renewal processes are introduced to use in the framework of different types of homogeneous and inhomogeneous hidden Markov models to model the time-dependent variable number of missing events between each pair of consecutively observed events of incomplete point processes. Simulation experiments are employed to check the performance of proposed renewal processes with hidden Markov models. We apply the proposed models to the large magnitude explosive volcanic eruptions database to analyze the time-dependent incompleteness and demonstrate how we estimate the completeness of the record and the future hazard rate.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"20 2","pages":"663 - 691"},"PeriodicalIF":2.1,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11634-025-00632-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148314724","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

Statistical jump model for mixed-type data with missing data imputation 缺失数据输入的混合类型数据统计跳跃模型

IF 2.1 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2025-03-25 DOI: 10.1007/s11634-025-00631-y

Federico P. Cortese, Antonio Pievatolo

引用次数: 0

Parametric models for distributional data 分布数据的参数化模型

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2025-03-10 DOI: 10.1007/s11634-025-00624-x

Paula Brito, A. Pedro Duarte Silva

引用次数: 0

A low-rank non-convex norm method for multiview graph clustering 多视图图聚类的低秩非凸范数方法

IF 2.1 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2025-03-03 DOI: 10.1007/s11634-025-00628-7

Alaeddine Zahir, Khalide Jbilou, Ahmed Ratnani

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

Weighted embedding and outlier detection of metric space data 度量空间数据的加权嵌入与离群点检测

IF 2.1 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2025-02-27 DOI: 10.1007/s11634-025-00627-8

Lauri Heinonen, Henri Nyberg, Joni Virta

引用次数: 0

Particle swarm optimization for preference rankings 偏好排序的粒子群优化

IF 2.1 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2025-02-17 DOI: 10.1007/s11634-025-00626-9

Maurizio Romano, Claudio Conversano, Roberta Siciliano, Antonio D’Ambrosio

{"title":"Particle swarm optimization for preference rankings","authors":"Maurizio Romano, Claudio Conversano, Roberta Siciliano, Antonio D’Ambrosio","doi":"10.1007/s11634-025-00626-9","DOIUrl":"10.1007/s11634-025-00626-9","url":null,"abstract":"<div><p>Preference learning, or the analysis of preference rankings, is gaining more and more importance in various scientific disciplines. Preference learning methods allow predicting preferences on a set of alternatives. The ingredients are a pool of evaluators and a set of objects or items to be ranked in order of preference. The rank aggregation problem must be solved in order to aggregate preferences or rankings with the aim to find a consensus or collective decision. Branch-and-bound-like procedures are usable up to problems involving a relatively small number of objects, say less than 200. When the number of items becomes very large, the rank aggregation problem becomes increasingly difficult to approach so that it is universally recognized as an NP-hard problem. Several heuristic methods have been proposed to provide increasingly accurate solutions. These assume the Kemeny axiomatic approach that better deals with tied rankings. In this paper, we adopt a strategy based on Particle Swarm Optimization by adapting procedures born to solve optimization problems in continuous spaces to discrete combinatorial optimization problems. A simulation study shows the performance of the proposed algorithm in a controlled environment. A benchmarking complex data set and two real world data sets with large number of items are considered. As a result, the proposed algorithm provides significant savings in computational time and comparable accuracy with respect to other recent algorithms.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"20 2","pages":"551 - 575"},"PeriodicalIF":2.1,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11634-025-00626-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148314747","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

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