Advances in Data Analysis and Classification最新文献_第7页

On some properties of Cronbach’s α coefficient for interval-valued data in questionnaires 关于调查问卷中区间值数据的克朗巴赫 α 系数的一些特性

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-07-26 DOI: 10.1007/s11634-024-00601-w

José García-García, María Ángeles Gil, María Asunción Lubiano

{"title":"On some properties of Cronbach’s α coefficient for interval-valued data in questionnaires","authors":"José García-García, María Ángeles Gil, María Asunción Lubiano","doi":"10.1007/s11634-024-00601-w","DOIUrl":"10.1007/s11634-024-00601-w","url":null,"abstract":"<div><p>Along recent years, interval-valued rating scales have been considered as an alternative to traditional single-point psychometric tools for human evaluations, such as Likert-type or visual analogue scales. More concretely, in answering to intrinsically imprecise items in a questionnaire, interval-valued scales seem to allow capturing a richer information than conventional ones. When analyzing data from given performances of questionnaires, one of the main targets is that of ensuring the internal consistency of the items in a construct or latent variable. The most popular indicator of internal consistency, whenever answers to items are given in accordance with a numerically based/encoded scale, is the well-known Cronbach <i> α</i> coefficient. This paper aims to extend such a coefficient to the case of interval-valued answers and to analyze some of its main statistical properties. For this purpose, after presenting some formal preliminaries for interval-valued data, firstly Cronbach’s <i> α</i> coefficient is extended to the case in which the constructs of a questionnaire allow interval-valued answers to their items. The range of the potential values of the extended coefficient is then discussed. Furthermore, the asymptotic distribution of the sample Cronbach <i> α</i> coefficient along with its bias and consistency properties, are examined from a theoretical perspective. Finally, the preceding asymptotic distribution of the sample coefficient as well as the influence of the number of respondents to the questionnaire and the number of items in the constructs are empirically illustrated through simulation-based studies.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"19 3","pages":"831 - 854"},"PeriodicalIF":1.3,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11634-024-00601-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141770279","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

Scalable Bayesian p-generalized probit and logistic regression 可扩展的贝叶斯 p 广义概率和逻辑回归

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-07-04 DOI: 10.1007/s11634-024-00599-1

Zeyu Ding, Simon Omlor, Katja Ickstadt, Alexander Munteanu

{"title":"Scalable Bayesian p-generalized probit and logistic regression","authors":"Zeyu Ding, Simon Omlor, Katja Ickstadt, Alexander Munteanu","doi":"10.1007/s11634-024-00599-1","DOIUrl":"10.1007/s11634-024-00599-1","url":null,"abstract":"<div><p>The logit and probit link functions are arguably the two most common choices for binary regression models. Many studies have extended the choice of link functions to avoid possible misspecification and to improve the model fit to the data. We introduce the <i>p</i>-generalized Gaussian distribution (<i>p</i>-GGD) to binary regression in a Bayesian framework. The <i>p</i>-GGD has received considerable attention due to its flexibility in modeling the tails, while generalizing, for instance, over the standard normal distribution where <span>(p=2)</span> or the Laplace distribution where <span>(p=1)</span>. Here, we extend from maximum likelihood estimation (MLE) to Bayesian posterior estimation using Markov Chain Monte Carlo (MCMC) sampling for the model parameters <span>(beta)</span> and the link function parameter <i>p</i>. We use simulated and real-world data to verify the effect of different parameters <i>p</i> on the estimation results, and how logistic regression and probit regression can be incorporated into a broader framework. To make our Bayesian methods scalable in the case of large data, we also incorporate coresets to reduce the data before running the complex and time-consuming MCMC analysis. This allows us to perform very efficient calculations while retaining the original posterior parameter distributions up to little distortions both, in practice, and with theoretical guarantees.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"20 :","pages":"109 - 143"},"PeriodicalIF":1.3,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11634-024-00599-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141546666","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

Dirichlet compound negative multinomial mixture models and applications Dirichlet 复合负多叉混合物模型及其应用

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-06-25 DOI: 10.1007/s11634-024-00598-2

Ornela Bregu, Nizar Bouguila

{"title":"Dirichlet compound negative multinomial mixture models and applications","authors":"Ornela Bregu, Nizar Bouguila","doi":"10.1007/s11634-024-00598-2","DOIUrl":"10.1007/s11634-024-00598-2","url":null,"abstract":"<div><p>In this paper, we consider an alternative parametrization of Dirichlet Compound Negative Multinomial (DCNM) using rising polynomials. The new parametrization gets rid of Gamma functions and allows us to derive the Exact Fisher Information Matrix, which brings significant improvements to model performance due to feature correlation consideration. Second, we propose to improve the computation efficiency by approximating the DCNM model as a member of the exponential family of distributions, called EDCNM. The novel EDCNM model brings several advantages as compared to the DCNM model, such as a closed-form solution for maximum likelihood estimation, higher efficiency due to computational time reduction for sparse datasets, etc. Third, we implement Agglomerative Hierarchical clustering, where Kullback–Leibler divergence is derived and used to measure the distance between two EDCNM probability distributions. Finally, we integrate the Minimum Message Length criterion in our algorithm to estimate the optimal number of components of the mixture model. The merits of our proposed models are validated via challenging real-world applications in Natural Language Processing and Image/Video Recognition. Results reveal that the exponential approximation of the DCNM model has reduced significantly the computational complexity in high-dimensional feature spaces.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"19 3","pages":"795 - 830"},"PeriodicalIF":1.3,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141506099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Natural language processing and financial markets: semi-supervised modelling of coronavirus and economic news 自然语言处理和金融市场：冠状病毒和经济新闻的半监督建模

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-06-19 DOI: 10.1007/s11634-024-00596-4

Carlos Moreno-Pérez, Marco Minozzo

{"title":"Natural language processing and financial markets: semi-supervised modelling of coronavirus and economic news","authors":"Carlos Moreno-Pérez, Marco Minozzo","doi":"10.1007/s11634-024-00596-4","DOIUrl":"10.1007/s11634-024-00596-4","url":null,"abstract":"<div><p>This paper investigates the reactions of US financial markets to press news from January 2019 to 1 May 2020. To this end, we deduce the content and uncertainty of the news by developing apposite indices from the headlines and snippets of The New York Times, using unsupervised machine learning techniques. In particular, we use Latent Dirichlet Allocation to infer the content (topics) of the articles, and Word Embedding (implemented with the Skip-gram model) and K-Means to measure their uncertainty. In this way, we arrive at the definition of a set of daily topic-specific uncertainty indices. These indices are then used to find explanations for the behavior of the US financial markets by implementing a batch of EGARCH models. In substance, we find that two topic-specific uncertainty indices, one related to COVID-19 news and the other to trade war news, explain the bulk of the movements in the financial markets from the beginning of 2019 to end-April 2020. Moreover, we find that the topic-specific uncertainty index related to the economy and the Federal Reserve is positively related to the financial markets, meaning that our index is able to capture the actions of the Federal Reserve during periods of uncertainty.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"19 3","pages":"769 - 793"},"PeriodicalIF":1.3,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11634-024-00596-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141506100","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 2 of volume 18 (2024) ADAC 第 18 卷（2024 年）第 2 期社论

IF 1.4 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-06-10 DOI: 10.1007/s11634-024-00597-3

Maurizio Vichi, Andrea Cerioli, Hans A. Kestler, Akinori Okada, Claus Weihs

引用次数: 0

Clustering large mixed-type data with ordinal variables 使用顺序变量对大型混合型数据进行聚类

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-05-27 DOI: 10.1007/s11634-024-00595-5

Gero Szepannek, Rabea Aschenbruck, Adalbert Wilhelm

引用次数: 0

A two-group canonical variate analysis biplot for an optimal display of both means and cases 两组典型变量分析双线图，优化显示均值和情况

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-05-06 DOI: 10.1007/s11634-024-00593-7

Niel le Roux, Sugnet Gardner-Lubbe

{"title":"A two-group canonical variate analysis biplot for an optimal display of both means and cases","authors":"Niel le Roux, Sugnet Gardner-Lubbe","doi":"10.1007/s11634-024-00593-7","DOIUrl":"10.1007/s11634-024-00593-7","url":null,"abstract":"<div><p>Canonical variate analysis (CVA) entails a two-sided eigenvalue decomposition. When the number of groups, <i>J</i>, is less than the number of variables, <i>p</i>, at most <span>(J-1)</span> eigenvalues are not exactly zero. A CVA biplot is the simultaneous display of the two entities: group means as points and variables as calibrated biplot axes. It follows that with two groups the group means can be exactly represented in a one-dimensional biplot but the individual samples are approximated. We define a criterion to measure the quality of representing the individual samples in a CVA biplot. Then, for the two-group case we propose an additional dimension for constructing an optimal two-dimensional CVA biplot. The proposed novel CVA biplot maintains the exact display of group means and biplot axes, but the individual sample points satisfy the optimality criterion in a unique simultaneous display of group means, calibrated biplot axes for the variables, and within group samples. Although our primary aim is to address two-group CVA, our proposal extends immediately to an optimal three-dimensional biplot when encountering the equally important case of comparing three groups in practice.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"19 3","pages":"721 - 748"},"PeriodicalIF":1.3,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11634-024-00593-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140888158","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 functional data via variational inference 通过变异推理对功能数据进行聚类

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-04-30 DOI: 10.1007/s11634-024-00590-w

Chengqian Xian, Camila P. E. de Souza, John Jewell, Ronaldo Dias

引用次数: 0

Liszt’s Étude S.136 no.1: audio data analysis of two different piano recordings 李斯特的 Étude S.136 no.1：两种不同钢琴录音的音频数据分析

IF 1.4 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-04-26 DOI: 10.1007/s11634-024-00594-6

Matteo Farnè

{"title":"Liszt’s Étude S.136 no.1: audio data analysis of two different piano recordings","authors":"Matteo Farnè","doi":"10.1007/s11634-024-00594-6","DOIUrl":"10.1007/s11634-024-00594-6","url":null,"abstract":"<div><p>In this paper, we review the main signal processing tools of Music Information Retrieval (MIR) from audio data, and we apply them to two recordings (by Leslie Howard and Thomas Rajna) of Franz Liszt’s Étude S.136 no.1, with the aim of uncovering the macro-formal structure and comparing the interpretative styles of the two performers. In particular, after a thorough spectrogram analysis, we perform a segmentation based on the degree of novelty, in the sense of spectral dissimilarity, calculated frame-by-frame via the cosine distance. We then compare the metrical, temporal and timbrical features of the two executions by MIR tools. Via this method, we are able to identify in a data-driven way the different moments of the piece according to their melodic and harmonic content, and to find out that Rajna’s execution is faster and less various, in terms of intensity and timbre, than Howard’s one. This enquiry represents a case study able to show the potentialities of MIR from audio data in supporting traditional music score analyses and in providing objective information for statistically founded musical execution analyses.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"18 3","pages":"797 - 822"},"PeriodicalIF":1.4,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11634-024-00594-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140801727","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

Comparison of internal evaluation criteria in hierarchical clustering of categorical data 分类数据分层聚类的内部评价标准比较

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-04-13 DOI: 10.1007/s11634-024-00592-8

Zdenek Sulc, Jaroslav Hornicek, Hana Rezankova, Jana Cibulkova

{"title":"Comparison of internal evaluation criteria in hierarchical clustering of categorical data","authors":"Zdenek Sulc, Jaroslav Hornicek, Hana Rezankova, Jana Cibulkova","doi":"10.1007/s11634-024-00592-8","DOIUrl":"10.1007/s11634-024-00592-8","url":null,"abstract":"<div><p>The paper discusses eleven internal evaluation criteria that can be used in the area of hierarchical clustering of categorical data. The criteria are divided into two distinct groups based on how they treat the cluster quality: variability- and distance-based. The paper follows three main aims. The first one is to compare the examined criteria regarding their mutual similarity and dependence on the clustered datasets’ properties and the used similarity measures. The second one is to analyze the relationships between internal and external cluster evaluation to determine how well the internal criteria can recognize the original number of clusters in datasets and to what extent they provide comparable results to the external criteria. The third aim is to propose two new variability-based internal evaluation criteria. In the experiment, 81 types of generated datasets with controlled properties are used. The results show which internal criteria can be recommended for specific tasks, such as judging the cluster quality or the optimal number of clusters determination.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"19 3","pages":"619 - 648"},"PeriodicalIF":1.3,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140589022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0