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  Subjects -> STATISTICS (Total: 130 journals)
Showing 1 - 151 of 151 Journals sorted alphabetically
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 52)
Annals of Applied Statistics     Full-text available via subscription   (Followers: 37)
Applied Categorical Structures     Hybrid Journal   (Followers: 5)
Argumentation et analyse du discours     Open Access   (Followers: 7)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 7)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 2)
Australian & New Zealand Journal of Statistics     Hybrid Journal   (Followers: 12)
Biometrical Journal     Hybrid Journal   (Followers: 6)
Biometrics     Hybrid Journal   (Followers: 49)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 19)
Building Simulation     Hybrid Journal   (Followers: 2)
CHANCE     Hybrid Journal   (Followers: 5)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Communications in Statistics - Theory and Methods     Hybrid Journal   (Followers: 10)
Computational Statistics     Hybrid Journal   (Followers: 17)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 38)
Current Research in Biostatistics     Open Access   (Followers: 9)
Decisions in Economics and Finance     Hybrid Journal   (Followers: 15)
Demographic Research     Open Access   (Followers: 14)
Engineering With Computers     Hybrid Journal   (Followers: 5)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
ESAIM: Probability and Statistics     Open Access   (Followers: 4)
Extremes     Hybrid Journal   (Followers: 2)
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 9)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 13)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 3)
Handbook of Statistics     Full-text available via subscription   (Followers: 8)
IEA World Energy Statistics and Balances -     Full-text available via subscription   (Followers: 2)
International Journal of Computational Economics and Econometrics     Hybrid Journal   (Followers: 6)
International Journal of Quality, Statistics, and Reliability     Open Access   (Followers: 19)
International Journal of Stochastic Analysis     Open Access   (Followers: 2)
International Statistical Review     Hybrid Journal   (Followers: 11)
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 3)
Journal of Applied Statistics     Hybrid Journal   (Followers: 20)
Journal of Biopharmaceutical Statistics     Hybrid Journal   (Followers: 17)
Journal of Business & Economic Statistics     Full-text available via subscription   (Followers: 41, SJR: 3.664, CiteScore: 2)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Journal of Computational & Graphical Statistics     Full-text available via subscription   (Followers: 21)
Journal of Econometrics     Hybrid Journal   (Followers: 85)
Journal of Educational and Behavioral Statistics     Hybrid Journal   (Followers: 8)
Journal of Forecasting     Hybrid Journal   (Followers: 21)
Journal of Global Optimization     Hybrid Journal   (Followers: 7)
Journal of Mathematics and Statistics     Open Access   (Followers: 6)
Journal of Nonparametric Statistics     Hybrid Journal   (Followers: 7)
Journal of Probability and Statistics     Open Access   (Followers: 11)
Journal of Risk and Uncertainty     Hybrid Journal   (Followers: 35)
Journal of Statistical and Econometric Methods     Open Access   (Followers: 3)
Journal of Statistical Physics     Hybrid Journal   (Followers: 12)
Journal of Statistical Planning and Inference     Hybrid Journal   (Followers: 8)
Journal of Statistical Software     Open Access   (Followers: 19, SJR: 13.802, CiteScore: 16)
Journal of the American Statistical Association     Full-text available via subscription   (Followers: 77, SJR: 3.746, CiteScore: 2)
Journal of the Korean Statistical Society     Hybrid Journal   (Followers: 1)
Journal of the Royal Statistical Society Series C (Applied Statistics)     Hybrid Journal   (Followers: 37)
Journal of the Royal Statistical Society, Series A (Statistics in Society)     Hybrid Journal   (Followers: 31)
Journal of the Royal Statistical Society, Series B (Statistical Methodology)     Hybrid Journal   (Followers: 43)
Journal of Theoretical Probability     Hybrid Journal   (Followers: 3)
Journal of Time Series Analysis     Hybrid Journal   (Followers: 18)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 28)
Law, Probability and Risk     Hybrid Journal   (Followers: 8)
Lifetime Data Analysis     Hybrid Journal   (Followers: 5)
Mathematical Methods of Statistics     Hybrid Journal   (Followers: 4)
Measurement Interdisciplinary Research and Perspectives     Hybrid Journal   (Followers: 1)
Metrika     Hybrid Journal   (Followers: 4)
Monthly Statistics of International Trade - Statistiques mensuelles du commerce international     Full-text available via subscription   (Followers: 4)
Multivariate Behavioral Research     Hybrid Journal   (Followers: 9)
Optimization Letters     Hybrid Journal   (Followers: 2)
Optimization Methods and Software     Hybrid Journal   (Followers: 5)
Oxford Bulletin of Economics and Statistics     Hybrid Journal   (Followers: 35)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 10)
Queueing Systems     Hybrid Journal   (Followers: 7)
Research Synthesis Methods     Hybrid Journal   (Followers: 8)
Review of Economics and Statistics     Hybrid Journal   (Followers: 281)
Review of Socionetwork Strategies     Hybrid Journal  
Risk Management     Hybrid Journal   (Followers: 16)
Sankhya A     Hybrid Journal   (Followers: 3)
Scandinavian Journal of Statistics     Hybrid Journal   (Followers: 9)
Sequential Analysis: Design Methods and Applications     Hybrid Journal   (Followers: 1)
Significance     Hybrid Journal   (Followers: 6)
Sociological Methods & Research     Hybrid Journal   (Followers: 49)
SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques     Full-text available via subscription  
Stata Journal     Full-text available via subscription   (Followers: 9)
Statistica Neerlandica     Hybrid Journal   (Followers: 1)
Statistical Inference for Stochastic Processes     Hybrid Journal   (Followers: 3)
Statistical Methods and Applications     Hybrid Journal   (Followers: 5)
Statistical Methods in Medical Research     Hybrid Journal   (Followers: 23)
Statistical Modelling     Hybrid Journal   (Followers: 18)
Statistical Papers     Hybrid Journal   (Followers: 4)
Statistics & Probability Letters     Hybrid Journal   (Followers: 13)
Statistics and Computing     Hybrid Journal   (Followers: 14)
Statistics and Economics     Open Access  
Statistics in Medicine     Hybrid Journal   (Followers: 144)
Statistics: A Journal of Theoretical and Applied Statistics     Hybrid Journal   (Followers: 11)
Stochastic Models     Hybrid Journal   (Followers: 2)
Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports     Hybrid Journal   (Followers: 2)
Structural and Multidisciplinary Optimization     Hybrid Journal   (Followers: 12)
Teaching Statistics     Hybrid Journal   (Followers: 8)
Technology Innovations in Statistics Education (TISE)     Open Access   (Followers: 2)
TEST     Hybrid Journal   (Followers: 3)
The American Statistician     Full-text available via subscription   (Followers: 25)
The Canadian Journal of Statistics / La Revue Canadienne de Statistique     Hybrid Journal   (Followers: 10)
Wiley Interdisciplinary Reviews - Computational Statistics     Hybrid Journal   (Followers: 1)

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Similar Journals
Journal Cover
Advances in Data Analysis and Classification
Journal Prestige (SJR): 1.09
Citation Impact (citeScore): 1
Number of Followers: 52  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1862-5355 - ISSN (Online) 1862-5347
Published by Springer-Verlag Homepage  [2468 journals]
  • Editorial for ADAC issue 2 of volume 18 (2024)

    • Free pre-print version: Loading...

      PubDate: 2024-06-10
       
  • Clustering large mixed-type data with ordinal variables

    • Free pre-print version: Loading...

      Abstract: Abstract One of the most frequently used algorithms for clustering data with both numeric and categorical variables is the k-prototypes algorithm, an extension of the well-known k-means clustering. Gower’s distance denotes another popular approach for dealing with mixed-type data and is suitable not only for numeric and categorical but also for ordinal variables. In the paper a modification of the k-prototypes algorithm to Gower’s distance is proposed that ensures convergence. This provides a tool that allows to take into account ordinal information for clustering and can also be used for large data. A simulation study demonstrates convergence, good clustering results as well as small runtimes.
      PubDate: 2024-05-27
       
  • A two-group canonical variate analysis biplot for an optimal display of
           both means and cases

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      Abstract: Abstract Canonical variate analysis (CVA) entails a two-sided eigenvalue decomposition. When the number of groups, J, is less than the number of variables, p, at most \(J-1\) 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.
      PubDate: 2024-05-06
       
  • Clustering functional data via variational inference

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      Abstract: Abstract Among different functional data analyses, clustering analysis aims to determine underlying groups of curves in the dataset when there is no information on the group membership of each curve. In this work, we develop a novel variational Bayes (VB) algorithm for clustering and smoothing functional data simultaneously via a B-spline regression mixture model with random intercepts. We employ the deviance information criterion to select the best number of clusters. The proposed VB algorithm is evaluated and compared with other methods (k-means, functional k-means and two other model-based methods) via a simulation study under various scenarios. We apply our proposed methodology to two publicly available datasets. We demonstrate that the proposed VB algorithm achieves satisfactory clustering performance in both simulation and real data analyses.
      PubDate: 2024-04-30
       
  • Liszt’s Étude S.136 no.1: audio data anaylsis of two different
           piano recordings

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      Abstract: Abstract 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.
      PubDate: 2024-04-26
       
  • Multidimensional scaling for big data

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      Abstract: Abstract We present a set of algorithms implementing multidimensional scaling (MDS) for large data sets. MDS is a family of dimensionality reduction techniques using a \(n \times n\) distance matrix as input, where n is the number of individuals, and producing a low dimensional configuration: a \(n\times r\) matrix with \(r<<n\) . When n is large, MDS is unaffordable with classical MDS algorithms because their extremely large memory and time requirements. We compare six non-standard algorithms intended to overcome these difficulties. They are based on the central idea of partitioning the data set into small pieces, where classical MDS methods can work. Two of these algorithms are original proposals. In order to check the performance of the algorithms as well as to compare them, we have done a simulation study. Additionally, we have used the algorithms to obtain an MDS configuration for EMNIST: a real large data set with more than 800000 points. We conclude that all the algorithms are appropriate to use for obtaining an MDS configuration, but we recommend to use one of our proposals, since it is a fast algorithm with satisfactory statistical properties when working with big data. An R package implementing the algorithms has been created.
      PubDate: 2024-04-13
       
  • Comparison of internal evaluation criteria in hierarchical clustering of
           categorical data

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      Abstract: Abstract 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.
      PubDate: 2024-04-13
       
  • View selection in multi-view stacking: choosing the meta-learner

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      Abstract: Abstract Multi-view stacking is a framework for combining information from different views (i.e. different feature sets) describing the same set of objects. In this framework, a base-learner algorithm is trained on each view separately, and their predictions are then combined by a meta-learner algorithm. In a previous study, stacked penalized logistic regression, a special case of multi-view stacking, has been shown to be useful in identifying which views are most important for prediction. In this article we expand this research by considering seven different algorithms to use as the meta-learner, and evaluating their view selection and classification performance in simulations and two applications on real gene-expression data sets. Our results suggest that if both view selection and classification accuracy are important to the research at hand, then the nonnegative lasso, nonnegative adaptive lasso and nonnegative elastic net are suitable meta-learners. Exactly which among these three is to be preferred depends on the research context. The remaining four meta-learners, namely nonnegative ridge regression, nonnegative forward selection, stability selection and the interpolating predictor, show little advantages in order to be preferred over the other three.
      PubDate: 2024-04-12
       
  • Natural-neighborhood based, label-specific undersampling for imbalanced,
           multi-label data

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      Abstract: Abstract This work presents a novel undersampling scheme to tackle the imbalance problem in multi-label datasets. We use the principles of the natural nearest neighborhood and follow a paradigm of label-specific undersampling. Natural-nearest neighborhood is a parameter-free principle. Our scheme’s novelty lies in exploring the parameter-optimization-free natural nearest neighborhood principles. The class imbalance problem is particularly challenging in a multi-label context, as the imbalance ratio and the majority–minority distributions vary from label to label. Consequently, the majority–minority class overlaps also vary across the labels. Working on this aspect, we propose a framework where a single natural neighbor search is sufficient to identify all the label-specific overlaps. Natural neighbor information is also used to find the key lattices of the majority class (which we do not undersample). The performance of the proposed method, NaNUML, indicates its ability to mitigate the class-imbalance issue in multi-label datasets to a considerable extent. We could also establish a statistically superior performance over other competing methods several times. An empirical study involving twelve real-world multi-label datasets, seven competing methods, and four evaluating metrics—shows that the proposed method effectively handles the class-imbalance issue in multi-label datasets. In this work, we have presented a novel label-specific undersampling scheme, NaNUML, for multi-label datasets. NaNUML is based on the parameter-free natural neighbor search and the key factor, neighborhood size ‘k’ is determined without invoking any parameter optimization.
      PubDate: 2024-03-30
      DOI: 10.1007/s11634-024-00589-3
       
  • Entropy-based fuzzy clustering of interval-valued time series

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      Abstract: Abstract This paper proposes a fuzzy C-medoids-based clustering method with entropy regularization to solve the issue of grouping complex data as interval-valued time series. The dual nature of the data, that are both time-varying and interval-valued, needs to be considered and embedded into clustering techniques. In this work, a new dissimilarity measure, based on Dynamic Time Warping, is proposed. The performance of the new clustering procedure is evaluated through a simulation study and an application to financial time series.
      PubDate: 2024-03-29
      DOI: 10.1007/s11634-024-00586-6
       
  • Clustering ensemble extraction: a knowledge reuse framework

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      Abstract: Abstract Clustering ensemble combines several fundamental clusterings with a consensus function to produce the final clustering without gaining access to data features. The quality and diversity of a vast library of base clusterings influence the performance of the consensus function. When a huge library of various clusterings is not available, this function produces results of lower quality than those of the basic clustering. The expansion of diverse clusters in the collection to increase the performance of consensus, especially in cases where there is no access to specific data features or assumptions in the data distribution, has still remained an open problem. The approach proposed in this paper, Clustering Ensemble Extraction, considers the similarity criterion at the cluster level and places the most similar clusters in the same group. Then, it extracts new clusters with the help of the Extracting Clusters Algorithm. Finally, two new consensus functions, namely Cluster-based extracted partitioning algorithm and Meta-cluster extracted algorithm, are defined and then applied to new clusters in order to create a high-quality clustering. The results of the empirical experiments conducted in this study showed that the new consensus function obtained by our proposed method outperformed the methods previously proposed in the literature regarding the clustering quality and efficiency.
      PubDate: 2024-03-27
      DOI: 10.1007/s11634-024-00588-4
       
  • Mixtures of regressions using matrix-variate heavy-tailed distributions

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      Abstract: Abstract Finite mixtures of regressions (FMRs) are powerful clustering devices used in many regression-type analyses. Unfortunately, real data often present atypical observations that make the commonly adopted normality assumption of the mixture components inadequate. Thus, to robustify the FMR approach in a matrix-variate framework, we introduce ten FMRs based on the matrix-variate t and contaminated normal distributions. Furthermore, once one of our models is estimated and the observations are assigned to the groups, different procedures can be used for the detection of the atypical points in the data. An ECM algorithm is outlined for maximum likelihood parameter estimation. By using simulated data, we show the negative consequences (in terms of parameter estimates and inferred classification) of the wrong normality assumption in the presence of heavy-tailed clusters or noisy matrices. Such issues are properly addressed by our models instead. Additionally, over the same data, the atypical points detection procedures are also investigated. A real-data analysis concerning the relationship between greenhouse gas emissions and their determinants is conducted, and the behavior of our models in the presence of heterogeneity and atypical observations is discussed.
      PubDate: 2024-03-16
      DOI: 10.1007/s11634-024-00585-7
       
  • Clustering by deep latent position model with graph convolutional network

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      Abstract: Abstract With the significant increase of interactions between individuals through numeric means, clustering of nodes in graphs has become a fundamental approach for analyzing large and complex networks. In this work, we propose the deep latent position model (DeepLPM), an end-to-end generative clustering approach which combines the widely used latent position model (LPM) for network analysis with a graph convolutional network encoding strategy. Moreover, an original estimation algorithm is introduced to integrate the explicit optimization of the posterior clustering probabilities via variational inference and the implicit optimization using stochastic gradient descent for graph reconstruction. Numerical experiments on simulated scenarios highlight the ability of DeepLPM to self-penalize the evidence lower bound for selecting the number of clusters, demonstrating its clustering capabilities compared to state-of-the-art methods. Finally, DeepLPM is further applied to an ecclesiastical network in Merovingian Gaul and to a citation network Cora to illustrate the practical interest in exploring large and complex real-world networks.
      PubDate: 2024-03-12
      DOI: 10.1007/s11634-024-00583-9
       
  • Choosing the number of factors in factor analysis with incomplete data via
           a novel hierarchical Bayesian information criterion

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      Abstract: Abstract The Bayesian information criterion (BIC), defined as the observed data log likelihood minus a penalty term based on the sample size N, is a popular model selection criterion for factor analysis with complete data. This definition has also been suggested for incomplete data. However, the penalty term based on the ‘complete’ sample size N is the same no matter whether in a complete or incomplete data case. For incomplete data, there are often only \(N_i<N\) observations for variable i, which means that using the ‘complete’ sample size N implausibly ignores the amounts of missing information inherent in incomplete data. Given this observation, a novel hierarchical BIC (HBIC) criterion is proposed for factor analysis with incomplete data, which is denoted by HBICinc. The novelty is that HBICinc only uses the actual amounts of observed information, namely \(N_i\) ’s, in the penalty term. Theoretically, it is shown that HBICinc is a large sample approximation of variational Bayesian (VB) lower bound, and BIC is a further approximation of HBICinc, which means that HBICinc shares the theoretical consistency of BIC. Experiments on synthetic and real data sets are conducted to access the finite sample performance of HBICinc, BIC, and related criteria with various missing rates. The results show that HBICinc and BIC perform similarly when the missing rate is small, but HBICinc is more accurate when the missing rate is not small.
      PubDate: 2024-03-07
      DOI: 10.1007/s11634-024-00582-w
       
  • Estimators of various kappa coefficients based on the unbiased estimator
           of the expected index of agreements

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      Abstract: Abstract To measure the degree of agreement between R observers who independently classify n subjects within K categories, various kappa-type coefficients are often used. When R = 2, it is common to use the Cohen' kappa, Scott's pi, Gwet’s AC1/2, and Krippendorf's alpha coefficients (weighted or not). When R > 2, some pairwise version based on the aforementioned coefficients is normally used; with the same order as above: Hubert's kappa, Fleiss's kappa, Gwet's AC1/2, and Krippendorf's alpha. However, all these statistics are based on biased estimators of the expected index of agreements, since they estimate the product of two population proportions through the product of their sample estimators. The aims of this article are three. First, to provide statistics based on unbiased estimators of the expected index of agreements and determine their variance based on the variance of the original statistic. Second, to make pairwise extensions of some measures. And third, to show that the old and new estimators of the Cohen’s kappa and Hubert’s kappa coefficients match the well-known estimators of concordance and intraclass correlation coefficients, if the former are defined by assuming quadratic weights. The article shows that the new estimators are always greater than or equal the classic ones, except for the case of Gwet where it is the other way around, although these differences are only relevant with small sample sizes (e.g. n ≤ 30).
      PubDate: 2024-03-06
      DOI: 10.1007/s11634-024-00581-x
       
  • The role of diversity and ensemble learning in credit card fraud detection

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      Abstract: Abstract The number of daily credit card transactions is inexorably growing: the e-commerce market expansion and the recent constraints for the Covid-19 pandemic have significantly increased the use of electronic payments. The ability to precisely detect fraudulent transactions is increasingly important, and machine learning models are now a key component of the detection process. Standard machine learning techniques are widely employed, but inadequate for the evolving nature of customers behavior entailing continuous changes in the underlying data distribution. his problem is often tackled by discarding past knowledge, despite its potential relevance in the case of recurrent concepts. Appropriate exploitation of historical knowledge is necessary: we propose a learning strategy that relies on diversity-based ensemble learning and allows to preserve past concepts and reuse them for a faster adaptation to changes. In our experiments, we adopt several state-of-the-art diversity measures and we perform comparisons with various other learning approaches. We assess the effectiveness of our proposed learning strategy on extracts of two real datasets from two European countries, containing more than 30 M and 50 M transactions, provided by our industrial partner, Worldline, a leading company in the field.
      PubDate: 2024-03-01
      DOI: 10.1007/s11634-022-00515-5
       
  • Co-clustering contaminated data: a robust model-based approach

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      Abstract: Abstract The exploration and analysis of large high-dimensional data sets calls for well-thought techniques to extract the salient information from the data, such as co-clustering. Latent block models cast co-clustering in a probabilistic framework that extends finite mixture models to the two-way setting. Real-world data sets often contain anomalies which could be of interest per se and may make the results provided by standard, non-robust procedures unreliable. Also estimation of latent block models can be heavily affected by contaminated data. We propose an algorithm to compute robust estimates for latent block models. Experiments on both simulated and real data show that our method is able to resist high levels of contamination and can provide additional insight into the data by highlighting possible anomalies.
      PubDate: 2024-03-01
      DOI: 10.1007/s11634-023-00549-3
       
  • Claims fraud detection with uncertain labels

    • Free pre-print version: Loading...

      Abstract: Abstract Insurance fraud is a non self-revealing type of fraud. The true historical labels (fraud or legitimate) are only as precise as the investigators’ efforts and successes to uncover them. Popular approaches of supervised and unsupervised learning fail to capture the ambiguous nature of uncertain labels. Imprecisely observed labels can be represented in the Dempster–Shafer theory of belief functions, a generalization of supervised and unsupervised learning suited to represent uncertainty. In this paper, we show that partial information from the historical investigations can add valuable, learnable information for the fraud detection system and improves its performances. We also show that belief function theory provides a flexible mathematical framework for concept drift detection and cost sensitive learning, two common challenges in fraud detection. Finally, we present an application to a real-world motor insurance claim fraud.
      PubDate: 2024-03-01
      DOI: 10.1007/s11634-023-00568-0
       
  • Semiparametric mixture of linear regressions with nonparametric Gaussian
           scale mixture errors

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      Abstract: Abstract In finite mixture of regression models, normal assumption for the errors of each regression component is typically adopted. Though this common assumption is theoretically and computationally convenient, it often produces inefficient and undesirable estimates which undermine the applicability of the model particularly in the presence of outliers. To reduce these defects, we propose to use nonparametric Gaussian scale mixture distributions for component error distributions. By this means, we can lessen the risk of misspecification and obtain robust estimators. In this paper, we study the identifiability of the proposed model and develop a feasible estimating algorithm. Numerical studies including simulation studies and real data analysis to demonstrate the performance of the proposed method are also presented.
      PubDate: 2024-03-01
      DOI: 10.1007/s11634-023-00570-6
       
  • Special issue on “advances in models and learning for clustering and
           classification”

    • Free pre-print version: Loading...

      PubDate: 2024-02-27
      DOI: 10.1007/s11634-024-00584-8
       
 
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              [Sort by number of followers]   [Restore default list]

  Subjects -> STATISTICS (Total: 130 journals)
Showing 1 - 151 of 151 Journals sorted alphabetically
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 52)
Annals of Applied Statistics     Full-text available via subscription   (Followers: 37)
Applied Categorical Structures     Hybrid Journal   (Followers: 5)
Argumentation et analyse du discours     Open Access   (Followers: 7)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 7)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 2)
Australian & New Zealand Journal of Statistics     Hybrid Journal   (Followers: 12)
Biometrical Journal     Hybrid Journal   (Followers: 6)
Biometrics     Hybrid Journal   (Followers: 49)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 19)
Building Simulation     Hybrid Journal   (Followers: 2)
CHANCE     Hybrid Journal   (Followers: 5)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Communications in Statistics - Theory and Methods     Hybrid Journal   (Followers: 10)
Computational Statistics     Hybrid Journal   (Followers: 17)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 38)
Current Research in Biostatistics     Open Access   (Followers: 9)
Decisions in Economics and Finance     Hybrid Journal   (Followers: 15)
Demographic Research     Open Access   (Followers: 14)
Engineering With Computers     Hybrid Journal   (Followers: 5)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
ESAIM: Probability and Statistics     Open Access   (Followers: 4)
Extremes     Hybrid Journal   (Followers: 2)
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 9)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 13)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 3)
Handbook of Statistics     Full-text available via subscription   (Followers: 8)
IEA World Energy Statistics and Balances -     Full-text available via subscription   (Followers: 2)
International Journal of Computational Economics and Econometrics     Hybrid Journal   (Followers: 6)
International Journal of Quality, Statistics, and Reliability     Open Access   (Followers: 19)
International Journal of Stochastic Analysis     Open Access   (Followers: 2)
International Statistical Review     Hybrid Journal   (Followers: 11)
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 3)
Journal of Applied Statistics     Hybrid Journal   (Followers: 20)
Journal of Biopharmaceutical Statistics     Hybrid Journal   (Followers: 17)
Journal of Business & Economic Statistics     Full-text available via subscription   (Followers: 41, SJR: 3.664, CiteScore: 2)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Journal of Computational & Graphical Statistics     Full-text available via subscription   (Followers: 21)
Journal of Econometrics     Hybrid Journal   (Followers: 85)
Journal of Educational and Behavioral Statistics     Hybrid Journal   (Followers: 8)
Journal of Forecasting     Hybrid Journal   (Followers: 21)
Journal of Global Optimization     Hybrid Journal   (Followers: 7)
Journal of Mathematics and Statistics     Open Access   (Followers: 6)
Journal of Nonparametric Statistics     Hybrid Journal   (Followers: 7)
Journal of Probability and Statistics     Open Access   (Followers: 11)
Journal of Risk and Uncertainty     Hybrid Journal   (Followers: 35)
Journal of Statistical and Econometric Methods     Open Access   (Followers: 3)
Journal of Statistical Physics     Hybrid Journal   (Followers: 12)
Journal of Statistical Planning and Inference     Hybrid Journal   (Followers: 8)
Journal of Statistical Software     Open Access   (Followers: 19, SJR: 13.802, CiteScore: 16)
Journal of the American Statistical Association     Full-text available via subscription   (Followers: 77, SJR: 3.746, CiteScore: 2)
Journal of the Korean Statistical Society     Hybrid Journal   (Followers: 1)
Journal of the Royal Statistical Society Series C (Applied Statistics)     Hybrid Journal   (Followers: 37)
Journal of the Royal Statistical Society, Series A (Statistics in Society)     Hybrid Journal   (Followers: 31)
Journal of the Royal Statistical Society, Series B (Statistical Methodology)     Hybrid Journal   (Followers: 43)
Journal of Theoretical Probability     Hybrid Journal   (Followers: 3)
Journal of Time Series Analysis     Hybrid Journal   (Followers: 18)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 28)
Law, Probability and Risk     Hybrid Journal   (Followers: 8)
Lifetime Data Analysis     Hybrid Journal   (Followers: 5)
Mathematical Methods of Statistics     Hybrid Journal   (Followers: 4)
Measurement Interdisciplinary Research and Perspectives     Hybrid Journal   (Followers: 1)
Metrika     Hybrid Journal   (Followers: 4)
Monthly Statistics of International Trade - Statistiques mensuelles du commerce international     Full-text available via subscription   (Followers: 4)
Multivariate Behavioral Research     Hybrid Journal   (Followers: 9)
Optimization Letters     Hybrid Journal   (Followers: 2)
Optimization Methods and Software     Hybrid Journal   (Followers: 5)
Oxford Bulletin of Economics and Statistics     Hybrid Journal   (Followers: 35)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 10)
Queueing Systems     Hybrid Journal   (Followers: 7)
Research Synthesis Methods     Hybrid Journal   (Followers: 8)
Review of Economics and Statistics     Hybrid Journal   (Followers: 281)
Review of Socionetwork Strategies     Hybrid Journal  
Risk Management     Hybrid Journal   (Followers: 16)
Sankhya A     Hybrid Journal   (Followers: 3)
Scandinavian Journal of Statistics     Hybrid Journal   (Followers: 9)
Sequential Analysis: Design Methods and Applications     Hybrid Journal   (Followers: 1)
Significance     Hybrid Journal   (Followers: 6)
Sociological Methods & Research     Hybrid Journal   (Followers: 49)
SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques     Full-text available via subscription  
Stata Journal     Full-text available via subscription   (Followers: 9)
Statistica Neerlandica     Hybrid Journal   (Followers: 1)
Statistical Inference for Stochastic Processes     Hybrid Journal   (Followers: 3)
Statistical Methods and Applications     Hybrid Journal   (Followers: 5)
Statistical Methods in Medical Research     Hybrid Journal   (Followers: 23)
Statistical Modelling     Hybrid Journal   (Followers: 18)
Statistical Papers     Hybrid Journal   (Followers: 4)
Statistics & Probability Letters     Hybrid Journal   (Followers: 13)
Statistics and Computing     Hybrid Journal   (Followers: 14)
Statistics and Economics     Open Access  
Statistics in Medicine     Hybrid Journal   (Followers: 144)
Statistics: A Journal of Theoretical and Applied Statistics     Hybrid Journal   (Followers: 11)
Stochastic Models     Hybrid Journal   (Followers: 2)
Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports     Hybrid Journal   (Followers: 2)
Structural and Multidisciplinary Optimization     Hybrid Journal   (Followers: 12)
Teaching Statistics     Hybrid Journal   (Followers: 8)
Technology Innovations in Statistics Education (TISE)     Open Access   (Followers: 2)
TEST     Hybrid Journal   (Followers: 3)
The American Statistician     Full-text available via subscription   (Followers: 25)
The Canadian Journal of Statistics / La Revue Canadienne de Statistique     Hybrid Journal   (Followers: 10)
Wiley Interdisciplinary Reviews - Computational Statistics     Hybrid Journal   (Followers: 1)

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