Pub Date : 2023-11-10DOI: 10.1007/s00180-023-01428-3
Cathy W. S. Chen, Rosaria Lombardo, Enrico Ripamonti
{"title":"High-dimensional data analysis and visualisation","authors":"Cathy W. S. Chen, Rosaria Lombardo, Enrico Ripamonti","doi":"10.1007/s00180-023-01428-3","DOIUrl":"https://doi.org/10.1007/s00180-023-01428-3","url":null,"abstract":"","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":"117 34","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135137888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-06DOI: 10.1007/s00180-023-01429-2
Yan Sun, Wei Huang
{"title":"Estimation and testing of kink regression model with endogenous regressors","authors":"Yan Sun, Wei Huang","doi":"10.1007/s00180-023-01429-2","DOIUrl":"https://doi.org/10.1007/s00180-023-01429-2","url":null,"abstract":"","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":"625 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135636112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-05DOI: 10.1007/s00180-023-01425-6
Roy Cerqueti, Pierpaolo D’Urso, Livia De Giovanni, Raffaele Mattera, Vincenzina Vitale
Abstract This paper proposes a new approach to fuzzy clustering of time series based on the dissimilarity among conditional higher moments. A system of weights accounts for the relevance of each conditional moment in defining the clusters. Robustness against outliers is also considered by extending the above clustering method using a suitable exponential transformation of the distance measure defined on the conditional higher moments. To show the usefulness of the proposed approach, we provide a study with simulated data and an empirical application to the time series of stocks included in the FTSEMIB 30 Index.
{"title":"Fuzzy clustering of time series based on weighted conditional higher moments","authors":"Roy Cerqueti, Pierpaolo D’Urso, Livia De Giovanni, Raffaele Mattera, Vincenzina Vitale","doi":"10.1007/s00180-023-01425-6","DOIUrl":"https://doi.org/10.1007/s00180-023-01425-6","url":null,"abstract":"Abstract This paper proposes a new approach to fuzzy clustering of time series based on the dissimilarity among conditional higher moments. A system of weights accounts for the relevance of each conditional moment in defining the clusters. Robustness against outliers is also considered by extending the above clustering method using a suitable exponential transformation of the distance measure defined on the conditional higher moments. To show the usefulness of the proposed approach, we provide a study with simulated data and an empirical application to the time series of stocks included in the FTSEMIB 30 Index.","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":"77 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135725100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-27DOI: 10.1007/s00180-023-01427-4
Jhonatan Medri, Braden D. Probst, Jürgen Symanzik
{"title":"Correction: Housing variables and immigration: an exploratory analysis in New York City","authors":"Jhonatan Medri, Braden D. Probst, Jürgen Symanzik","doi":"10.1007/s00180-023-01427-4","DOIUrl":"https://doi.org/10.1007/s00180-023-01427-4","url":null,"abstract":"","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136262551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-27DOI: 10.1007/s00180-023-01424-7
Tanguy Appriou, Didier Rullière, David Gaudrie
Kriging metamodeling (also called Gaussian Process regression) is a popular approach to predict the output of a function based on few observations. The Kriging method involves length-scale hyperparameters whose optimization is essential to obtain an accurate model and is typically performed using maximum likelihood estimation (MLE). However, for high-dimensional problems, the hyperparameter optimization is problematic and often fails to provide correct values. This is especially true for Kriging-based design optimization where the dimension is often quite high. In this article, we propose a method for building high-dimensional surrogate models which avoids the hyperparameter optimization by combining Kriging sub-models with randomly chosen length-scales. Contrarily to other approaches, it does not rely on dimension reduction techniques and it provides a closed-form expression for the model. We present a recipe to determine a suitable range for the sub-models length-scales. We also compare different approaches to compute the weights in the combination. We show for a high-dimensional test problem and a real-world application that our combination is more accurate than the classical Kriging approach using MLE.
{"title":"Combination of optimization-free kriging models for high-dimensional problems","authors":"Tanguy Appriou, Didier Rullière, David Gaudrie","doi":"10.1007/s00180-023-01424-7","DOIUrl":"https://doi.org/10.1007/s00180-023-01424-7","url":null,"abstract":"Kriging metamodeling (also called Gaussian Process regression) is a popular approach to predict the output of a function based on few observations. The Kriging method involves length-scale hyperparameters whose optimization is essential to obtain an accurate model and is typically performed using maximum likelihood estimation (MLE). However, for high-dimensional problems, the hyperparameter optimization is problematic and often fails to provide correct values. This is especially true for Kriging-based design optimization where the dimension is often quite high. In this article, we propose a method for building high-dimensional surrogate models which avoids the hyperparameter optimization by combining Kriging sub-models with randomly chosen length-scales. Contrarily to other approaches, it does not rely on dimension reduction techniques and it provides a closed-form expression for the model. We present a recipe to determine a suitable range for the sub-models length-scales. We also compare different approaches to compute the weights in the combination. We show for a high-dimensional test problem and a real-world application that our combination is more accurate than the classical Kriging approach using MLE.","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":"6 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136233155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-24DOI: 10.1007/s00180-023-01418-5
Alba Martinez-Ruiz, Natale Carlo Lauro
{"title":"Incremental singular value decomposition for some numerical aspects of multiblock redundancy analysis","authors":"Alba Martinez-Ruiz, Natale Carlo Lauro","doi":"10.1007/s00180-023-01418-5","DOIUrl":"https://doi.org/10.1007/s00180-023-01418-5","url":null,"abstract":"","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135274262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-23DOI: 10.1007/s00180-023-01422-9
Xu Qin, Huiqun Gao
{"title":"Nonparametric binary regression models with spherical predictors based on the random forests kernel","authors":"Xu Qin, Huiqun Gao","doi":"10.1007/s00180-023-01422-9","DOIUrl":"https://doi.org/10.1007/s00180-023-01422-9","url":null,"abstract":"","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":"54 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135366965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-21DOI: 10.1007/s00180-023-01421-w
Jingjing Qu, Hon Keung Tony Ng, Chul Moon
{"title":"Empirical likelihood ratio tests for homogeneity of component lifetime distributions based on system lifetime data","authors":"Jingjing Qu, Hon Keung Tony Ng, Chul Moon","doi":"10.1007/s00180-023-01421-w","DOIUrl":"https://doi.org/10.1007/s00180-023-01421-w","url":null,"abstract":"","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":"66 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135510820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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