American Journal of Mathematical and Management Sciences最新文献_第10页

On Moments of Lindley Distribution Based on Generalized Order Statistics 基于广义阶统计量的Lindley分布矩

American Journal of Mathematical and Management Sciences Pub Date : 2020-02-10 DOI: 10.1080/01966324.2020.1718568

M. J. S. Khan, A. Sharma, S. Iqrar

引用次数: 2

Prediction for Big Data Through Kriging: Small Sequential and One-Shot Designs 通过克里格预测大数据:小序列和一次性设计

American Journal of Mathematical and Management Sciences Pub Date : 2020-01-30 DOI: 10.1080/01966324.2020.1716281

J. Kleijnen, Wim C. M. van Beers

{"title":"Prediction for Big Data Through Kriging: Small Sequential and One-Shot Designs","authors":"J. Kleijnen, Wim C. M. van Beers","doi":"10.1080/01966324.2020.1716281","DOIUrl":"https://doi.org/10.1080/01966324.2020.1716281","url":null,"abstract":"Abstract Kriging—or Gaussian process (GP) modeling—is an interpolation method assuming that the outputs (responses) are more correlated, as the inputs (explanatory or independent variables) are closer. Such a GP has unknown (hyper)parameters that are usually estimated through the maximum-likelihood method. Big data, however, make it problematic to compute these estimated parameters, and the corresponding Kriging predictor and its predictor variance. To solve this problem, some authors select a relatively small subset from the big set of previously observed “old” data. These selection methods are sequential, and they depend on the variance of the Kriging predictor; this variance requires a specific Kriging model and the estimation of its parameters. The resulting designs turn out to be “local”; i.e., most selected old input combinations are concentrated around the new combination to be predicted. We develop a simpler one-shot (fixed-sample, non-sequential) design; i.e., from the big data set we select a small subset with the nearest neighbors of the new combination. To compare our designs and the sequential designs empirically, we use the squared prediction errors, in several numerical experiments. These experiments show that our design may yield reasonable performance.","PeriodicalId":35850,"journal":{"name":"American Journal of Mathematical and Management Sciences","volume":"39 1","pages":"199 - 213"},"PeriodicalIF":0.0,"publicationDate":"2020-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/01966324.2020.1716281","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46860746","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: 13

Simple, Yet Fast and Effective Two-Phase Method for Nurse Rostering 简单、快速、有效的护士名册两阶段法

American Journal of Mathematical and Management Sciences Pub Date : 2020-01-02 DOI: 10.1080/01966324.2019.1570882

F. Guessoum, S. Haddadi, E. Gattal

引用次数: 7

Bayesian Inference for Weibull Distribution under the Balanced Joint Type-II Progressive Censoring Scheme 平衡联合II型渐进Censoring方案下威布尔分布的贝叶斯推断

American Journal of Mathematical and Management Sciences Pub Date : 2020-01-02 DOI: 10.1080/01966324.2019.1579124

Shuvashree Mondal, D. Kundu

{"title":"Bayesian Inference for Weibull Distribution under the Balanced Joint Type-II Progressive Censoring Scheme","authors":"Shuvashree Mondal, D. Kundu","doi":"10.1080/01966324.2019.1579124","DOIUrl":"https://doi.org/10.1080/01966324.2019.1579124","url":null,"abstract":"SYNOPTIC ABSTRACT Progressive censoring schemes have received considerable attention recently. All of these developments are mainly based on a single population. Recently, Mondal and Kundu (2016) introduced the balanced joint progressive censoring scheme (BJPC), and studied the exact inference for two exponential populations. It is well known that the exponential distribution has some limitations. In this article, we implement the BJPC scheme on two Weibull populations with the common shape parameter. The treatment here is purely Bayesian in nature. Under the Bayesian set up we assume a Beta Gamma prior of the scale parameters, and an independent Gamma prior for the common shape parameter. Under these prior assumptions, the Bayes estimators cannot be obtained in closed forms, and we use the importance sampling technique to compute the Bayes estimators and the associated credible intervals. We further consider the order restricted Bayesian inference of the parameters based on the ordered Beta Gamma priors of the scale parameters. We propose one precision criteria based on expected volume of the joint credible set of model parameters to find out the optimum censoring scheme. We perform extensive simulation experiments to study the performance of the estimators, and finally analyze one real data set for illustrative purposes.","PeriodicalId":35850,"journal":{"name":"American Journal of Mathematical and Management Sciences","volume":"39 1","pages":"56 - 74"},"PeriodicalIF":0.0,"publicationDate":"2020-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/01966324.2019.1579124","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46767411","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: 21

Sequential Minimum Risk Point Estimation of the Parameters of an Inverse Gaussian Distribution 逆高斯分布参数序贯最小风险点估计

American Journal of Mathematical and Management Sciences Pub Date : 2020-01-02 DOI: 10.1080/01966324.2019.1570883

Ajit Chaturvedi, Sudeep R. Bapat, Neeraj Joshi

引用次数: 6

Some Results on Dynamic Weighted Varma’s Entropy and its Applications 关于动态加权Varma熵的一些结果及其应用

American Journal of Mathematical and Management Sciences Pub Date : 2020-01-02 DOI: 10.1080/01966324.2019.1642817

K. Ajith, E. I. Abdul Sathar

引用次数: 5

Asymptotic and Bootstrap Confidence Intervals for the Process Capability Index cpy Based on Lindley Distributed Quality Characteristic 基于Lindley分布质量特征的过程能力指数cpy的渐近置信区间和Bootstrap置信区间

American Journal of Mathematical and Management Sciences Pub Date : 2020-01-02 DOI: 10.1080/01966324.2019.1580644

Mahendra Saha, Sumit Kumar, Sudhansu S. Maiti, Abhimanyu Singh Yadav, S. Dey

{"title":"Asymptotic and Bootstrap Confidence Intervals for the Process Capability Index cpy Based on Lindley Distributed Quality Characteristic","authors":"Mahendra Saha, Sumit Kumar, Sudhansu S. Maiti, Abhimanyu Singh Yadav, S. Dey","doi":"10.1080/01966324.2019.1580644","DOIUrl":"https://doi.org/10.1080/01966324.2019.1580644","url":null,"abstract":"SYNOPTIC ABSTRACT Process capability indices (PCIs) have been widely applied in measuring product potential and performance. It is of great significance to quality control engineers, as it quantifies the relation between the actual performance of the process and the preset specifications of the product. Among the plethora of the suggested PCIs, most of them were developed for normally distributed processes. In this article, we consider generalized process capability index Cpy suggested by Maiti et al. (2010), which can be used for normal, non-normal, and continuous as well as discrete random variables. The objective of this article is twofold. First, we obtain maximum likelihood estimator (MLE) and minimum variance unbiased estimator (MVUE) of the PCI Cpy for the Lindley distributed quality characteristics. Second, we compare asymptotic confidence interval (ACI) with four bootstrap confidence intervals (BCIs); namely, standard bootstrap (s-boot), percentile bootstrap (p-boot), Student’s t bootstrap (t-boot), and bias-corrected accelerated bootstrap (BCa-boot) of Cpy based on maximum likelihood method of estimation. Monte Carlo simulations have been carried out to compare the performance of MLEs and MVUEs, and also investigate the average widths, coverage probabilities, and relative coverages of ACI and BCIs of Cpy. Two real data sets have been analyzed for illustrative purposes.","PeriodicalId":35850,"journal":{"name":"American Journal of Mathematical and Management Sciences","volume":"39 1","pages":"75 - 89"},"PeriodicalIF":0.0,"publicationDate":"2020-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/01966324.2019.1580644","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48019427","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: 13

E-Bayesian Estimation for Burr-X Distribution Based on Generalized Type-I Hybrid Censoring Scheme 基于广义I型混合Censoring方案的Burr-X分布的E-Bayesian估计

American Journal of Mathematical and Management Sciences Pub Date : 2020-01-02 DOI: 10.1080/01966324.2019.1579123

A. Rabie, Junping Li

引用次数: 18

Two M/M/1 Queues with Incongruent Arrivals and Services with Random Feedback 两个到达不一致和服务随机反馈的M/M/1队列

American Journal of Mathematical and Management Sciences Pub Date : 2019-05-01 DOI: 10.1080/01966324.2019.1597794

M. Ghahramani, A. Badamchi Zadeh, M. R. Salehi Rad

引用次数: 1

Performance Improvement of a Multi-State Coherent System using Component Importance Measure 利用分量重要性测度改善多状态相干系统的性能

American Journal of Mathematical and Management Sciences Pub Date : 2019-04-30 DOI: 10.1080/01966324.2018.1551733

S. Roychowdhury, D. Bhattacharya

{"title":"Performance Improvement of a Multi-State Coherent System using Component Importance Measure","authors":"S. Roychowdhury, D. Bhattacharya","doi":"10.1080/01966324.2018.1551733","DOIUrl":"https://doi.org/10.1080/01966324.2018.1551733","url":null,"abstract":"SYNOPTIC ABSTRACT In system engineering, numerous efforts have been made for achieving improvement in system performance under a binary set up, where each component, as well as the entire system, has any one of two states; namely, perfect functioning and complete failure. However, there are systems which perform their tasks at various performance levels rather than functioning at only the above two performance levels. These systems are multi-state systems. In these systems, there can be some partially working states or performance levels before the system comes to the state of complete failure. Hence, the need has been felt to develop the procedures for improving the performance of multi-state systems consisting of multi-state components. This article resolves such an issue for a multi-state system using a multi-state component importance measure. The measure developed here is used to assess the impact of individual components on the improvement of system performance. Some basic theory to deal with a homogeneous multi-state coherent system has been developed, and finally, a rule has been derived to improve system performance using the importance measure. The applications of the results have been illustrated through a real-life example.","PeriodicalId":35850,"journal":{"name":"American Journal of Mathematical and Management Sciences","volume":"38 1","pages":"312 - 324"},"PeriodicalIF":0.0,"publicationDate":"2019-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/01966324.2018.1551733","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43816343","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: 2