Pub Date : 2023-11-11DOI: 10.1080/23737484.2023.2278106
Rahim Alhamzawi, Ahmed Alhamzawi, Himel Mallick
AbstractA new penalized likelihood method (reciprocal elastic net) is put forward for regularization and variable selection. Our proposal is based on a new class of reciprocal penalty functions, combining the strengths of the reciprocal LASSO regularization and ridge regression. We formulate the reciprocal elastic net problem as an equivalent reciprocal LASSO problem on augmented data, facilitating a direct utilization of the reciprocal LASSO algorithm to generate the entire reciprocal elastic net solution path. We further present the reciprocal adaptive elastic net, fusing the merits of ridge regression with the adaptively weighted reciprocal LASSO regularization. These methods, illustrated through simulated examples and real data analysis, demonstrate satisfactory performance in various diversified scenarios compared to published methods. Finally, we propose Bayesian methods to solve the reciprocal elastic net and reciprocal adaptive elastic net models using Gibbs samplers.Keywords: Reciprocal LASSOregularizationvariable SelectionLASSOelastic netBayesian analysis Disclosure statementOn behalf of all the co-authors, the corresponding author states that there is no conflict of interest.
{"title":"The reciprocal elastic net","authors":"Rahim Alhamzawi, Ahmed Alhamzawi, Himel Mallick","doi":"10.1080/23737484.2023.2278106","DOIUrl":"https://doi.org/10.1080/23737484.2023.2278106","url":null,"abstract":"AbstractA new penalized likelihood method (reciprocal elastic net) is put forward for regularization and variable selection. Our proposal is based on a new class of reciprocal penalty functions, combining the strengths of the reciprocal LASSO regularization and ridge regression. We formulate the reciprocal elastic net problem as an equivalent reciprocal LASSO problem on augmented data, facilitating a direct utilization of the reciprocal LASSO algorithm to generate the entire reciprocal elastic net solution path. We further present the reciprocal adaptive elastic net, fusing the merits of ridge regression with the adaptively weighted reciprocal LASSO regularization. These methods, illustrated through simulated examples and real data analysis, demonstrate satisfactory performance in various diversified scenarios compared to published methods. Finally, we propose Bayesian methods to solve the reciprocal elastic net and reciprocal adaptive elastic net models using Gibbs samplers.Keywords: Reciprocal LASSOregularizationvariable SelectionLASSOelastic netBayesian analysis Disclosure statementOn behalf of all the co-authors, the corresponding author states that there is no conflict of interest.","PeriodicalId":36561,"journal":{"name":"Communications in Statistics Case Studies Data Analysis and Applications","volume":"16 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135043043","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 : 2023-10-13DOI: 10.1080/23737484.2023.2266404
P. Divya, S. Suresh
AbstractSurvival analysis is a statistical technique mainly used to analyze time-to-event data. Identification of influential observation attains greater importance since it leads to discovering new prognostic factors. Influential observation in survival typically points to individuals whose survival time is extremely short or long in comparison to others. Particularly, when the data possess more covariates than the observations, all classical approaches fail to perform. Hence, dimensionality reduction is necessary for choosing appropriate variables and it has been done by popular techniques such as LASSO and elastic net algorithm. This paper consider high-dimensional breast cancer data, and its dimensionality is reduced using variable selection methods. Subsequently, the rank product test and martingale residuals are used to identify an influential observation. Furthermore, a resampling technique is used to validate the consistency and robustness of the methods. The novelty of this paper lies in comparing the prediction accuracy of datasets with and without outliers using Random Survival Forest (RSF) for different training fractions. Comparatively, the RSF result demonstrates that the LASSO approach outperform others in the absence of outliers. Therefore, we suggest reducing dimensionality using the LASSO variable selection technique first, followed by removing likely outliers to improve the performance of classification algorithms.KEYWORDS: Survival analysisvariable selection methodsmartingale residualsrank product testrandom survival forest Disclosure statementThe authors declare that there is no conflict of interest regarding the publication of this paper.
{"title":"Detection of influential observations in high-dimensional survival data","authors":"P. Divya, S. Suresh","doi":"10.1080/23737484.2023.2266404","DOIUrl":"https://doi.org/10.1080/23737484.2023.2266404","url":null,"abstract":"AbstractSurvival analysis is a statistical technique mainly used to analyze time-to-event data. Identification of influential observation attains greater importance since it leads to discovering new prognostic factors. Influential observation in survival typically points to individuals whose survival time is extremely short or long in comparison to others. Particularly, when the data possess more covariates than the observations, all classical approaches fail to perform. Hence, dimensionality reduction is necessary for choosing appropriate variables and it has been done by popular techniques such as LASSO and elastic net algorithm. This paper consider high-dimensional breast cancer data, and its dimensionality is reduced using variable selection methods. Subsequently, the rank product test and martingale residuals are used to identify an influential observation. Furthermore, a resampling technique is used to validate the consistency and robustness of the methods. The novelty of this paper lies in comparing the prediction accuracy of datasets with and without outliers using Random Survival Forest (RSF) for different training fractions. Comparatively, the RSF result demonstrates that the LASSO approach outperform others in the absence of outliers. Therefore, we suggest reducing dimensionality using the LASSO variable selection technique first, followed by removing likely outliers to improve the performance of classification algorithms.KEYWORDS: Survival analysisvariable selection methodsmartingale residualsrank product testrandom survival forest Disclosure statementThe authors declare that there is no conflict of interest regarding the publication of this paper.","PeriodicalId":36561,"journal":{"name":"Communications in Statistics Case Studies Data Analysis and Applications","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135853777","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 : 2023-10-10DOI: 10.1080/23737484.2023.2266714
Felix Wamono, Leonard Atuhaire, Innocent Ngaruye, Dietrich von Rosen, Martin Singull
AbstractPanel survey data from Uganda, as well as data from the 2014 Uganda Population and Housing Census have been analyzed. The Growth Curve model with rank restrictions on parameters was used to estimate the small area means. The aim of the analysis was to assess change over time in household living standards (welfare), i.e., to investigate whether households display growth in living standards? whether households grow at the same rates? and whether households in different geographical areas of the country grow at the same rates? Using a GMANOVA-MANOVA model with rank restrictions on parameters, it was established that growth in household standards of living in Uganda varied across small areas. Sub-regions (small areas) with the highest standards of living in Uganda at the endline were Central Urban region, Kampala Urban region and South Western Urban region, while the sub-regions with the lowest standards of living at the endline were North East Rural region, North East Urban region and Eastern Rural region. The sub-regions with the highest growth rates in standards of living were Mid West Urban region, Mid North Rural region, and South Western Urban region. The sub-regions with the highest decline in standards of living were East Central Rural region, East Rural region and West Nile Urban region.KEYWORDS: Extended growth curve modelrank restrictions on parametersrepeated surveyssmall area estimation AcknowledgmentsWe thank the Uganda Bureau of Statistics for providing all the datasets that were used in this article. We thank the editors and reviewers for the insightful comments.Additional informationFundingThe research of Felix Wamono is supported by the Swedish International Development and Cooperation Agency (Sida) in collaboration with Makerere University, under Sida-Makerere University Cooperation Agreement, Project 316. Dietrich von Rosen is supported by the Swedish Research Council (2017-03003).
摘要本文对乌干达的面板调查数据以及2014年乌干达人口和住房普查数据进行了分析。采用对参数进行秩限制的生长曲线模型估计小面积均值。分析的目的是评估家庭生活水平(福利)随时间的变化，即调查家庭的生活水平是否有所提高?家庭是否以同样的速度增长?国家不同地理区域的家庭是否以相同的速度增长?使用具有参数等级限制的GMANOVA-MANOVA模型，可以确定乌干达家庭生活水平的增长在小地区之间存在差异。乌干达末端生活水平最高的分区域(小区域)是中部城市地区、坎帕拉城市地区和西南城市地区，而末端生活水平最低的分区域是东北农村地区、东北城市地区和东部农村地区。生活水平增长率最高的分区域是中西部城市地区、中部北部农村地区和西南城市地区。生活水平下降幅度最大的分区域是东部中部农村地区、东部农村地区和西尼罗河城市地区。关键词:扩展生长曲线模型参数的等级限制重复调查小面积估计感谢乌干达统计局为本文提供的所有数据集。我们感谢编辑和审稿人的深刻见解。Felix Wamono的研究由瑞典国际发展与合作署(Sida)与Makerere大学合作支持，根据Sida-Makerere大学合作协议，316项目。Dietrich von Rosen由瑞典研究委员会资助(2017-03003)。
{"title":"Small area estimation of trends in household living standards in Uganda using a GMANOVA-MANOVA model and repeated surveys","authors":"Felix Wamono, Leonard Atuhaire, Innocent Ngaruye, Dietrich von Rosen, Martin Singull","doi":"10.1080/23737484.2023.2266714","DOIUrl":"https://doi.org/10.1080/23737484.2023.2266714","url":null,"abstract":"AbstractPanel survey data from Uganda, as well as data from the 2014 Uganda Population and Housing Census have been analyzed. The Growth Curve model with rank restrictions on parameters was used to estimate the small area means. The aim of the analysis was to assess change over time in household living standards (welfare), i.e., to investigate whether households display growth in living standards? whether households grow at the same rates? and whether households in different geographical areas of the country grow at the same rates? Using a GMANOVA-MANOVA model with rank restrictions on parameters, it was established that growth in household standards of living in Uganda varied across small areas. Sub-regions (small areas) with the highest standards of living in Uganda at the endline were Central Urban region, Kampala Urban region and South Western Urban region, while the sub-regions with the lowest standards of living at the endline were North East Rural region, North East Urban region and Eastern Rural region. The sub-regions with the highest growth rates in standards of living were Mid West Urban region, Mid North Rural region, and South Western Urban region. The sub-regions with the highest decline in standards of living were East Central Rural region, East Rural region and West Nile Urban region.KEYWORDS: Extended growth curve modelrank restrictions on parametersrepeated surveyssmall area estimation AcknowledgmentsWe thank the Uganda Bureau of Statistics for providing all the datasets that were used in this article. We thank the editors and reviewers for the insightful comments.Additional informationFundingThe research of Felix Wamono is supported by the Swedish International Development and Cooperation Agency (Sida) in collaboration with Makerere University, under Sida-Makerere University Cooperation Agreement, Project 316. Dietrich von Rosen is supported by the Swedish Research Council (2017-03003).","PeriodicalId":36561,"journal":{"name":"Communications in Statistics Case Studies Data Analysis and Applications","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136294032","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 : 2023-09-04DOI: 10.1080/23737484.2023.2249850
S. Dey, Mahendra Saha, Chanseok Park
{"title":"Applications of a new loss and cost-based process capability index to electronic industries","authors":"S. Dey, Mahendra Saha, Chanseok Park","doi":"10.1080/23737484.2023.2249850","DOIUrl":"https://doi.org/10.1080/23737484.2023.2249850","url":null,"abstract":"","PeriodicalId":36561,"journal":{"name":"Communications in Statistics Case Studies Data Analysis and Applications","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78573223","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 : 2023-07-04DOI: 10.1080/23737484.2023.2230483
T. Tsiampalis, A. Faka, C. Chalkias, Demosthenes Panagiotakos
{"title":"A methodological framework for imputing missing spatial data at an aggregate level and guaranteeing data privacy: the AFFINITY method; implementation in the context of the official spatial Greek census data","authors":"T. Tsiampalis, A. Faka, C. Chalkias, Demosthenes Panagiotakos","doi":"10.1080/23737484.2023.2230483","DOIUrl":"https://doi.org/10.1080/23737484.2023.2230483","url":null,"abstract":"","PeriodicalId":36561,"journal":{"name":"Communications in Statistics Case Studies Data Analysis and Applications","volume":"313 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76268603","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 : 2023-06-26DOI: 10.1080/23737484.2023.2194040
D. Bilgili, Mayssam Naji, E. Erdem
Abstract In nanotechnology, the size and shape control of nanoparticles is crucial as their properties are highly dependent on their morphology. This obliges researchers to work on obtaining uniform size and shape distribution in synthesized particles so that their electrical, optical, and magnetic properties remain uniform within the same batch. This brings the problem of how to quantify the shape and size of nanoparticles in the most accurate way. The most common way to determine size distribution is using UV-vis spectrometry; however, this method disregards the existence of agglomerated particles and may sometimes give an incorrect measurement. Therefore a technique that can obtain this data directly from the transmission electron microscopy (TEM) images of particles would be more reliable as it would capture data for every single particle in the batch. In a classical statistical sense, our interest is the shape and size distribution of nanoparticles. In this paper, we quantify the size and shape of nanoparticles using the statistical techniques applied on TEM images of particles: Functional Data and Shape Analysis. By using this shape theory we obtain the geodesic distances not only between the particles but also among the frames. We further cluster the nanoparticles in terms of their similarities. We want to emphasize the importance of the deformation and how it is done of one nanoparticle onto another.
{"title":"Size and shape analysis of silica (SiO2) and gold (Au) nanoparticles","authors":"D. Bilgili, Mayssam Naji, E. Erdem","doi":"10.1080/23737484.2023.2194040","DOIUrl":"https://doi.org/10.1080/23737484.2023.2194040","url":null,"abstract":"Abstract In nanotechnology, the size and shape control of nanoparticles is crucial as their properties are highly dependent on their morphology. This obliges researchers to work on obtaining uniform size and shape distribution in synthesized particles so that their electrical, optical, and magnetic properties remain uniform within the same batch. This brings the problem of how to quantify the shape and size of nanoparticles in the most accurate way. The most common way to determine size distribution is using UV-vis spectrometry; however, this method disregards the existence of agglomerated particles and may sometimes give an incorrect measurement. Therefore a technique that can obtain this data directly from the transmission electron microscopy (TEM) images of particles would be more reliable as it would capture data for every single particle in the batch. In a classical statistical sense, our interest is the shape and size distribution of nanoparticles. In this paper, we quantify the size and shape of nanoparticles using the statistical techniques applied on TEM images of particles: Functional Data and Shape Analysis. By using this shape theory we obtain the geodesic distances not only between the particles but also among the frames. We further cluster the nanoparticles in terms of their similarities. We want to emphasize the importance of the deformation and how it is done of one nanoparticle onto another.","PeriodicalId":36561,"journal":{"name":"Communications in Statistics Case Studies Data Analysis and Applications","volume":"36 1","pages":"219 - 233"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84921722","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 : 2023-06-13DOI: 10.1080/23737484.2023.2220158
Iliana M. Paternina-Ortega, E. Castaño‐Tostado, M. Santana-Cibrian
Abstract Shelf life of a food product is a time threshold that determines when it no longer retains its quality. It can be estimated using data coming from sensory experiments where potential consumers taste aged portions of the product and state whether they will consume the product or not. The raw data obtained from the experiment consist of a binary sequence for each assessor. The standard approach to analyze this type of data is based on reliability theory and requires the coding of the raw data into censored intervals. This paper discusses how the aforementioned coding yields a loss of information and low coverage of confidence intervals. Furthermore, the paper introduces an alternative methodology to estimate shelf life based on longitudinal data theory. This methodology prevents loss of information, quantifies the effect of inconsistent responses, explicitly incorporates and diagnoses the correlation structure of the consumer’s responses, and provides an easier interpretation of the results for the final user. These features are shown using real data coming from sensory experiments.
{"title":"A longitudinal data analysis approach for sensory shelf life estimation","authors":"Iliana M. Paternina-Ortega, E. Castaño‐Tostado, M. Santana-Cibrian","doi":"10.1080/23737484.2023.2220158","DOIUrl":"https://doi.org/10.1080/23737484.2023.2220158","url":null,"abstract":"Abstract Shelf life of a food product is a time threshold that determines when it no longer retains its quality. It can be estimated using data coming from sensory experiments where potential consumers taste aged portions of the product and state whether they will consume the product or not. The raw data obtained from the experiment consist of a binary sequence for each assessor. The standard approach to analyze this type of data is based on reliability theory and requires the coding of the raw data into censored intervals. This paper discusses how the aforementioned coding yields a loss of information and low coverage of confidence intervals. Furthermore, the paper introduces an alternative methodology to estimate shelf life based on longitudinal data theory. This methodology prevents loss of information, quantifies the effect of inconsistent responses, explicitly incorporates and diagnoses the correlation structure of the consumer’s responses, and provides an easier interpretation of the results for the final user. These features are shown using real data coming from sensory experiments.","PeriodicalId":36561,"journal":{"name":"Communications in Statistics Case Studies Data Analysis and Applications","volume":"2 1","pages":"287 - 301"},"PeriodicalIF":0.0,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88390836","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 : 2023-06-06DOI: 10.1080/23737484.2023.2219223
Mahendra Saha, Anju Devi, Pratibha Pareek
Abstract In process capability indices, the generalized confidence interval (GCI) method has been used many times in several research articles. In this article, we have considered the supplier’s selection problems by using the GCI of difference between two process capability indices . We have considered two classical methods of estimation, viz. maximum likelihood estimation (MLE), and maximum product spacing estimation (MPSE) to estimate . By using Monte Carlo simulation, we have obtained the biases and corresponding mean squared errors (MSEs) of . Here, we also find the lower confidence limit (L), upper confidence limit (U), and their corresponding average width (AW) for both classical methods MLE and MPSE. Three real data sets have been reanalyzed to illustrate the methodology of the supplier’s selection problem by utilizing the generalized confidence interval method.
{"title":"Applications of process capability indices for suppliers selection problems using generalized confidence interval","authors":"Mahendra Saha, Anju Devi, Pratibha Pareek","doi":"10.1080/23737484.2023.2219223","DOIUrl":"https://doi.org/10.1080/23737484.2023.2219223","url":null,"abstract":"Abstract In process capability indices, the generalized confidence interval (GCI) method has been used many times in several research articles. In this article, we have considered the supplier’s selection problems by using the GCI of difference between two process capability indices . We have considered two classical methods of estimation, viz. maximum likelihood estimation (MLE), and maximum product spacing estimation (MPSE) to estimate . By using Monte Carlo simulation, we have obtained the biases and corresponding mean squared errors (MSEs) of . Here, we also find the lower confidence limit (L), upper confidence limit (U), and their corresponding average width (AW) for both classical methods MLE and MPSE. Three real data sets have been reanalyzed to illustrate the methodology of the supplier’s selection problem by utilizing the generalized confidence interval method.","PeriodicalId":36561,"journal":{"name":"Communications in Statistics Case Studies Data Analysis and Applications","volume":"19 1","pages":"270 - 286"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83458437","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 : 2023-05-11DOI: 10.1080/23737484.2023.2208736
B. R. Brum, O. C. N. Pereira, C. M. Silva, I. Previdelli
Abstract The framework that permeates joint modeling has great appeal for biological interpretation, in addition to allowing the quantification of the contribution of the longitudinal biomarker in survival. This effect on the risk function is incorporated by the original variability of the observations made over time. In addition, this methodology provides more efficient estimates for the effect of the groups under a longitudinal aspect and with less bias in the survival model. A joint model is proposed in this research, adopting a mixed multivariate normal model for the longitudinal response and the relative risk model for survival. Our sample is composed of HIV carriers exposed to cART, characterized in groups: control (HIV), and co-infections with HBV and HCV. The joint distribution of the biomarker count, the square root of CD4, was modeled in relation to the start time of cART until the CD4:CD8 ratio = 0.9 was reached. The model selected, by testing the likelihood ratio, showed greater severity in HBV co-infections. The association was significant, indicating that the biomarker and risk of CD4:CD8 ratio = 0.9 should be analyzed together. This result corroborates with clinical evidence that points to this possible relationship and is supported by the residual analysis, which describes the good adequacy of the model.
{"title":"Joint model applied in a cohort study on HIV carriers coinfected with HBV and HCV","authors":"B. R. Brum, O. C. N. Pereira, C. M. Silva, I. Previdelli","doi":"10.1080/23737484.2023.2208736","DOIUrl":"https://doi.org/10.1080/23737484.2023.2208736","url":null,"abstract":"Abstract The framework that permeates joint modeling has great appeal for biological interpretation, in addition to allowing the quantification of the contribution of the longitudinal biomarker in survival. This effect on the risk function is incorporated by the original variability of the observations made over time. In addition, this methodology provides more efficient estimates for the effect of the groups under a longitudinal aspect and with less bias in the survival model. A joint model is proposed in this research, adopting a mixed multivariate normal model for the longitudinal response and the relative risk model for survival. Our sample is composed of HIV carriers exposed to cART, characterized in groups: control (HIV), and co-infections with HBV and HCV. The joint distribution of the biomarker count, the square root of CD4, was modeled in relation to the start time of cART until the CD4:CD8 ratio = 0.9 was reached. The model selected, by testing the likelihood ratio, showed greater severity in HBV co-infections. The association was significant, indicating that the biomarker and risk of CD4:CD8 ratio = 0.9 should be analyzed together. This result corroborates with clinical evidence that points to this possible relationship and is supported by the residual analysis, which describes the good adequacy of the model.","PeriodicalId":36561,"journal":{"name":"Communications in Statistics Case Studies Data Analysis and Applications","volume":"28 1","pages":"302 - 320"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84369049","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 : 2023-05-11DOI: 10.1080/23737484.2023.2208737
Antonios Psathas, Dionysia Rallatou, M. Tsagris
Abstract Skin tone discrimination in professional basketball leagues, and more specifically at the NBA, has been the major concern of many researchers who have provided evidence of its existence. The question of interest is whether it is still present. We revisit this question by applying various linear and complex non-linear, univariate, and multivariate, hypothesis testing procedures to numerous players’ performance statistics and their salaries for three seasons, from 2017 up to 2019. The nonlinear procedures are a key differentiation from previous studies as they may reveal patterns non-detectable by linear models. No evidence of discrimination, based on skin tone, was found. Strong evidence is provided that the skin tone of NBA players is not related to their performance statistics on the court nor is it a factor of salary discrimination.
{"title":"Skin tone of NBA players and performance statistics. Is there a relationship?","authors":"Antonios Psathas, Dionysia Rallatou, M. Tsagris","doi":"10.1080/23737484.2023.2208737","DOIUrl":"https://doi.org/10.1080/23737484.2023.2208737","url":null,"abstract":"Abstract Skin tone discrimination in professional basketball leagues, and more specifically at the NBA, has been the major concern of many researchers who have provided evidence of its existence. The question of interest is whether it is still present. We revisit this question by applying various linear and complex non-linear, univariate, and multivariate, hypothesis testing procedures to numerous players’ performance statistics and their salaries for three seasons, from 2017 up to 2019. The nonlinear procedures are a key differentiation from previous studies as they may reveal patterns non-detectable by linear models. No evidence of discrimination, based on skin tone, was found. Strong evidence is provided that the skin tone of NBA players is not related to their performance statistics on the court nor is it a factor of salary discrimination.","PeriodicalId":36561,"journal":{"name":"Communications in Statistics Case Studies Data Analysis and Applications","volume":"9 1","pages":"234 - 251"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81922055","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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