{"title":"Tuning parameter selection in fused lasso signal approximator with false discovery rate control","authors":"W. Son, Johan Lim, Donghyeon Yu","doi":"10.1214/23-bjps577","DOIUrl":"https://doi.org/10.1214/23-bjps577","url":null,"abstract":"","PeriodicalId":51242,"journal":{"name":"Brazilian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139345746","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}
{"title":"Inference for a competing risks model with Burr XII distributions under generalized progressive hybrid censoring","authors":"Prakash Chandra, Amulya Kumar Mahto, Y. Tripathi","doi":"10.1214/23-bjps582","DOIUrl":"https://doi.org/10.1214/23-bjps582","url":null,"abstract":"","PeriodicalId":51242,"journal":{"name":"Brazilian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139346088","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}
Brenda V. Mac’Oduol, Narayanaswamy Balakrishnan, Paul van Staden, Robert King
{"title":"L-moments of asymmetric generalized distributions obtained through quantile splicing","authors":"Brenda V. Mac’Oduol, Narayanaswamy Balakrishnan, Paul van Staden, Robert King","doi":"10.1214/23-bjps580","DOIUrl":"https://doi.org/10.1214/23-bjps580","url":null,"abstract":"","PeriodicalId":51242,"journal":{"name":"Brazilian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139347117","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}
R. Vila, Narayanaswamy Balakrishnan, H. Saulo, Ana Protazio
{"title":"Bivariate log-symmetric models: Distributional properties, parameter estimation and an application to public spending data","authors":"R. Vila, Narayanaswamy Balakrishnan, H. Saulo, Ana Protazio","doi":"10.1214/23-bjps584","DOIUrl":"https://doi.org/10.1214/23-bjps584","url":null,"abstract":"","PeriodicalId":51242,"journal":{"name":"Brazilian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139346290","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}
G. Martínez-Flórez, Mario Pacheco, Artur J. Lemonte
{"title":"Influence diagnostics for the power-normal Tobit model","authors":"G. Martínez-Flórez, Mario Pacheco, Artur J. Lemonte","doi":"10.1214/23-bjps573","DOIUrl":"https://doi.org/10.1214/23-bjps573","url":null,"abstract":"","PeriodicalId":51242,"journal":{"name":"Brazilian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41532723","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}
{"title":"High-dimensional regime for Wishart matrices based on the increments of the solution to the stochastic heat equation","authors":"Julie Gamain, D. Mollinedo, C. Tudor","doi":"10.1214/23-bjps574","DOIUrl":"https://doi.org/10.1214/23-bjps574","url":null,"abstract":"","PeriodicalId":51242,"journal":{"name":"Brazilian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44436546","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}
. In this paper, a new family of continuous random variables with positive support is introduced. Its density function has the capacity to incorporate features of uni-modality and bimodality. Special attention is paid to the lognormal distribution which is included as a particular case. Its density function is given in closed-form, allowing prob-abilities, moments and other related measures such as skewness and kurtosis coefficients to be computed easily. In addition, a stochastic representation of the family that enables us to generate random variates of this model is also presented. Some properties related with the right tail and actuarial aspects of the distribution are also shown. This new family of distributions is numerically illustrated with data taken from the Medical Expenditure Panel Survey (MEPS), conducted by the US Agency of Health Research and Quality and with a well-known data set which has been studied widely in the actuarial literature.
{"title":"Beyond the lognormal distribution with properties and applications","authors":"E. Gómez–Déniz, Osvaldo Venegas, H. W. Gómez","doi":"10.1214/22-bjps546","DOIUrl":"https://doi.org/10.1214/22-bjps546","url":null,"abstract":". In this paper, a new family of continuous random variables with positive support is introduced. Its density function has the capacity to incorporate features of uni-modality and bimodality. Special attention is paid to the lognormal distribution which is included as a particular case. Its density function is given in closed-form, allowing prob-abilities, moments and other related measures such as skewness and kurtosis coefficients to be computed easily. In addition, a stochastic representation of the family that enables us to generate random variates of this model is also presented. Some properties related with the right tail and actuarial aspects of the distribution are also shown. This new family of distributions is numerically illustrated with data taken from the Medical Expenditure Panel Survey (MEPS), conducted by the US Agency of Health Research and Quality and with a well-known data set which has been studied widely in the actuarial literature.","PeriodicalId":51242,"journal":{"name":"Brazilian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44515551","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}
{"title":"Preface to the Special Issue","authors":"","doi":"10.1214/23-bjps372pre","DOIUrl":"https://doi.org/10.1214/23-bjps372pre","url":null,"abstract":"","PeriodicalId":51242,"journal":{"name":"Brazilian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135094956","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}
Luis Benites, C. Zeller, H. Bolfarine, V. H. Lachos
. In the framework of censored regression models, the distribution of the error term can depart significantly from normality, for instance, due to the presence of multi-modality, skewness and/or atypical observations. In this paper we propose a novel censored linear regression model where the random errors follow a finite mixture of scale mixtures of normal (SMN) distribution. The SMN is an attractive class of symmetrical heavy-tailed densities that includes the normal, Student-t, slash and the contaminated normal distribution as special cases. This approach allows us to model data with great flexibility, ac-commodating simultaneously multimodality, heavy tails and skewness depending on the structure of the mixture components. We develop an analytically tractable and efficient EM-type algorithm for iteratively computing the maximum likelihood estimates of the parameters, with standard errors and prediction of the censored values as a by-products. The proposed algorithm has closed-form expressions at the E-step, that rely on formulas for the mean and variance of the truncated SMN distributions. The efficacy of the method is verified through the analysis of simulated and real datasets. The methodology addressed in this paper is implemented in the R package C ensMixReg.
{"title":"Regression modeling of censored data based on compound scale mixtures of normal distributions","authors":"Luis Benites, C. Zeller, H. Bolfarine, V. H. Lachos","doi":"10.1214/22-bjps551","DOIUrl":"https://doi.org/10.1214/22-bjps551","url":null,"abstract":". In the framework of censored regression models, the distribution of the error term can depart significantly from normality, for instance, due to the presence of multi-modality, skewness and/or atypical observations. In this paper we propose a novel censored linear regression model where the random errors follow a finite mixture of scale mixtures of normal (SMN) distribution. The SMN is an attractive class of symmetrical heavy-tailed densities that includes the normal, Student-t, slash and the contaminated normal distribution as special cases. This approach allows us to model data with great flexibility, ac-commodating simultaneously multimodality, heavy tails and skewness depending on the structure of the mixture components. We develop an analytically tractable and efficient EM-type algorithm for iteratively computing the maximum likelihood estimates of the parameters, with standard errors and prediction of the censored values as a by-products. The proposed algorithm has closed-form expressions at the E-step, that rely on formulas for the mean and variance of the truncated SMN distributions. The efficacy of the method is verified through the analysis of simulated and real datasets. The methodology addressed in this paper is implemented in the R package C ensMixReg.","PeriodicalId":51242,"journal":{"name":"Brazilian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41428767","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}
Jalmar M. F. Carrasco, S. Ferrari, R. Arellano-Valle
. This paper deals with beta regression models with a covariate that is not directly observed; instead, it is replaced by a surrogate covariate that underpredicts its actual value. We propose a multiplicative errors-in-variables model tailored for this situation and develop calibration regression and pseudo-likelihood-based inference for the unknown parameters. The impact of ignoring the measurement error and the performance of the inference methods are evaluated through simulations and a real data illustration.
{"title":"Multiplicative errors-in-variables beta regression","authors":"Jalmar M. F. Carrasco, S. Ferrari, R. Arellano-Valle","doi":"10.1214/22-bjps543","DOIUrl":"https://doi.org/10.1214/22-bjps543","url":null,"abstract":". This paper deals with beta regression models with a covariate that is not directly observed; instead, it is replaced by a surrogate covariate that underpredicts its actual value. We propose a multiplicative errors-in-variables model tailored for this situation and develop calibration regression and pseudo-likelihood-based inference for the unknown parameters. The impact of ignoring the measurement error and the performance of the inference methods are evaluated through simulations and a real data illustration.","PeriodicalId":51242,"journal":{"name":"Brazilian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43695919","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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