Journal of data science : JDS最新文献_第9页

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

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

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

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

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

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

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

Adapted Autoregressive Model and Volatility Model with Application 自适应自回归模型与波动率模型及其应用

Journal of data science : JDS Pub Date : 2021-07-30 DOI: 10.6339/JDS.2013.11(4).1165

Naisheng Wang, Yan Lu

引用次数: 1

A New Procedure of Clustering Based on Multivariate Outlier Detection 一种基于多变量异常值检测的聚类新方法

Journal of data science : JDS Pub Date : 2021-07-30 DOI: 10.6339/JDS.2013.11(1).1091

Grégory David, S. Jayakumar, B. Thomas

{"title":"A New Procedure of Clustering Based on Multivariate Outlier Detection","authors":"Grégory David, S. Jayakumar, B. Thomas","doi":"10.6339/JDS.2013.11(1).1091","DOIUrl":"https://doi.org/10.6339/JDS.2013.11(1).1091","url":null,"abstract":"Clustering is an extremely important task in a wide variety of ap- plication domains especially in management and social science research. In this paper, an iterative procedure of clustering method based on multivariate outlier detection was proposed by using the famous Mahalanobis distance. At rst, Mahalanobis distance should be calculated for the entire sample, then using T 2 -statistic x a UCL. Above the UCL are treated as outliers which are grouped as outlier cluster and repeat the same procedure for the remaining inliers, until the variance-covariance matrix for the variables in the last cluster achieved singularity. At each iteration, multivariate test of mean used to check the discrimination between the outlier clusters and the inliers. Moreover, multivariate control charts also used to graphically visual- izes the iterations and outlier clustering process. Finally multivariate test of means helps to rmly establish the cluster discrimination and validity. This paper employed this procedure for clustering 275 customers of a famous two- wheeler in India based on 19 dierent attributes of the two wheeler and its company. The result of the proposed technique conrms there exist 5 and 7 outlier clusters of customers in the entire sample at 5% and 1% signicance level respectively.","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":"49148312","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: 22