Pub Date : 2023-11-22DOI: 10.1080/00949655.2023.2280804
H. P. T. N. Silva, G. S. Dissanayake, T. S. G. Peiris
Standard long memory models are in abundance in the literature today. Selecting the best such a model in terms of capturing key requisite features and trends in data becomes a challenge. This paper...
{"title":"Comparison of standard long memory time series","authors":"H. P. T. N. Silva, G. S. Dissanayake, T. S. G. Peiris","doi":"10.1080/00949655.2023.2280804","DOIUrl":"https://doi.org/10.1080/00949655.2023.2280804","url":null,"abstract":"Standard long memory models are in abundance in the literature today. Selecting the best such a model in terms of capturing key requisite features and trends in data becomes a challenge. This paper...","PeriodicalId":50040,"journal":{"name":"Journal of Statistical Computation and Simulation","volume":"107 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138533708","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}
Pub Date : 2023-11-21DOI: 10.1080/00949655.2023.2282740
Aritra Saha, M.Z. Anis
In this paper we extend the family of test proposed by Majumder and Mitra [Detecting trend change in failure functions-an L-statistic approach. Stat Pap. 2019;62:31–52. doi: 10.1007/s00362-018-0107...
{"title":"A family of tests for trend change in failure rate function with right censored data","authors":"Aritra Saha, M.Z. Anis","doi":"10.1080/00949655.2023.2282740","DOIUrl":"https://doi.org/10.1080/00949655.2023.2282740","url":null,"abstract":"In this paper we extend the family of test proposed by Majumder and Mitra [Detecting trend change in failure functions-an L-statistic approach. Stat Pap. 2019;62:31–52. doi: 10.1007/s00362-018-0107...","PeriodicalId":50040,"journal":{"name":"Journal of Statistical Computation and Simulation","volume":"286 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138533699","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}
Pub Date : 2023-11-21DOI: 10.1080/00949655.2023.2284256
Guoqing Dong, Mohammed K. Shakhatreh, Daojiang He
The Lomax distribution is one of the well-known distributions that is used to fit heavy-tailed data. In this paper, we investigate the estimation of Shannon entropy of the Lomax distribution using ...
{"title":"Bayesian analysis for the Shannon entropy of the Lomax distribution using noninformative priors","authors":"Guoqing Dong, Mohammed K. Shakhatreh, Daojiang He","doi":"10.1080/00949655.2023.2284256","DOIUrl":"https://doi.org/10.1080/00949655.2023.2284256","url":null,"abstract":"The Lomax distribution is one of the well-known distributions that is used to fit heavy-tailed data. In this paper, we investigate the estimation of Shannon entropy of the Lomax distribution using ...","PeriodicalId":50040,"journal":{"name":"Journal of Statistical Computation and Simulation","volume":"7 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138533695","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}
Pub Date : 2023-11-15DOI: 10.1080/00949655.2023.2283764
Byungsoo Kim, Sangyeol Lee
In this study, we develop a robust estimator for integer-valued one-parameter exponential family autoregressive models, named general integer-valued autoregressive models. This model accommodates a...
{"title":"Robust estimation for general integer-valued autoregressive models based on the exponential-polynomial divergence","authors":"Byungsoo Kim, Sangyeol Lee","doi":"10.1080/00949655.2023.2283764","DOIUrl":"https://doi.org/10.1080/00949655.2023.2283764","url":null,"abstract":"In this study, we develop a robust estimator for integer-valued one-parameter exponential family autoregressive models, named general integer-valued autoregressive models. This model accommodates a...","PeriodicalId":50040,"journal":{"name":"Journal of Statistical Computation and Simulation","volume":"22 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138533696","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}
Pub Date : 2023-11-15DOI: 10.1080/00949655.2023.2282742
Yixuan Fan, Dehui Wang, Jianhua Cheng
In this paper, a new threshold INAR(1) model based on modified negative binomial operator with random coefficient is proposed. Basic probabilistic and statistical properties of this process are est...
{"title":"A new threshold INAR(1) model based on modified negative binomial operator with random coefficient","authors":"Yixuan Fan, Dehui Wang, Jianhua Cheng","doi":"10.1080/00949655.2023.2282742","DOIUrl":"https://doi.org/10.1080/00949655.2023.2282742","url":null,"abstract":"In this paper, a new threshold INAR(1) model based on modified negative binomial operator with random coefficient is proposed. Basic probabilistic and statistical properties of this process are est...","PeriodicalId":50040,"journal":{"name":"Journal of Statistical Computation and Simulation","volume":"22 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138533710","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}
Pub Date : 2023-11-15DOI: 10.1080/00949655.2023.2281644
Young Geun Kim, Changryong Baek
The Bayesian vector autoregressive (BVAR) model with the Minnesota prior proposed by Litterman [Litterman RB. Forecasting with bayesian vector autoregressions-five years of experience. J Business E...
{"title":"Bayesian vector heterogeneous autoregressive modelling","authors":"Young Geun Kim, Changryong Baek","doi":"10.1080/00949655.2023.2281644","DOIUrl":"https://doi.org/10.1080/00949655.2023.2281644","url":null,"abstract":"The Bayesian vector autoregressive (BVAR) model with the Minnesota prior proposed by Litterman [Litterman RB. Forecasting with bayesian vector autoregressions-five years of experience. J Business E...","PeriodicalId":50040,"journal":{"name":"Journal of Statistical Computation and Simulation","volume":"160 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138533693","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}
Pub Date : 2023-11-15DOI: 10.1080/00949655.2023.2282174
Feifei Chen, Simos G. Meintanis, Lixing Zhu
We propose three test criteria each of which is appropriate for testing, respectively, the equivalence hypotheses of symmetry, homogeneity, and independence, with multivariate data. All quantities ...
{"title":"Testing semiparametric model-equivalence hypotheses based on the characteristic function","authors":"Feifei Chen, Simos G. Meintanis, Lixing Zhu","doi":"10.1080/00949655.2023.2282174","DOIUrl":"https://doi.org/10.1080/00949655.2023.2282174","url":null,"abstract":"We propose three test criteria each of which is appropriate for testing, respectively, the equivalence hypotheses of symmetry, homogeneity, and independence, with multivariate data. All quantities ...","PeriodicalId":50040,"journal":{"name":"Journal of Statistical Computation and Simulation","volume":"22 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138533709","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}
Pub Date : 2023-11-14DOI: 10.1080/00949655.2023.2280827
Julia T. Thomas, Mahesh Kumar
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).
{"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}
Pub Date : 2023-11-07DOI: 10.1080/00949655.2023.2277885
Jackson Zhou, Clara Grazian, John T. Ormerod
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).
摘要许多近似贝叶斯推理方法为了逼近后验分布，都采用了特定的参数形式。高斯分布为这类方法提供了方便的密度;例子包括拉普拉斯、惩罚类似然、高斯变分和期望传播方法。不幸的是，这些都忽略了潜在的后偏性。Durante et al.[偏斜Bernstein-von Mises定理和偏模态近似;2023. ArXiv预印本ArXiv:2301.03038。]使用偏模态(SM)近似解决了这个问题，并在理论上由偏斜的伯恩斯坦-冯·米塞斯定理证明了这一点。然而，SM近似在可追溯性和存储成本方面可能不切实际，并且只使用局部后验信息。我们介绍了各种基于匹配的近似方案，使用标准斜态分布来解决这些问题。实验比较了这种斜态-正态匹配方法(作为独立近似和事后偏度调整)与SM和现有高斯近似的性能。我们表明，对于小尺寸和中等尺寸，斜正态匹配可以比这些其他方法更准确。对于事后的偏度调整，这在额外的计算时间上花费很少。关键词:近似贝叶斯推断匹配模拟偏正态分布披露声明作者确认没有相关的财务或非财务竞争利益需要报告。John T. Ormerod的工作得到了澳大利亚研究委员会发现项目资助(DP210100521)的支持。
{"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}
Pub Date : 2023-11-07DOI: 10.1080/00949655.2023.2276306
Pravash Jena, Manas Ranjan Tripathy
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.
{"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}
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