Pub Date : 2020-12-19DOI: 10.1080/23737484.2020.1869629
H. Shang, Ruofan Xu
Abstract We consider forecasting functional time series of extreme values within a generalized extreme value distribution (GEV). The GEV distribution can be characterized using the three parameters (location, scale, and shape). As a result, the forecasts of the GEV density can be accomplished by forecasting these three latent parameters. Depending on the underlying data structure, some of the three parameters can either be modeled as scalars or functions. We provide two forecasting algorithms to model and forecast these parameters. To assess the forecast uncertainty, we apply a sieve bootstrap method to construct pointwise and simultaneous prediction intervals of the forecasted extreme values. Illustrated by a daily maximum temperature dataset, we demonstrate the advantages of modeling these parameters as functions. Further, the finite-sample performance of our methods is quantified using several Monte Carlo simulated data under a range of scenarios.
{"title":"Functional time series forecasting of extreme values","authors":"H. Shang, Ruofan Xu","doi":"10.1080/23737484.2020.1869629","DOIUrl":"https://doi.org/10.1080/23737484.2020.1869629","url":null,"abstract":"Abstract We consider forecasting functional time series of extreme values within a generalized extreme value distribution (GEV). The GEV distribution can be characterized using the three parameters (location, scale, and shape). As a result, the forecasts of the GEV density can be accomplished by forecasting these three latent parameters. Depending on the underlying data structure, some of the three parameters can either be modeled as scalars or functions. We provide two forecasting algorithms to model and forecast these parameters. To assess the forecast uncertainty, we apply a sieve bootstrap method to construct pointwise and simultaneous prediction intervals of the forecasted extreme values. Illustrated by a daily maximum temperature dataset, we demonstrate the advantages of modeling these parameters as functions. Further, the finite-sample performance of our methods is quantified using several Monte Carlo simulated data under a range of scenarios.","PeriodicalId":36561,"journal":{"name":"Communications in Statistics Case Studies Data Analysis and Applications","volume":"154 1","pages":"182 - 199"},"PeriodicalIF":0.0,"publicationDate":"2020-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88997212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-09DOI: 10.1080/23737484.2020.1850372
O. Habimana, K. Månsson, P. Sjölander
Abstract We suggest a wavelet filtering technique as a remedy to the problem of measurement errors when testing for cointegration using Johansen’s (1988) likelihood ratio test. Measurement errors, which more or less are always present in empirical economic data, essentially indicates that the variable of interest (the true signal) is contaminated with noise, which may induce biased and inconsistent estimates and erroneous inference. Our Monte Carlo experiments demonstrate that measurement errors distort the statistical size of Johansen’s cointegration test in finite samples; the test is significantly oversized. A contribution and major finding of this article is that the proposed wavelet-based technique significantly improves the statistical size of the traditional Johansen test in small and medium sized samples. Since Johansen’s test is a standard cointegration test, and we demonstrate that the constantly present measurement errors in empirical data over sizes the test, this simple alteration can be used in most situations with more reliable finite sample inference. We empirically examine the long-run relation between CO2 emissions and the real GDP in the G7 countries. The traditional Johansen tests provide evidence of an equilibrium relation for Canada and weak evidence for the US. However, the suggested size-unbiased wavelet-filtering approach consistently indicates no evidence of cointegration for all six countries.
{"title":"A wavelet-based approach for Johansen’s likelihood ratio test for cointegration in the presence of measurement errors: An application to CO2 emissions and real GDP data","authors":"O. Habimana, K. Månsson, P. Sjölander","doi":"10.1080/23737484.2020.1850372","DOIUrl":"https://doi.org/10.1080/23737484.2020.1850372","url":null,"abstract":"Abstract We suggest a wavelet filtering technique as a remedy to the problem of measurement errors when testing for cointegration using Johansen’s (1988) likelihood ratio test. Measurement errors, which more or less are always present in empirical economic data, essentially indicates that the variable of interest (the true signal) is contaminated with noise, which may induce biased and inconsistent estimates and erroneous inference. Our Monte Carlo experiments demonstrate that measurement errors distort the statistical size of Johansen’s cointegration test in finite samples; the test is significantly oversized. A contribution and major finding of this article is that the proposed wavelet-based technique significantly improves the statistical size of the traditional Johansen test in small and medium sized samples. Since Johansen’s test is a standard cointegration test, and we demonstrate that the constantly present measurement errors in empirical data over sizes the test, this simple alteration can be used in most situations with more reliable finite sample inference. We empirically examine the long-run relation between CO2 emissions and the real GDP in the G7 countries. The traditional Johansen tests provide evidence of an equilibrium relation for Canada and weak evidence for the US. However, the suggested size-unbiased wavelet-filtering approach consistently indicates no evidence of cointegration for all six countries.","PeriodicalId":36561,"journal":{"name":"Communications in Statistics Case Studies Data Analysis and Applications","volume":"14 1","pages":"128 - 145"},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88862684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-08DOI: 10.1080/23737484.2020.1852131
Tatiane Fontana Ribeiro, E. Seidel, R. Guerra, Fernando A. Peña-Ramírez, A. M. D. Silva
Abstract In this article, we consider the more recent soybean production data in Rio Grande do Sul (years 2017 and 2018) and obtain regression models through the generalized additive models for location, scale, and shape (GAMLSS) approach, and provide a dashboard as a visualization tool of the considered variables. Two models are applied to explain and predict the soybean production value as a function of the covariates, such as produced quantity, number of establishments, and average yield in each city of RS. Validation and cross-validation methods are considered to assess whether the predictions provided by the fitted models are reliable. The fitted model with data of 2017 provides the best predictions. The GAMLSS framework may be more accurate than linear regression to model data related to soybean production, constituting them in a reliable and useful source to auxiliary the farmers and economic sector managers in making decisions.
{"title":"Soybean production value in the Rio Grande do Sul under the GAMLSS framework","authors":"Tatiane Fontana Ribeiro, E. Seidel, R. Guerra, Fernando A. Peña-Ramírez, A. M. D. Silva","doi":"10.1080/23737484.2020.1852131","DOIUrl":"https://doi.org/10.1080/23737484.2020.1852131","url":null,"abstract":"Abstract In this article, we consider the more recent soybean production data in Rio Grande do Sul (years 2017 and 2018) and obtain regression models through the generalized additive models for location, scale, and shape (GAMLSS) approach, and provide a dashboard as a visualization tool of the considered variables. Two models are applied to explain and predict the soybean production value as a function of the covariates, such as produced quantity, number of establishments, and average yield in each city of RS. Validation and cross-validation methods are considered to assess whether the predictions provided by the fitted models are reliable. The fitted model with data of 2017 provides the best predictions. The GAMLSS framework may be more accurate than linear regression to model data related to soybean production, constituting them in a reliable and useful source to auxiliary the farmers and economic sector managers in making decisions.","PeriodicalId":36561,"journal":{"name":"Communications in Statistics Case Studies Data Analysis and Applications","volume":"126 1","pages":"146 - 165"},"PeriodicalIF":0.0,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74484187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-11DOI: 10.1080/23737484.2020.1850108
C. Skiadas, Yiannis Dimotikalis, C. Skiadas
{"title":"Special Issue – Communications in Statistics – Case Studies and Data Analysis: 5th Stochastic Modeling Techniques and Data Analysis International Conference","authors":"C. Skiadas, Yiannis Dimotikalis, C. Skiadas","doi":"10.1080/23737484.2020.1850108","DOIUrl":"https://doi.org/10.1080/23737484.2020.1850108","url":null,"abstract":"","PeriodicalId":36561,"journal":{"name":"Communications in Statistics Case Studies Data Analysis and Applications","volume":"9 1","pages":"381 - 382"},"PeriodicalIF":0.0,"publicationDate":"2020-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81641717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-05DOI: 10.1080/23737484.2020.1842269
N. M. Khan, Ashwinee Devi Soobhug, Z. Jannoo
Abstract This article proposes a nonstationary clustered longitudinal model to analyze road traffic accident time series data from 2016 to 2017 in Mauritius. The Conway–Maxwell–Poisson model (COM-Poisson) is used as the baseline model with gamma-distributed random effects (CMP-G). Several time-variant explanatory variables are incorporated into the model specification link predictor to identify the likely causes of road crashes in the Mauritius. The proposed model competes with the popular Poisson gamma and log-normal mixtures when modeling over-dispersion. The model parameters namely the regression, serial, and dispersion effects, are estimated suitably by a generalized quasi-likelihood (GQL) estimation method while the serial parameter is treated as nuisance and estimated by method of moments. The asymptotic properties of the GQL estimators are discussed. A simulation study based on an integer-valued auto-regressive of order 1 structure (INAR(1)) with CMP-G distributed innovation terms, is also proposed to assess the performance of GQL based on the CMP-G. Data application is of road traffic accidents in Mauritius where some model criteria have been computed to assess the goodness of fits of the proposed model against the Poisson-gamma and Poisson-log normal mixtures.
{"title":"Modeling road traffic accidents in Mauritius using clustered longitudinal COM-Poisson with gamma random effects","authors":"N. M. Khan, Ashwinee Devi Soobhug, Z. Jannoo","doi":"10.1080/23737484.2020.1842269","DOIUrl":"https://doi.org/10.1080/23737484.2020.1842269","url":null,"abstract":"Abstract This article proposes a nonstationary clustered longitudinal model to analyze road traffic accident time series data from 2016 to 2017 in Mauritius. The Conway–Maxwell–Poisson model (COM-Poisson) is used as the baseline model with gamma-distributed random effects (CMP-G). Several time-variant explanatory variables are incorporated into the model specification link predictor to identify the likely causes of road crashes in the Mauritius. The proposed model competes with the popular Poisson gamma and log-normal mixtures when modeling over-dispersion. The model parameters namely the regression, serial, and dispersion effects, are estimated suitably by a generalized quasi-likelihood (GQL) estimation method while the serial parameter is treated as nuisance and estimated by method of moments. The asymptotic properties of the GQL estimators are discussed. A simulation study based on an integer-valued auto-regressive of order 1 structure (INAR(1)) with CMP-G distributed innovation terms, is also proposed to assess the performance of GQL based on the CMP-G. Data application is of road traffic accidents in Mauritius where some model criteria have been computed to assess the goodness of fits of the proposed model against the Poisson-gamma and Poisson-log normal mixtures.","PeriodicalId":36561,"journal":{"name":"Communications in Statistics Case Studies Data Analysis and Applications","volume":"1 1","pages":"113 - 127"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73436860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-04DOI: 10.1080/23737484.2020.1842268
Dominika Ballová
Abstract In hydrometeorological processes, it is crucial to detect changes, since it can help prevent or at least prepare for extreme events like floods and drought. In this article, long term and abrupt changes in the development of average monthly overflow of main rivers of Slovakia are detected. Since the data follow non-normal distribution, results are obtained by means of nonparametric methods. Significant trends in the series were detected by applying the Mann–Kendall test, the Spearman’s rho test and the Cox–Stuart test. Change-points were detected by using the Pettitt’s test and the Buishand test. Since an abrupt change in the series could cause a misleading outcome of the trend analysis, first we applied change-point detection. If at least one significant change appeared in the series, trend analysis is applied on each segment bounded by the change-points. Otherwise a trend analysis is applied to the whole series.
{"title":"Detecting long term and abrupt changes of river overflows in Slovakia","authors":"Dominika Ballová","doi":"10.1080/23737484.2020.1842268","DOIUrl":"https://doi.org/10.1080/23737484.2020.1842268","url":null,"abstract":"Abstract In hydrometeorological processes, it is crucial to detect changes, since it can help prevent or at least prepare for extreme events like floods and drought. In this article, long term and abrupt changes in the development of average monthly overflow of main rivers of Slovakia are detected. Since the data follow non-normal distribution, results are obtained by means of nonparametric methods. Significant trends in the series were detected by applying the Mann–Kendall test, the Spearman’s rho test and the Cox–Stuart test. Change-points were detected by using the Pettitt’s test and the Buishand test. Since an abrupt change in the series could cause a misleading outcome of the trend analysis, first we applied change-point detection. If at least one significant change appeared in the series, trend analysis is applied on each segment bounded by the change-points. Otherwise a trend analysis is applied to the whole series.","PeriodicalId":36561,"journal":{"name":"Communications in Statistics Case Studies Data Analysis and Applications","volume":"4 1","pages":"383 - 391"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85599494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-22DOI: 10.1080/23737484.2020.1836533
Afshan Riaz, Muhammad Noor-ul-Amin, Hina Khan
Abstract Monitoring the coefficient of variation (CV) is a successful approach in statistical process control to monitor the process variability when the process mean and standard deviation are not constant. In this study, we proposed auxiliary information based exponentially weighted moving CV control chart by using a three-parameter logarithmic transformation to monitor the small and moderate change in process CV. The new chart is compared to the considered charts by means of average run length. The application of the proposed chart is demonstrated to monitor the electric conductivity of a water system by incorporating aggregate hardness of water as an auxiliary variable.
{"title":"Auxiliary information based exponentially weighted moving co-efficient of variation control chart: An application to monitor electric conductivity for water system","authors":"Afshan Riaz, Muhammad Noor-ul-Amin, Hina Khan","doi":"10.1080/23737484.2020.1836533","DOIUrl":"https://doi.org/10.1080/23737484.2020.1836533","url":null,"abstract":"Abstract Monitoring the coefficient of variation (CV) is a successful approach in statistical process control to monitor the process variability when the process mean and standard deviation are not constant. In this study, we proposed auxiliary information based exponentially weighted moving CV control chart by using a three-parameter logarithmic transformation to monitor the small and moderate change in process CV. The new chart is compared to the considered charts by means of average run length. The application of the proposed chart is demonstrated to monitor the electric conductivity of a water system by incorporating aggregate hardness of water as an auxiliary variable.","PeriodicalId":36561,"journal":{"name":"Communications in Statistics Case Studies Data Analysis and Applications","volume":"37 1","pages":"49 - 66"},"PeriodicalIF":0.0,"publicationDate":"2020-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87555532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-10DOI: 10.1080/23737484.2020.1829177
P. Economou, S. Malefaki, K. Kounetas
Abstract Growth theory argues on the role of heterogeneity that can lead to multiple regimes examining countries’ performance. A metaproduction stochastic function for the Bayesian frontier model is developed to estimate productive performance across 109 countries over a 20-year period using two distinct frontiers (OECD vs. non-OECD countries). The metafrontier model is used to highlight heterogeneity among clusters of countries revealing catch up phenomena. The estimation procedure is based on the notion of stochastic ordering and relies on the solution of an optimization problem on the parameters’ posterior means. Empirical results reveal that heterogeneity indeed plays a significant and distinctive role.
{"title":"A metafrontier production function for a Bayesian frontier model: A cross-country comparison","authors":"P. Economou, S. Malefaki, K. Kounetas","doi":"10.1080/23737484.2020.1829177","DOIUrl":"https://doi.org/10.1080/23737484.2020.1829177","url":null,"abstract":"Abstract Growth theory argues on the role of heterogeneity that can lead to multiple regimes examining countries’ performance. A metaproduction stochastic function for the Bayesian frontier model is developed to estimate productive performance across 109 countries over a 20-year period using two distinct frontiers (OECD vs. non-OECD countries). The metafrontier model is used to highlight heterogeneity among clusters of countries revealing catch up phenomena. The estimation procedure is based on the notion of stochastic ordering and relies on the solution of an optimization problem on the parameters’ posterior means. Empirical results reveal that heterogeneity indeed plays a significant and distinctive role.","PeriodicalId":36561,"journal":{"name":"Communications in Statistics Case Studies Data Analysis and Applications","volume":"2 1","pages":"88 - 111"},"PeriodicalIF":0.0,"publicationDate":"2020-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89638952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-07DOI: 10.1080/23737484.2020.1826368
F. Nascimento, Wires do Nascimento Moura
Abstract Choosing the prior distribution may have great impact on the result of the posterior distribution and, consequently, point and interval estimation of parameters and the respective predictive density. In situations where the parameters are positive, the gamma distribution is the most common as a conjugate prior to a wide family of parameters. Bourguignon presented the weighted Lindley (WL) distribution as an alternative conjugate prior to parameters conjugated from the gamma family. This work consists in presenting a general way to perform inference to this parameters in a p-parametric vector distribution. The method is illustrated with two cases where both the WL distribution and the gamma distribution are conjugated to these families. Estimation of posterior points is sampled using MCMC techniques. The results of the applications showed advantage on using the WL prior distribution, compared to results with the usual prior gamma distribution.
{"title":"Inference using MCMC to a new conjugate prior to positive parameters, applied in environmental data","authors":"F. Nascimento, Wires do Nascimento Moura","doi":"10.1080/23737484.2020.1826368","DOIUrl":"https://doi.org/10.1080/23737484.2020.1826368","url":null,"abstract":"Abstract Choosing the prior distribution may have great impact on the result of the posterior distribution and, consequently, point and interval estimation of parameters and the respective predictive density. In situations where the parameters are positive, the gamma distribution is the most common as a conjugate prior to a wide family of parameters. Bourguignon presented the weighted Lindley (WL) distribution as an alternative conjugate prior to parameters conjugated from the gamma family. This work consists in presenting a general way to perform inference to this parameters in a p-parametric vector distribution. The method is illustrated with two cases where both the WL distribution and the gamma distribution are conjugated to these families. Estimation of posterior points is sampled using MCMC techniques. The results of the applications showed advantage on using the WL prior distribution, compared to results with the usual prior gamma distribution.","PeriodicalId":36561,"journal":{"name":"Communications in Statistics Case Studies Data Analysis and Applications","volume":"50 1","pages":"36 - 48"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87552004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-01DOI: 10.1080/23737484.2020.1830733
Rafik Abdesselam
ABSTRACT Topological multiple correspondence analysis (TMCA) studies a group of categorical variables defined on the same set of individuals. It is a topological method of data analysis that consists of exploring, analyzing, and representing the associations between several qualitative variables in the context of multiple correspondence analysis (MCA). It compares and classifies proximity measures to select the best one according to the data under consideration, then analyzes, interprets, and visualizes with graphic representations, the possible associations between several categorical variables relating to the known problem of MCA. Based on the notion of neighborhood graphs, some of these proximity measures are more-or-less equivalent. A topological equivalence index between two measures is defined and statistically tested according to the degree of description of the associations between the modalities of these qualitative variables. We compare proximity measures and propose a topological criterion for choosing the best association measure, adapted to the data considered, from among some of the most widely used proximity measures for categorical data. The principle of the proposed approach is illustrated using a real dataset with conventional proximity measures for binary variables from the literature. The first step is to find the proximity measure that can best be adapted to the data; the second step is to use this measure to perform the TMCA.
{"title":"A topological approach of multiple correspondence analysis","authors":"Rafik Abdesselam","doi":"10.1080/23737484.2020.1830733","DOIUrl":"https://doi.org/10.1080/23737484.2020.1830733","url":null,"abstract":"ABSTRACT Topological multiple correspondence analysis (TMCA) studies a group of categorical variables defined on the same set of individuals. It is a topological method of data analysis that consists of exploring, analyzing, and representing the associations between several qualitative variables in the context of multiple correspondence analysis (MCA). It compares and classifies proximity measures to select the best one according to the data under consideration, then analyzes, interprets, and visualizes with graphic representations, the possible associations between several categorical variables relating to the known problem of MCA. Based on the notion of neighborhood graphs, some of these proximity measures are more-or-less equivalent. A topological equivalence index between two measures is defined and statistically tested according to the degree of description of the associations between the modalities of these qualitative variables. We compare proximity measures and propose a topological criterion for choosing the best association measure, adapted to the data considered, from among some of the most widely used proximity measures for categorical data. The principle of the proposed approach is illustrated using a real dataset with conventional proximity measures for binary variables from the literature. The first step is to find the proximity measure that can best be adapted to the data; the second step is to use this measure to perform the TMCA.","PeriodicalId":36561,"journal":{"name":"Communications in Statistics Case Studies Data Analysis and Applications","volume":"1 1","pages":"429 - 447"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86442973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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