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Bonus-Malus System in Iran: An Empirical Evaluation 伊朗奖惩制度:一个实证评价
Journal of data science : JDS Pub Date : 2021-07-30 DOI: 10.6339/JDS.2013.11(1).1098
R. Mahmoudvand, A. Edalati, Farhad Shokoohi
{"title":"Bonus-Malus System in Iran: An Empirical Evaluation","authors":"R. Mahmoudvand, A. Edalati, Farhad Shokoohi","doi":"10.6339/JDS.2013.11(1).1098","DOIUrl":"https://doi.org/10.6339/JDS.2013.11(1).1098","url":null,"abstract":"The aim of this paper is to represent the Bonus-Malus System (BMS) of Iran, which is a mandatory scheme based on Insurance act num- ber 56. We examine the current Iranian BMS, using various criteria such as elasticity and time of convergence to steady state with respect to the claim frequency as well as nancial balance. We also nd the closed form of stationary distribution of the Iranian BMS that plays a key role in study of BMSs. Moreover, we compare the results with the German and Japan BMS. Finally we give some hints that can be used to improve the performance of the current Iranian BMS.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44922936","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}
引用次数: 1
Notes on Entropy for Concomitants of Record Values in Farlie-Gumbel-Morgenstern (FGM) Family 关于Farlie Gumbel-Morgenstern（FGM）家族中记录值伴随物的熵的注记
Journal of data science : JDS Pub Date : 2021-07-30 DOI: 10.6339/JDS.2013.11(1).1104
S. Tahmasebi
{"title":"Notes on Entropy for Concomitants of Record Values in Farlie-Gumbel-Morgenstern (FGM) Family","authors":"S. Tahmasebi","doi":"10.6339/JDS.2013.11(1).1104","DOIUrl":"https://doi.org/10.6339/JDS.2013.11(1).1104","url":null,"abstract":"Let {(Xi; Yi), i ≥ 1} be a sequence of bivariate random variables from a continuous distribution. If {R(subscript n), n ≥ 1} is the sequence of record values in the sequence of X's, then the Y which corresponds with the nth-record will be called the concomitant of the nth-record, denoted by R(subscript [n]). In FGM family, we determine the amount of information contained in R(subscript [n]) and compare it with amount of information given in R(subscript n). Also, we show that the Kullback-Leibler distance among the concomitants of record values is distribution-free. Finally, we provide some numerical results of mutual information and Pearson correlation coefficient for measuring the amount of dependency between R(subscript n) and R(subscript [n]) in the copula model of FGM family.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42626849","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}
引用次数: 0
Multiple Taxicab Correspondence Analysis of a Survey Related to Health Services 卫生服务相关调查的多出租车对应分析
Journal of data science : JDS Pub Date : 2021-07-30 DOI: 10.6339/JDS.2013.11(2).1113
V. Choulakian, J. Allard, B. Simonetti
{"title":"Multiple Taxicab Correspondence Analysis of a Survey Related to Health Services","authors":"V. Choulakian, J. Allard, B. Simonetti","doi":"10.6339/JDS.2013.11(2).1113","DOIUrl":"https://doi.org/10.6339/JDS.2013.11(2).1113","url":null,"abstract":"We present an analysis of a health survey data by multiple cor- respondence analysis (MCA) and multiple taxicab correspondence analysis (MTCA), MTCA being a robust L1 variant of MCA. The survey has one passive item, gender, and 22 active substantive items representing health services oered by municipal authorities; each active item has four answer categories: this service is used, never tried, tried with no access, non re- sponse. We show that the rst principal MTCA factor is perfectly charac- terized by the sum score of the category this service is used over all service items. Further, we prove that such a sum score characterization always exists for any survey data.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42197777","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}
引用次数: 5
On the Three-Parameter Weibull Distribution Shape Parameter Estimation 关于三参数威布尔分布形状参数估计
Journal of data science : JDS Pub Date : 2021-07-30 DOI: 10.6339/JDS.2013.11(3).1110
M. Teimouri, Arjun K. Gupta
{"title":"On the Three-Parameter Weibull Distribution Shape Parameter Estimation","authors":"M. Teimouri, Arjun K. Gupta","doi":"10.6339/JDS.2013.11(3).1110","DOIUrl":"https://doi.org/10.6339/JDS.2013.11(3).1110","url":null,"abstract":"The Weibull distribution has received much interest in reliability theory. The well-known maximum likelihood estimators (MLE) of this fam- ily are not available in closed form expression. In this work, we propose a consistent and closed form estimator for shape parameter of three-parameter Weibull distribution. Apart from high degree of performance, the derived estimator is location and scale-invariant.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45452764","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}
引用次数: 38
A Comparative Study of Shared Frailty Models for Kidney Infection Data with Generalized Exponential Baseline Distribution 具有广义指数基线分布的肾脏感染数据共享脆弱性模型的比较研究
Journal of data science : JDS Pub Date : 2021-07-30 DOI: 10.6339/JDS.2013.11(1).1126
David D. Hanagal, Alok D. Dabade
{"title":"A Comparative Study of Shared Frailty Models for Kidney Infection Data with Generalized Exponential Baseline Distribution","authors":"David D. Hanagal, Alok D. Dabade","doi":"10.6339/JDS.2013.11(1).1126","DOIUrl":"https://doi.org/10.6339/JDS.2013.11(1).1126","url":null,"abstract":"Shared frailty models are often used to model heterogeneity in survival analysis. The most common shared frailty model is a model in which hazard function is a product of random factor (frailty) and baseline hazard function which is common to all individuals. There are certain as- sumptions about the baseline distribution and distribution of frailty. Mostly assumption of gamma distribution is considered for frailty distribution. To compare the results with gamma frailty model, we introduce three shared frailty models with generalized exponential as baseline distribution. The other three shared frailty models are inverse Gaussian shared frailty model, compound Poisson shared frailty model and compound negative binomial shared frailty model. We t these models to a real life bivariate survival data set of McGilchrist and Aisbett (1991) related to kidney infection using Markov Chain Monte Carlo (MCMC) technique. Model comparison is made using Bayesian model selection criteria and a better model is suggested for the data.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41846796","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}
引用次数: 8
A Bayesian Analysis of the Spherical Distribution in Presence of Covariates 存在协变量时球形分布的贝叶斯分析
Journal of data science : JDS Pub Date : 2021-07-30 DOI: 10.6339/jds.201310_11(4).0008
J. Achcar, Gian Franco Napa, Roberto Molina de Souza
{"title":"A Bayesian Analysis of the Spherical Distribution in Presence of Covariates","authors":"J. Achcar, Gian Franco Napa, Roberto Molina de Souza","doi":"10.6339/jds.201310_11(4).0008","DOIUrl":"https://doi.org/10.6339/jds.201310_11(4).0008","url":null,"abstract":"In this paper we introduce a Bayesian analysis of a spherical distribution applied to rock joint orientation data in presence or not of a vector of covariates, where the response variable is given by the angle from the mean and the covariates are the components of the normal upwards vector. Standard simulation MCMC (Markov Chain Monte Carlo) methods have been used to obtain the posterior summaries of interest obtained from WinBugs software. Illustration of the proposed methodology are given using a simulated data set and a real rock spherical data set from a hydroelectrical site.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42477547","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}
引用次数: 0
Nonparametric Assessment of Aftershock Clusters of the Maule Earthquake Mw = 8.8 毛勒8.8级地震余震群的非参数评估
Journal of data science : JDS Pub Date : 2021-07-30 DOI: 10.6339/jds.201310_11(4).0001
Javier E. Contreras-Reyes
{"title":"Nonparametric Assessment of Aftershock Clusters of the Maule Earthquake Mw = 8.8","authors":"Javier E. Contreras-Reyes","doi":"10.6339/jds.201310_11(4).0001","DOIUrl":"https://doi.org/10.6339/jds.201310_11(4).0001","url":null,"abstract":"so Abstract: We study the spatial distribution of clusters associated to the aftershocks of the megathrust Maule earthquake MW 8.8 of 27 February 2010. We used a recent clustering method which hinges on a nonparametric estimation of the underlying probability density function to detect subsets of points forming clusters associated with high density areas. In addition, we estimate the probability density function using a nonparametric kernel method for each of these clusters. This allows us to identify a set of regions where there is an association between frequency of events and coseismic slip. Our results suggest that high coseismic slip is spatially related to high aftershock frequency.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44937847","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}
引用次数: 3
On Choosing a Mixture Model for Clustering 关于聚类的混合模型选择
Journal of data science : JDS Pub Date : 2021-07-30 DOI: 10.6339/JDS.2013.11(1).1135
J. Ngatchou-Wandji, J. Bulla, E. Lorraine
{"title":"On Choosing a Mixture Model for Clustering","authors":"J. Ngatchou-Wandji, J. Bulla, E. Lorraine","doi":"10.6339/JDS.2013.11(1).1135","DOIUrl":"https://doi.org/10.6339/JDS.2013.11(1).1135","url":null,"abstract":"2 Universit e de Caen Abstract: Two methods for clustering data and choosing a mixture model are proposed. First, we derive a new classication algorithm based on the classication likelihood. Then, the likelihood conditional on these clusters is written as the product of likelihoods of each cluster, and AIC- respectively BIC-type approximations are applied. The resulting criteria turn out to be the sum of the AIC or BIC relative to each cluster plus an entropy term. The performance of our methods is evaluated by Monte-Carlo methods and on a real data set, showing in particular that the iterative estimation algorithm converges quickly in general, and thus the computational load is rather low.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43648301","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}
引用次数: 3
The Log-Kumaraswamy Generalized Gamma Regression Model with Application to Chemical Dependency Data LogKumaraswamy广义伽玛回归模型及其在化学依赖数据中的应用
Journal of data science : JDS Pub Date : 2021-07-30 DOI: 10.6339/JDS.2013.11(4).1131
Marcelino A. R. Pascoa, Claudia M. M. de Paiva, G. Cordeiro, E. Ortega
{"title":"The Log-Kumaraswamy Generalized Gamma Regression Model with Application to Chemical Dependency Data","authors":"Marcelino A. R. Pascoa, Claudia M. M. de Paiva, G. Cordeiro, E. Ortega","doi":"10.6339/JDS.2013.11(4).1131","DOIUrl":"https://doi.org/10.6339/JDS.2013.11(4).1131","url":null,"abstract":"The ve parameter Kumaraswamy generalized gamma model (Pas- coa et al., 2011) includes some important distributions as special cases and it is very useful for modeling lifetime data. We propose an extended version of this distribution by assuming that a shape parameter can take negative values. The new distribution can accommodate increasing, decreasing, bath- tub and unimodal shaped hazard functions. A second advantage is that it also includes as special models reciprocal distributions such as the recipro- cal gamma and reciprocal Weibull distributions. A third advantage is that it can represent the error distribution for the log-Kumaraswamy general- ized gamma regression model. We provide a mathematical treatment of the new distribution including explicit expressions for moments, generating function, mean deviations and order statistics. We obtain the moments of the log-transformed distribution. The new regression model can be used more eectively in the analysis of survival data since it includes as sub- models several widely-known regression models. The method of maximum likelihood and a Bayesian procedure are used for estimating the model pa- rameters for censored data. Overall, the new regression model is very useful to the analysis of real data.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42116600","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}
引用次数: 5
Robust Methods in Event Studies: Empirical Evidence and Theoretical Implications 事件研究中的稳健方法:经验证据和理论意义
Journal of data science : JDS Pub Date : 2021-07-30 DOI: 10.6339/JDS.2013.11(3).1166
N. Sorokina, David E. Booth, John H. Thornton
{"title":"Robust Methods in Event Studies: Empirical Evidence and Theoretical Implications","authors":"N. Sorokina, David E. Booth, John H. Thornton","doi":"10.6339/JDS.2013.11(3).1166","DOIUrl":"https://doi.org/10.6339/JDS.2013.11(3).1166","url":null,"abstract":"We apply methodology robust to outliers to an existing event study of the eect of U.S. nancial reform on the stock markets of the 10 largest world economies, and obtain results that dier from the original OLS results in important ways. This nding underlines the importance of han- dling outliers in event studies. We further review closely the population of outliers identied using Cook's distance and nd that many of the out- liers lie within the event windows. We acknowledge that those data points lead to inaccurate regression tting; however, we cannot remove them since they carry valuable information regarding the event eect. We study further the residuals of the outliers within event windows and nd that the resid- uals change with application of M-estimators and MM-estimators; in most cases they became larger, meaning the main prediction equation is pulled back towards the main data population and further from the outliers and indicating more proper tting. We support our empirical results by pseudo- simulation experiments and nd signicant improvement in determination of both types of the event eect abnormal returns and change in systematic risk. We conclude that robust methods are important for obtaining accurate measurement of event eects in event studies.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47109351","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}
引用次数: 23
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