{"title":"The Ability of Artificial Neural Networks in Learning Dependency of Spatial Data","authors":"A. Tavasoli, Y. Waghei, A. Nazemi","doi":"10.52547/jsri.16.1.211","DOIUrl":"https://doi.org/10.52547/jsri.16.1.211","url":null,"abstract":"","PeriodicalId":422124,"journal":{"name":"Journal of Statistical Research of Iran","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124464714","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}
{"title":"Some New Results on the Preservation of Stochastic Orders and Aging Classes under Random Minima and Maxima","authors":"Ebrahim Salehi, Ezzatollah Gholami","doi":"10.52547/jsri.16.1.143","DOIUrl":"https://doi.org/10.52547/jsri.16.1.143","url":null,"abstract":"","PeriodicalId":422124,"journal":{"name":"Journal of Statistical Research of Iran","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117136745","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}
{"title":"A Quantile Approach to the Interval Shannon Entropy","authors":"M. Khorashadizadeh","doi":"10.29252/jsri.15.2.317","DOIUrl":"https://doi.org/10.29252/jsri.15.2.317","url":null,"abstract":"","PeriodicalId":422124,"journal":{"name":"Journal of Statistical Research of Iran","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115333606","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}
{"title":"On Properties of a Class of Bivariate FGM Type Distributions","authors":"Z. Sharifonnasabi, M. H. Alamatsaz, I. Kazemi","doi":"10.29252/jsri.15.2.300","DOIUrl":"https://doi.org/10.29252/jsri.15.2.300","url":null,"abstract":"","PeriodicalId":422124,"journal":{"name":"Journal of Statistical Research of Iran","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128817219","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}
A longitudinal study refers to collection of a response variable and possibly some explanatory variables at multiple follow-up times. In many clinical studies with longitudinal measurements, the response variable, for each patient is collected as long as an event of interest, which considered as clinical end point, occurs. Joint modeling of continuous longitudinal measurements and survival time is an approach for accounting association between two outcomes which frequently discussed in the literature, but design aspects of these models have been rarely considered. This paper uses a simulation-based method to determine the sample size from a Bayesian perspective. For this purpose, several Bayesian criteria for sample size determination are used, of which the most important one is the Bayesian power criterion (BPC), where the determined sample sizes are given based on BPC. We determine the sample size based on treatment effect on both outcomes (longitudinal measurements and survival time). The sample size determination is performed based on multiple hypotheses. Using several examples, the proposed Bayesian methods are illustrated and discussed. All the implementations are performed using R2OpenBUGS package and R 3.5.1 software.
{"title":"Bayesian Sample Size Determination for Joint Modeling of Longitudinal Measurements and Survival Data","authors":"T. Baghfalaki","doi":"10.29252/jsri.15.2.213","DOIUrl":"https://doi.org/10.29252/jsri.15.2.213","url":null,"abstract":"A longitudinal study refers to collection of a response variable and possibly some explanatory variables at multiple follow-up times. In many clinical studies with longitudinal measurements, the response variable, for each patient is collected as long as an event of interest, which considered as clinical end point, occurs. Joint modeling of continuous longitudinal measurements and survival time is an approach for accounting association between two outcomes which frequently discussed in the literature, but design aspects of these models have been rarely considered. This paper uses a simulation-based method to determine the sample size from a Bayesian perspective. For this purpose, several Bayesian criteria for sample size determination are used, of which the most important one is the Bayesian power criterion (BPC), where the determined sample sizes are given based on BPC. We determine the sample size based on treatment effect on both outcomes (longitudinal measurements and survival time). The sample size determination is performed based on multiple hypotheses. Using several examples, the proposed Bayesian methods are illustrated and discussed. All the implementations are performed using R2OpenBUGS package and R 3.5.1 software.","PeriodicalId":422124,"journal":{"name":"Journal of Statistical Research of Iran","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128430943","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}
{"title":"Shrinkage and Bayesian Shrinkage Estimation of the Expected Length of a M/M/1 Queue System","authors":"A. Kiapour, M. N. Qomi","doi":"10.29252/jsri.15.2.301","DOIUrl":"https://doi.org/10.29252/jsri.15.2.301","url":null,"abstract":"","PeriodicalId":422124,"journal":{"name":"Journal of Statistical Research of Iran","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131144530","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}
{"title":"Assessment and Estimation of the Coefficients of a Linear Model for Interval Data","authors":"Amir Massoud Malekfar, F. Eskandari","doi":"10.29252/jsri.15.2.237","DOIUrl":"https://doi.org/10.29252/jsri.15.2.237","url":null,"abstract":"","PeriodicalId":422124,"journal":{"name":"Journal of Statistical Research of Iran","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114520325","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}
. We have considered a perfect sample method for model selection of finite mixture models with either known (fixed) or unknown number of components which can be applied in the most general setting with assumptions on the relation between the rival models and the true distribution. It is, both, one or neither to be well-specified or mis-specified, they may be nested or non-nested. We consider mixture distribution as a complete-data (bivariate) distribution by prediction of missing data variable (unobserved variable) and show that this ideas is applicable to use Vuong’s test for select optimum mixture model when number of components are known (fixed) or unknown. We have considered AIC and BIC based on the complete-data distribution. The performance of this method is evaluated by Monte-Carlo method and real data set, as Total Energy Production.
{"title":"Model Selection for Mixture Models Using Perfect Sample","authors":"S. Fallahigilan, A. Sayyareh","doi":"10.29252/jsri.15.2.173","DOIUrl":"https://doi.org/10.29252/jsri.15.2.173","url":null,"abstract":". We have considered a perfect sample method for model selection of finite mixture models with either known (fixed) or unknown number of components which can be applied in the most general setting with assumptions on the relation between the rival models and the true distribution. It is, both, one or neither to be well-specified or mis-specified, they may be nested or non-nested. We consider mixture distribution as a complete-data (bivariate) distribution by prediction of missing data variable (unobserved variable) and show that this ideas is applicable to use Vuong’s test for select optimum mixture model when number of components are known (fixed) or unknown. We have considered AIC and BIC based on the complete-data distribution. The performance of this method is evaluated by Monte-Carlo method and real data set, as Total Energy Production.","PeriodicalId":422124,"journal":{"name":"Journal of Statistical Research of Iran","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124286913","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}
. This article presents the analysis of the Type-II hybrid progressively censored data when the lifetime distributions of the items follow Type-II generalized logistic distribution. Maximum likelihood estimators (MLEs) are investigated for estimating the location and scale parameters. It is observed that the MLEs can not be obtained in explicit forms. We provide the approximate maximum likelihood estimators (AMLEs) by appropriately approximating the likelihood equations. Asymptotic confidence intervals based on MLEs and AMLEs and one bootstrap confidence interval are proposed. Estimation of the shape parameter is also discussed. Monte Carlo simula-tions are performed to compare the performances of the different methods and two real data sets have been analyzed for illustrative purposes.
{"title":"Inference for the Type-II Generalized Logistic Distribution with Progressive Hybrid Censoring","authors":"M. Azizpour, A. Asgharzadeh","doi":"10.29252/JSRI.14.2.189","DOIUrl":"https://doi.org/10.29252/JSRI.14.2.189","url":null,"abstract":". This article presents the analysis of the Type-II hybrid progressively censored data when the lifetime distributions of the items follow Type-II generalized logistic distribution. Maximum likelihood estimators (MLEs) are investigated for estimating the location and scale parameters. It is observed that the MLEs can not be obtained in explicit forms. We provide the approximate maximum likelihood estimators (AMLEs) by appropriately approximating the likelihood equations. Asymptotic confidence intervals based on MLEs and AMLEs and one bootstrap confidence interval are proposed. Estimation of the shape parameter is also discussed. Monte Carlo simula-tions are performed to compare the performances of the different methods and two real data sets have been analyzed for illustrative purposes.","PeriodicalId":422124,"journal":{"name":"Journal of Statistical Research of Iran","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126383727","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}
. In this paper, the problem of predicting times to failure of units censored in multiple stages of progressively hybrid censoring for the proportional hazards family is considered. We discuss different classical predictors. The best unbiased predictor ( BUP ), the maximum likelihood predictor ( MLP ) and conditional median predictor ( CMP ) are all derived. As an example, the obtained results are computed for exponential distribution. A numerical example is presented to illustrate the prediction methods discussed here. Using simulation studies, the predictors are compared in terms of bias and mean squared prediction error ( MSP E ).
{"title":"Prediction of Times to Failure of Censored Units in Progressive Hybrid Censored Samples for the Proportional Hazards Family","authors":"Samaneh Ameli, Majid Rezaie, J. Ahmadi","doi":"10.29252/jsri.14.2.131","DOIUrl":"https://doi.org/10.29252/jsri.14.2.131","url":null,"abstract":". In this paper, the problem of predicting times to failure of units censored in multiple stages of progressively hybrid censoring for the proportional hazards family is considered. We discuss different classical predictors. The best unbiased predictor ( BUP ), the maximum likelihood predictor ( MLP ) and conditional median predictor ( CMP ) are all derived. As an example, the obtained results are computed for exponential distribution. A numerical example is presented to illustrate the prediction methods discussed here. Using simulation studies, the predictors are compared in terms of bias and mean squared prediction error ( MSP E ).","PeriodicalId":422124,"journal":{"name":"Journal of Statistical Research of Iran","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129091350","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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