Journal of Statistical Computation and Simulation最新文献_第9页

Comparison of standard long memory time series 标准长记忆时间序列的比较

IF 1.2 4区数学

Journal of Statistical Computation and Simulation Pub Date : 2023-11-22 DOI: 10.1080/00949655.2023.2280804

H. P. T. N. Silva, G. S. Dissanayake, T. S. G. Peiris

引用次数: 0

A family of tests for trend change in failure rate function with right censored data 右截尾数据下故障率函数趋势变化的一系列检验

IF 1.2 4区数学

Journal of Statistical Computation and Simulation Pub Date : 2023-11-21 DOI: 10.1080/00949655.2023.2282740

Aritra Saha, M.Z. Anis

引用次数: 0

Bayesian analysis for the Shannon entropy of the Lomax distribution using noninformative priors 利用非信息先验对Lomax分布的香农熵进行贝叶斯分析

IF 1.2 4区数学

Journal of Statistical Computation and Simulation Pub Date : 2023-11-21 DOI: 10.1080/00949655.2023.2284256

Guoqing Dong, Mohammed K. Shakhatreh, Daojiang He

引用次数: 0

Robust estimation for general integer-valued autoregressive models based on the exponential-polynomial divergence 基于指数-多项式散度的一般整值自回归模型的鲁棒估计

IF 1.2 4区数学

Journal of Statistical Computation and Simulation Pub Date : 2023-11-15 DOI: 10.1080/00949655.2023.2283764

Byungsoo Kim, Sangyeol Lee

引用次数: 0

A new threshold INAR(1) model based on modified negative binomial operator with random coefficient 基于改进的带随机系数负二项式算子的阈值INAR(1)模型

IF 1.2 4区数学

Journal of Statistical Computation and Simulation Pub Date : 2023-11-15 DOI: 10.1080/00949655.2023.2282742

Yixuan Fan, Dehui Wang, Jianhua Cheng

引用次数: 0

Bayesian vector heterogeneous autoregressive modelling 贝叶斯向量异构自回归建模

IF 1.2 4区数学

Journal of Statistical Computation and Simulation Pub Date : 2023-11-15 DOI: 10.1080/00949655.2023.2281644

Young Geun Kim, Changryong Baek

引用次数: 0

Testing semiparametric model-equivalence hypotheses based on the characteristic function 基于特征函数的半参数模型等价假设检验

IF 1.2 4区数学

Journal of Statistical Computation and Simulation Pub Date : 2023-11-15 DOI: 10.1080/00949655.2023.2282174

Feifei Chen, Simos G. Meintanis, Lixing Zhu

引用次数: 0

Optimal Bayesian generalized multiple-dependent state sampling plan for attributes 属性的最优贝叶斯广义多依赖状态抽样方案

4区数学

Journal of Statistical Computation and Simulation Pub Date : 2023-11-14 DOI: 10.1080/00949655.2023.2280827

Julia T. Thomas, Mahesh Kumar

{"title":"Optimal Bayesian generalized multiple-dependent state sampling plan for attributes","authors":"Julia T. Thomas, Mahesh Kumar","doi":"10.1080/00949655.2023.2280827","DOIUrl":"https://doi.org/10.1080/00949655.2023.2280827","url":null,"abstract":"AbstractOver the years, acceptance sampling plans have been crucial to quality assurance in manufacturing. Sample plans are designed using operating characteristic curve conditions to safeguard producers and customers. We propose a conditional probability-based Bayesian generalized multiple-dependent state sampling technique in this paper. The technique relies on Gamma-Poisson distribution. Other performance indicators and acceptance probability are calculated. Also, the new plan's operational method is discussed. The proposed technique is also compared to current attribute sampling schemes for efficacy. Optimal plan parameters for the plan's economic structure are also generated, adding managerial insights to the suggested plan. The entire cost study showed that the suggested plan is cheaper than existing sample plans under identical conditions. To account for inspection flaws, the plan is adjusted. We examine how Type I and Type II errors affect sampling plan outcomes. The plan is demonstrated with numerical examples and a data-driven application.Keywords: Bayesian sampling plangamma-Poisson distributioncost optimizationinspection errors Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe authors would like to thank DST, Govt. of India for extending laboratory support under the project (SR/FST/MS-1/2019/40) of the Department of Mathematics, NIT Calicut. The first author would also like to thank CSIR, Govt. of India for extending financial support (09/874(0039)/2019-EMR-I).","PeriodicalId":50040,"journal":{"name":"Journal of Statistical Computation and Simulation","volume":"24 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134954037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Tractable skew-normal approximations via matching 可处理的斜正态近似通过匹配

4区数学

Journal of Statistical Computation and Simulation Pub Date : 2023-11-07 DOI: 10.1080/00949655.2023.2277885

Jackson Zhou, Clara Grazian, John T. Ormerod

{"title":"Tractable skew-normal approximations via matching","authors":"Jackson Zhou, Clara Grazian, John T. Ormerod","doi":"10.1080/00949655.2023.2277885","DOIUrl":"https://doi.org/10.1080/00949655.2023.2277885","url":null,"abstract":"AbstractMany approximate Bayesian inference methods assume a particular parametric form for approximating the posterior distribution. A Gaussian distribution provides a convenient density for such approaches; examples include the Laplace, penalized quasi-likelihood, Gaussian variational, and expectation propagation methods. Unfortunately, these all ignore potential posterior skewness. The recent work of Durante et al. [Skewed Bernstein-von Mises theorem and skew-modal approximations; 2023. ArXiv preprint arXiv:2301.03038.] addresses this using skew-modal (SM) approximations, and is theoretically justified by a skewed Bernstein-von Mises theorem. However, the SM approximation can be impractical to work with in terms of tractability and storage costs, and uses only local posterior information. We introduce a variety of matching-based approximation schemes using the standard skew-normal distribution to resolve these issues. Experiments were conducted to compare the performance of this skew-normal matching method (both as a standalone approximation and as a post-hoc skewness adjustment) with the SM and existing Gaussian approximations. We show that for small and moderate dimensions, skew-normal matching can be much more accurate than these other approaches. For post-hoc skewness adjustments, this comes at very little cost in additional computational time.Keywords: Approximate Bayesian inferencemoment matchingsimulationskew-normal distribution Disclosure statementThe authors confirm that there are no relevant financial or non-financial competing interests to report.Additional informationFundingThe work of John T. Ormerod was supported by an Australian Research Council Discovery Project Grant (DP210100521).","PeriodicalId":50040,"journal":{"name":"Journal of Statistical Computation and Simulation","volume":"297 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135475083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Testing powers of the ratio of variances of two normal populations with a common mean 具有共同均值的两个正态总体方差之比的检验能力

4区数学

Journal of Statistical Computation and Simulation Pub Date : 2023-11-07 DOI: 10.1080/00949655.2023.2276306

Pravash Jena, Manas Ranjan Tripathy

{"title":"Testing powers of the ratio of variances of two normal populations with a common mean","authors":"Pravash Jena, Manas Ranjan Tripathy","doi":"10.1080/00949655.2023.2276306","DOIUrl":"https://doi.org/10.1080/00949655.2023.2276306","url":null,"abstract":"AbstractThis article addresses the problem of hypothesis testing about the powers of the ratio of variances of two normal populations with a common mean. Different test procedures are proposed, such as the likelihood ratio test, the standardized likelihood ratio test, the parametric bootstrap likelihood ratio test, the computational approach test and its modification. Further, several generalized p-value approach test procedures are derived using some of the existing common mean estimators. The performances of all the suggested test methods are compared numerically in terms of their size values and power functions. In light of our simulation findings, we provide a few suggestions for utilizing the proposed test methods. Finally, we analyse real-life data to show the potential application of the proposed model.Keywords: Bootstrap samplescommon meangeneralized p-valueplug-in estimatorsratio of variancespower functionsimulation studysize value2010 AMS Subject Classifications: 62F0362F0562F1065C05 AcknowledgmentsThe authors would like to sincerely thank the two anonymous reviewers, whose constructive and thoughtful comments on the earlier version of the manuscript led to greater improvements in the manuscript's content.Disclosure statementThe authors declare that there are no relevant financial or non-financial competing interests to report for this work.","PeriodicalId":50040,"journal":{"name":"Journal of Statistical Computation and Simulation","volume":"106 13","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135540382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0