Pub Date : 2017-07-04DOI: 10.1186/s40488-017-0062-7
M. Alizadeh, S. M. T. K. MirMostafee, E. Ortega, T. Ramires, G. Cordeiro
{"title":"The odd log-logistic logarithmic generated family of distributions with applications in different areas","authors":"M. Alizadeh, S. M. T. K. MirMostafee, E. Ortega, T. Ramires, G. Cordeiro","doi":"10.1186/s40488-017-0062-7","DOIUrl":"https://doi.org/10.1186/s40488-017-0062-7","url":null,"abstract":"","PeriodicalId":52216,"journal":{"name":"Journal of Statistical Distributions and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40488-017-0062-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65887958","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 : 2017-06-05DOI: 10.1186/s40488-020-00103-y
Cornelis J. Potgieter
{"title":"Density deconvolution for generalized skew-symmetric distributions","authors":"Cornelis J. Potgieter","doi":"10.1186/s40488-020-00103-y","DOIUrl":"https://doi.org/10.1186/s40488-020-00103-y","url":null,"abstract":"","PeriodicalId":52216,"journal":{"name":"Journal of Statistical Distributions and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40488-020-00103-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42249610","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 : 2017-03-21DOI: 10.1186/s40488-017-0055-6
W. Richter, Kay Schicker
{"title":"Simulation of polyhedral convex contoured distributions","authors":"W. Richter, Kay Schicker","doi":"10.1186/s40488-017-0055-6","DOIUrl":"https://doi.org/10.1186/s40488-017-0055-6","url":null,"abstract":"","PeriodicalId":52216,"journal":{"name":"Journal of Statistical Distributions and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40488-017-0055-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65887463","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 : 2017-03-03DOI: 10.1186/s40488-017-0059-2
G. D. Lin
{"title":"Recent developments on the moment problem","authors":"G. D. Lin","doi":"10.1186/s40488-017-0059-2","DOIUrl":"https://doi.org/10.1186/s40488-017-0059-2","url":null,"abstract":"","PeriodicalId":52216,"journal":{"name":"Journal of Statistical Distributions and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40488-017-0059-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65887546","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 : 2017-01-01Epub Date: 2017-05-03DOI: 10.1186/s40488-017-0058-3
Mei Ling Huang, Christine Nguyen
For extreme events, estimation of high conditional quantiles for heavy tailed distributions is an important problem. Quantile regression is a useful method in this field with many applications. Quantile regression uses an L1-loss function, and an optimal solution by means of linear programming. In this paper, we propose a weighted quantile regression method. Monte Carlo simulations are performed to compare the proposed method with existing methods for estimating high conditional quantiles. We also investigate two real-world examples by using the proposed weighted method. The Monte Carlo simulation and two real-world examples show the proposed method is an improvement of the existing method.
{"title":"High quantile regression for extreme events.","authors":"Mei Ling Huang, Christine Nguyen","doi":"10.1186/s40488-017-0058-3","DOIUrl":"https://doi.org/10.1186/s40488-017-0058-3","url":null,"abstract":"<p><p>For extreme events, estimation of high conditional quantiles for heavy tailed distributions is an important problem. Quantile regression is a useful method in this field with many applications. Quantile regression uses an <i>L</i> <sub>1</sub>-loss function, and an optimal solution by means of linear programming. In this paper, we propose a weighted quantile regression method. Monte Carlo simulations are performed to compare the proposed method with existing methods for estimating high conditional quantiles. We also investigate two real-world examples by using the proposed weighted method. The Monte Carlo simulation and two real-world examples show the proposed method is an improvement of the existing method.</p>","PeriodicalId":52216,"journal":{"name":"Journal of Statistical Distributions and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40488-017-0058-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37682930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-01-01Epub Date: 2017-08-31DOI: 10.1186/s40488-017-0070-7
Luai Al-Labadi, Zeynep Baskurt, Michael Evans
A logistic regression model is a specialized model for product-binomial data. When a proper, noninformative prior is placed on the unrestricted model for the product-binomial model, the hypothesis H0 of a logistic regression model holding can then be assessed by comparing the concentration of the posterior distribution about H0 with the concentration of the prior about H0. This comparison is effected via a relative belief ratio, a measure of the evidence that H0 is true, together with a measure of the strength of the evidence that H0 is either true or false. This gives an effective goodness of fit test for logistic regression.
逻辑回归模型是积二叉数据的专门模型。如果在乘积-二叉模型的非限制模型上放置一个适当的、非信息先验，那么就可以通过比较关于 H 0 的后验分布浓度和关于 H 0 的先验浓度，来评估逻辑回归模型持有的假设 H 0。这为逻辑回归提供了有效的拟合优度检验。
{"title":"Goodness of fit for the logistic regression model using relative belief.","authors":"Luai Al-Labadi, Zeynep Baskurt, Michael Evans","doi":"10.1186/s40488-017-0070-7","DOIUrl":"10.1186/s40488-017-0070-7","url":null,"abstract":"<p><p>A logistic regression model is a specialized model for product-binomial data. When a proper, noninformative prior is placed on the unrestricted model for the product-binomial model, the hypothesis <i>H</i> <sub>0</sub> of a logistic regression model holding can then be assessed by comparing the concentration of the posterior distribution about <i>H</i> <sub>0</sub> with the concentration of the prior about <i>H</i> <sub>0</sub>. This comparison is effected via a relative belief ratio, a measure of the evidence that <i>H</i> <sub>0</sub> is true, together with a measure of the strength of the evidence that <i>H</i> <sub>0</sub> is either true or false. This gives an effective goodness of fit test for logistic regression.</p>","PeriodicalId":52216,"journal":{"name":"Journal of Statistical Distributions and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6961508/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37603708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-01-01Epub Date: 2017-04-07DOI: 10.1186/s40488-017-0057-4
Habtamu K Benecha, Brian Neelon, Kimon Divaris, John S Preisser
Mixture distributions provide flexibility in modeling data collected from populations having unexplained heterogeneity. While interpretations of regression parameters from traditional finite mixture models are specific to unobserved subpopulations or latent classes, investigators are often interested in making inferences about the marginal mean of a count variable in the overall population. Recently, marginal mean regression modeling procedures for zero-inflated count outcomes have been introduced within the framework of maximum likelihood estimation of zero-inflated Poisson and negative binomial regression models. In this article, we propose marginalized mixture regression models based on two-component mixtures of non-degenerate count data distributions that provide directly interpretable estimates of exposure effects on the overall population mean of a count outcome. The models are examined using simulations and applied to two datasets, one from a double-blind dental caries incidence trial, and the other from a horticultural experiment. The finite sample performance of the proposed models are compared with each other and with marginalized zero-inflated count models, as well as ordinary Poisson and negative binomial regression.
{"title":"Marginalized mixture models for count data from multiple source populations.","authors":"Habtamu K Benecha, Brian Neelon, Kimon Divaris, John S Preisser","doi":"10.1186/s40488-017-0057-4","DOIUrl":"https://doi.org/10.1186/s40488-017-0057-4","url":null,"abstract":"<p><p>Mixture distributions provide flexibility in modeling data collected from populations having unexplained heterogeneity. While interpretations of regression parameters from traditional finite mixture models are specific to unobserved subpopulations or latent classes, investigators are often interested in making inferences about the marginal mean of a count variable in the overall population. Recently, marginal mean regression modeling procedures for zero-inflated count outcomes have been introduced within the framework of maximum likelihood estimation of zero-inflated Poisson and negative binomial regression models. In this article, we propose marginalized mixture regression models based on two-component mixtures of non-degenerate count data distributions that provide directly interpretable estimates of exposure effects on the overall population mean of a count outcome. The models are examined using simulations and applied to two datasets, one from a double-blind dental caries incidence trial, and the other from a horticultural experiment. The finite sample performance of the proposed models are compared with each other and with marginalized zero-inflated count models, as well as ordinary Poisson and negative binomial regression.</p>","PeriodicalId":52216,"journal":{"name":"Journal of Statistical Distributions and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40488-017-0057-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34946340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-01-01Epub Date: 2017-09-15DOI: 10.1186/s40488-017-0076-1
Jean-François Plante
Rank correlation is invariant to bijective marginal transformations, but it is not immune to confounding. Assuming a categorical confounding variable is observed, the author proposes weighted coefficients of correlation for continuous variables developed within a larger framework based on copulas. While the weighting is clear under the assumption that the dependence is the same within each group implied by the confounder, the author extends the Minimum Averaged Mean Squared Error (MAMSE) weights to borrow strength between groups when the dependence may vary across them. Asymptotic properties of the proposed coefficients are derived and simulations are used to assess their finite sample properties.
{"title":"Rank correlation under categorical confounding.","authors":"Jean-François Plante","doi":"10.1186/s40488-017-0076-1","DOIUrl":"10.1186/s40488-017-0076-1","url":null,"abstract":"<p><p>Rank correlation is invariant to bijective marginal transformations, but it is not immune to confounding. Assuming a categorical confounding variable is observed, the author proposes weighted coefficients of correlation for continuous variables developed within a larger framework based on copulas. While the weighting is clear under the assumption that the dependence is the same within each group implied by the confounder, the author extends the Minimum Averaged Mean Squared Error (MAMSE) weights to borrow strength between groups when the dependence may vary across them. Asymptotic properties of the proposed coefficients are derived and simulations are used to assess their finite sample properties.</p>","PeriodicalId":52216,"journal":{"name":"Journal of Statistical Distributions and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40488-017-0076-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37602166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-11-29DOI: 10.1186/s40488-016-0054-z
Shahedul A. Khan, Saima K. Khosa
{"title":"Generalized log-logistic proportional hazard model with applications in survival analysis","authors":"Shahedul A. Khan, Saima K. Khosa","doi":"10.1186/s40488-016-0054-z","DOIUrl":"https://doi.org/10.1186/s40488-016-0054-z","url":null,"abstract":"","PeriodicalId":52216,"journal":{"name":"Journal of Statistical Distributions and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40488-016-0054-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65887453","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 : 2016-11-05DOI: 10.1186/s40488-016-0051-2
Cícero R. B. Dias, G. Cordeiro, M. Alizadeh, Pedro Rafael Diniz Marinho, Hemílio Fernandes Campos Coêlho
{"title":"Exponentiated Marshall-Olkin family of distributions","authors":"Cícero R. B. Dias, G. Cordeiro, M. Alizadeh, Pedro Rafael Diniz Marinho, Hemílio Fernandes Campos Coêlho","doi":"10.1186/s40488-016-0051-2","DOIUrl":"https://doi.org/10.1186/s40488-016-0051-2","url":null,"abstract":"","PeriodicalId":52216,"journal":{"name":"Journal of Statistical Distributions and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40488-016-0051-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65887742","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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