Pub Date : 2020-10-14DOI: 10.1285/I20705948V13N2P536
Hazem Al-Mofleh, M. Elgarhy, A. Afify, Mohammad Zannon
A new family of distributions called type II exponentiated half logistic is introduced and studied. Four new special models are presented. Some mathematical properties of the new family are studied. Explicit expressions for the moments, probability weighted moments, quantile function, mean deviation, order statistics and Renyi entropy are investigated. Parameter estimation of the family are obtained based on maximum likelihood procedure. Two real data sets are employed to show the usefulness of the new family.
{"title":"Type II Exponentiated Half Logistic Generated Family of Distributions with Applications","authors":"Hazem Al-Mofleh, M. Elgarhy, A. Afify, Mohammad Zannon","doi":"10.1285/I20705948V13N2P536","DOIUrl":"https://doi.org/10.1285/I20705948V13N2P536","url":null,"abstract":"A new family of distributions called type II exponentiated half logistic is introduced and studied. Four new special models are presented. Some mathematical properties of the new family are studied. Explicit expressions for the moments, probability weighted moments, quantile function, mean deviation, order statistics and Renyi entropy are investigated. Parameter estimation of the family are obtained based on maximum likelihood procedure. Two real data sets are employed to show the usefulness of the new family.","PeriodicalId":44770,"journal":{"name":"Electronic Journal of Applied Statistical Analysis","volume":"13 1","pages":"536-561"},"PeriodicalIF":0.7,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1285/I20705948V13N2P536","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44580084","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 : 2020-10-14DOI: 10.1285/I20705948V13N2P519
Marica Manisera, P. Zuccolotto, E. Brentari
In consumer research, marketing, public policy and other fields, individ- uals’ choice depends on the number of possible alternatives. In addition, according to the literature, the choice satisfaction is influenced not only by the number of options but also by the perceived variety. The aim of the present study is to apply a novel approach to model perceived variety, in or- der to better understand the perceptions of individuals about the variety of the possible choice options and to model the impact of perceived variety and individuals’ characteristics on the choice outcome satisfaction. We resort to the class of cub (Combination of Uniform and Binomial random variables) models for rating data that model the respondents’ decision process as a combination of two latent components, called feeling and uncertainty , that express, respectively, the level of agreement with the item being evaluated and the human indecision surrounding any discrete choice. The model ap- plied in this paper is an alternative to the most common models used in the studies of human judgments and decisions, whenever attitudes, perceptions and opinions are measured by means of questionnaires having questions with ordered response categories. The chosen approach is composed of two steps: (1) we construct measures of feeling and uncertainty of perceived variety by means of cub and (2) we investigate their impact (eventually together with personal characteristics) on choice satisfaction. The R FastCUB package is exploited to select the best set of covariates to include in the final model.
{"title":"How perceived variety impacts on choice satisfaction: a two-step approach using the CUB class of models and best-subset variable selection","authors":"Marica Manisera, P. Zuccolotto, E. Brentari","doi":"10.1285/I20705948V13N2P519","DOIUrl":"https://doi.org/10.1285/I20705948V13N2P519","url":null,"abstract":"In consumer research, marketing, public policy and other fields, individ- uals’ choice depends on the number of possible alternatives. In addition, according to the literature, the choice satisfaction is influenced not only by the number of options but also by the perceived variety. The aim of the present study is to apply a novel approach to model perceived variety, in or- der to better understand the perceptions of individuals about the variety of the possible choice options and to model the impact of perceived variety and individuals’ characteristics on the choice outcome satisfaction. We resort to the class of cub (Combination of Uniform and Binomial random variables) models for rating data that model the respondents’ decision process as a combination of two latent components, called feeling and uncertainty , that express, respectively, the level of agreement with the item being evaluated and the human indecision surrounding any discrete choice. The model ap- plied in this paper is an alternative to the most common models used in the studies of human judgments and decisions, whenever attitudes, perceptions and opinions are measured by means of questionnaires having questions with ordered response categories. The chosen approach is composed of two steps: (1) we construct measures of feeling and uncertainty of perceived variety by means of cub and (2) we investigate their impact (eventually together with personal characteristics) on choice satisfaction. The R FastCUB package is exploited to select the best set of covariates to include in the final model.","PeriodicalId":44770,"journal":{"name":"Electronic Journal of Applied Statistical Analysis","volume":"13 1","pages":"519-535"},"PeriodicalIF":0.7,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41658184","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 : 2020-10-14DOI: 10.1285/I20705948V13N2P436
F. Llobell, E. Qannari
The STATIS method is one of many strategies of analysis devoted to the unsupervised analysis of multiblock data. A new optimization criterion to define this method of analysis is introduced and an extension to the cluster analysis of several blocks of variables is discussed. This consists in a hierarchical cluster analysis and a partitioning algorithm akin to the K-means algorithm. Moreover, in order to improve the cluster analysis outcomes, an additional cluster called noise cluster which contains atypical blocks of variables is introduced. The general strategy of analysis is illustrated by means of two cases studies.
{"title":"CLUSTATIS: cluster analysis of blocks of variables","authors":"F. Llobell, E. Qannari","doi":"10.1285/I20705948V13N2P436","DOIUrl":"https://doi.org/10.1285/I20705948V13N2P436","url":null,"abstract":"The STATIS method is one of many strategies of analysis devoted to the unsupervised analysis of multiblock data. A new optimization criterion to define this method of analysis is introduced and an extension to the cluster analysis of several blocks of variables is discussed. This consists in a hierarchical cluster analysis and a partitioning algorithm akin to the K-means algorithm. Moreover, in order to improve the cluster analysis outcomes, an additional cluster called noise cluster which contains atypical blocks of variables is introduced. The general strategy of analysis is illustrated by means of two cases studies.","PeriodicalId":44770,"journal":{"name":"Electronic Journal of Applied Statistical Analysis","volume":"13 1","pages":"436-453"},"PeriodicalIF":0.7,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41567785","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 : 2020-10-14DOI: 10.1285/I20705948V13N2P350
S. W. Mahmood, Noor Nawzat Seyala, Z. Algamal
R 2 measure, which named coefficient of determination, is usually used as tools for evaluation the predictive power of the regression models. However, this measure, which is based on deviance for generalized linear models, is sensitive to the small samples. Therefore, it is necessary to adjust R 2 measure according to the number of covariates. Beta regression model has received much attention in several science fields in modeling proportions or rates data. In this paper, several adjusted R 2 measures are proposed in beta regression models. The performance of the proposed measures is evaluated through simulation and real data application. Results demonstrate the superiority of the proposed measures compared to others.
{"title":"Adjusted R2 - type measures for beta regression model","authors":"S. W. Mahmood, Noor Nawzat Seyala, Z. Algamal","doi":"10.1285/I20705948V13N2P350","DOIUrl":"https://doi.org/10.1285/I20705948V13N2P350","url":null,"abstract":"R 2 measure, which named coefficient of determination, is usually used as tools for evaluation the predictive power of the regression models. However, this measure, which is based on deviance for generalized linear models, is sensitive to the small samples. Therefore, it is necessary to adjust R 2 measure according to the number of covariates. Beta regression model has received much attention in several science fields in modeling proportions or rates data. In this paper, several adjusted R 2 measures are proposed in beta regression models. The performance of the proposed measures is evaluated through simulation and real data application. Results demonstrate the superiority of the proposed measures compared to others.","PeriodicalId":44770,"journal":{"name":"Electronic Journal of Applied Statistical Analysis","volume":"13 1","pages":"350-357"},"PeriodicalIF":0.7,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42487661","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 : 2020-10-14DOI: 10.1285/I20705948V13N2P562
B. Albadareen, N. Ismail, Omar M. Eidous, Jamil J. Jaber
The non-parametric kernel density estimation is used in practice to estimate population abundance using the line transect sampling. In general, the classical kernel estimator of f(0) tends to be underestimated. In this article, a shifted logarithmic transformation of perpendicular distance is proposed for the kernel estimator when the shoulder condition is violated. Mathematically, the proposed estimator is more efficient than the classical kernel estimator. A simulation study is also carried out to compare the performance of the proposed estimators and the classical kernel estimators.
{"title":"A non-parametric density estimate adaptation for population abundance when the shoulder condition is violated","authors":"B. Albadareen, N. Ismail, Omar M. Eidous, Jamil J. Jaber","doi":"10.1285/I20705948V13N2P562","DOIUrl":"https://doi.org/10.1285/I20705948V13N2P562","url":null,"abstract":"The non-parametric kernel density estimation is used in practice to estimate population abundance using the line transect sampling. In general, the classical kernel estimator of f(0) tends to be underestimated. In this article, a shifted logarithmic transformation of perpendicular distance is proposed for the kernel estimator when the shoulder condition is violated. Mathematically, the proposed estimator is more efficient than the classical kernel estimator. A simulation study is also carried out to compare the performance of the proposed estimators and the classical kernel estimators.","PeriodicalId":44770,"journal":{"name":"Electronic Journal of Applied Statistical Analysis","volume":"13 1","pages":"562-579"},"PeriodicalIF":0.7,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66340411","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 : 2020-10-14DOI: 10.1285/I20705948V13N2P474
G. Contu, L. Frigau, F. Mola, Maurizio Romano, C. Conversano
We investigate if Erasmus mobility has a positive impact on the career of students. The focus is on graduation bonus, the difference between the final degree grade and the base degree score computed before graduation, that depends from the average mark obtained at the exams. Observing students graduated at the university of Cagliari, Italy, graduation bonus is modeled as a function of other student-specific variables concerning characteristics of students, academic performance and international mobility. The statistical analysis is framed within the case-control studies and utilizes model averaging to obtain robust results. The same approach is used to evaluate the effectiveness of different Erasmus programs through post-hoc tests. Results document the effect of international mobility on the graduation bonus is context-specific as it depends on the faculty and the type of degree a student is enrolled. Moreover, the positive effect of international mobility is more evident for the Erasmus Studio program.
{"title":"University student achievements and international mobility: The case of University of Cagliari","authors":"G. Contu, L. Frigau, F. Mola, Maurizio Romano, C. Conversano","doi":"10.1285/I20705948V13N2P474","DOIUrl":"https://doi.org/10.1285/I20705948V13N2P474","url":null,"abstract":"We investigate if Erasmus mobility has a positive impact on the career of students. The focus is on graduation bonus, the difference between the final degree grade and the base degree score computed before graduation, that depends from the average mark obtained at the exams. Observing students graduated at the university of Cagliari, Italy, graduation bonus is modeled as a function of other student-specific variables concerning characteristics of students, academic performance and international mobility. The statistical analysis is framed within the case-control studies and utilizes model averaging to obtain robust results. The same approach is used to evaluate the effectiveness of different Erasmus programs through post-hoc tests. Results document the effect of international mobility on the graduation bonus is context-specific as it depends on the faculty and the type of degree a student is enrolled. Moreover, the positive effect of international mobility is more evident for the Erasmus Studio program.","PeriodicalId":44770,"journal":{"name":"Electronic Journal of Applied Statistical Analysis","volume":"13 1","pages":"474-497"},"PeriodicalIF":0.7,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42126222","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 : 2020-10-14DOI: 10.1285/I20705948V13N2P358
Giuseppe Pandolfo
The notion of the interpoint depth is applied to spherical spaces by us-ing an appropriate angular distance function for data lying on the surfaceof the unit hypersphere. The traditional multivariate methods, indeed, arenot suitable for the analysis of directional data and this holds true also fordistance measures and related depth based methods. The interpoint depthfor directional data possesses some nice properties and can be used for highdimensional data analysis. This notion of depth is particularly useful toinvestigate local features of distribution such as multimodality and can beexploited to deal with many statistical problems. The behavior of the pro-posed depth is investigated by means of simulated data. In addition threeinteresting applications are presented.
{"title":"The interpoint depth for directional data","authors":"Giuseppe Pandolfo","doi":"10.1285/I20705948V13N2P358","DOIUrl":"https://doi.org/10.1285/I20705948V13N2P358","url":null,"abstract":"The notion of the interpoint depth is applied to spherical spaces by us-ing an appropriate angular distance function for data lying on the surfaceof the unit hypersphere. The traditional multivariate methods, indeed, arenot suitable for the analysis of directional data and this holds true also fordistance measures and related depth based methods. The interpoint depthfor directional data possesses some nice properties and can be used for highdimensional data analysis. This notion of depth is particularly useful toinvestigate local features of distribution such as multimodality and can beexploited to deal with many statistical problems. The behavior of the pro-posed depth is investigated by means of simulated data. In addition threeinteresting applications are presented.","PeriodicalId":44770,"journal":{"name":"Electronic Journal of Applied Statistical Analysis","volume":"13 1","pages":"358-374"},"PeriodicalIF":0.7,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43697991","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 : 2020-10-14DOI: 10.1285/I20705948V13N2P454
M. Migliorati
This paper aims to detect which are the drivers leading to victory for basketball matches in NBA, the American National Basketball Association. First games for regular seasons from 2004-2005 to 2017-2018 have been summarized in terms of box scores and Dean's four factors. Then box scores and four factors have been used as classication independent variables to identify victory drivers, focusing on Golden StateWarriors matches. Both CART and Random Forests machine learning techniques have been applied, and results are compared to assess the more suitable approach.
{"title":"Detecting drivers of basketball successful games: an exploratory study with machine learning algorithms","authors":"M. Migliorati","doi":"10.1285/I20705948V13N2P454","DOIUrl":"https://doi.org/10.1285/I20705948V13N2P454","url":null,"abstract":"This paper aims to detect which are the drivers leading to victory for basketball matches in NBA, the American National Basketball Association. First games for regular seasons from 2004-2005 to 2017-2018 have been summarized in terms of box scores and Dean's four factors. Then box scores and four factors have been used as classication independent variables to identify victory drivers, focusing on Golden StateWarriors matches. Both CART and Random Forests machine learning techniques have been applied, and results are compared to assess the more suitable approach.","PeriodicalId":44770,"journal":{"name":"Electronic Journal of Applied Statistical Analysis","volume":"13 1","pages":"454-473"},"PeriodicalIF":0.7,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1285/I20705948V13N2P454","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49548525","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 : 2020-10-14DOI: 10.1285/I20705948V13N2P580
Prayas Sharma, S. Khare, Rajesh Singh
: Use of suitable auxiliary information is always suggested in literature at the planning and estimation stage to make the estimators more powerful in terms of efficiency. Estimation using auxiliary information is common in sampling literature but using distribution of study and auxiliary information at the estimation stage is uncommon and useful specially when dealing with rare variable. This study utilizes the auxiliary information and Poisson distributed variates for proposing the log-type estimator and another generalized estimator for finite population mean under simple random sampling without replacement. The Mean Square Error expressions of the proposed estimators are obtained and mathematical conditions are established to prove the efficiency of proposed estimators. It is revealed from empirical (point estimation and interval estimation) & theoretical study that use of log type estimators along with suitable auxiliary information for Poisson distributed variates excels the performance of estimators in terms of efficiency.
{"title":"Estimation of Population Mean under logarithmic for the Poisson Distributed Study and Auxiliary Variates","authors":"Prayas Sharma, S. Khare, Rajesh Singh","doi":"10.1285/I20705948V13N2P580","DOIUrl":"https://doi.org/10.1285/I20705948V13N2P580","url":null,"abstract":": Use of suitable auxiliary information is always suggested in literature at the planning and estimation stage to make the estimators more powerful in terms of efficiency. Estimation using auxiliary information is common in sampling literature but using distribution of study and auxiliary information at the estimation stage is uncommon and useful specially when dealing with rare variable. This study utilizes the auxiliary information and Poisson distributed variates for proposing the log-type estimator and another generalized estimator for finite population mean under simple random sampling without replacement. The Mean Square Error expressions of the proposed estimators are obtained and mathematical conditions are established to prove the efficiency of proposed estimators. It is revealed from empirical (point estimation and interval estimation) & theoretical study that use of log type estimators along with suitable auxiliary information for Poisson distributed variates excels the performance of estimators in terms of efficiency.","PeriodicalId":44770,"journal":{"name":"Electronic Journal of Applied Statistical Analysis","volume":"13 1","pages":"580-588"},"PeriodicalIF":0.7,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1285/I20705948V13N2P580","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49319723","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 : 2020-10-14DOI: 10.1285/I20705948V13N2P413
M’barek Iaousse, Zouhair Elhadri, M. Hanafi, P. Dolce, Y. Elkettani
The present paper announces and demonstrates some useful properties of the impliedcorrelation matrix built by the Finite Iterative Method (Elhadri and Hanafi,2015, 2016; Elhadri et al., 2019) The most important property is that the impliedcorrelation matrix is affine for each of its parameters. In other words, the firstderivative with respect to each parameter does not depend on this parameter. Moreover,two properties affirm that the first and the second derivatives can be builtiteratively using the previous property. The final property shows that the secondderivatives with respect to every pair ofparameters in the same structural equationare null. These properties are very important in the sense that they can be used toconstruct a new computational approach to estimate recursive model parameters.These findings can be exploited in the estimation stage implementation, especiallyin the computation of the Newton Raphson algorithm to make the first and the secondderivatives of the discrepancy function more explicit and simplistic.
本文宣布并证明了有限迭代法建立的隐相关矩阵的一些有用性质（Elhadri和Hanafi，20152016；Elhadri et al.，2019）。最重要的性质是隐相关矩阵对其每个参数都是仿射的。换句话说，关于每个参数的一阶导数不取决于这个参数。此外，有两个性质证实了一阶导数和二阶导数可以使用前面的性质重复构建。最后的性质表明，对于同一结构方程中的每一对参数的二阶导数为零。这些性质非常重要，因为它们可以用来构建一种新的计算方法来估计递归模型参数。这些发现可以在估计阶段的实现中加以利用，特别是在Newton-Raphson算法的计算中，使差异函数的一阶和二阶导数更加明确和简单。
{"title":"Properties of the Correlation Matrix Implied by a Recursive Path Model using the Finite Iterative Method","authors":"M’barek Iaousse, Zouhair Elhadri, M. Hanafi, P. Dolce, Y. Elkettani","doi":"10.1285/I20705948V13N2P413","DOIUrl":"https://doi.org/10.1285/I20705948V13N2P413","url":null,"abstract":"The present paper announces and demonstrates some useful properties of the impliedcorrelation matrix built by the Finite Iterative Method (Elhadri and Hanafi,2015, 2016; Elhadri et al., 2019) The most important property is that the impliedcorrelation matrix is affine for each of its parameters. In other words, the firstderivative with respect to each parameter does not depend on this parameter. Moreover,two properties affirm that the first and the second derivatives can be builtiteratively using the previous property. The final property shows that the secondderivatives with respect to every pair ofparameters in the same structural equationare null. These properties are very important in the sense that they can be used toconstruct a new computational approach to estimate recursive model parameters.These findings can be exploited in the estimation stage implementation, especiallyin the computation of the Newton Raphson algorithm to make the first and the secondderivatives of the discrepancy function more explicit and simplistic.","PeriodicalId":44770,"journal":{"name":"Electronic Journal of Applied Statistical Analysis","volume":"24 9","pages":"413-435"},"PeriodicalIF":0.7,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41267513","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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