المؤلفون: Xin, Hua, Lio, Yuhlong, Fan, Ya-Yen, Tsai, Tzong-Ru

المصدر: Mathematics (2227-7390); Jun2024, Vol. 12 Issue 12, p1828, 17p

مصطلحات موضوعية: DISTRIBUTION (Probability theory), MAXIMUM likelihood statistics, SOIL moisture, NEWTON-Raphson method

مستخلص: The bias of the maximum likelihood estimator can cause a considerable estimation error if the sample size is small. To reduce the bias of the maximum likelihood estimator under the small sample situation, the maximum likelihood and parametric bootstrap bias-correction methods are proposed in this study to obtain more reliable maximum likelihood estimators of the unit exponential distribution parameters. The procedure to implement the bias-corrected maximum likelihood estimation method is derived analytically, and the steps to obtain the bias-corrected bootstrap estimators are presented. The simulation results show that the proposed maximum likelihood bootstrap bias-correction method can significantly reduce the bias and mean squared error of the maximum likelihood estimators for most of the parameter combinations in the simulation study. A soil moisture data set and a numerical example are used for illustration. [ABSTRACT FROM AUTHOR]

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المؤلفون: Jiang, Peihua, Wang, Bingxing, Wang, Xiaofei, Tsai, Tzong-Ru

المساهمون: National Natural Science Foundation of China, National Social Science Fund of China, Talent Cultivation and Research Start-up Foundation of Anhui Polytechnic University, Excellent and Top-notch Personnel Cultivation Project of Universities, Natural Science Research Project of Anhui Educational Committee, Characteristic and Preponderant Discipline of Key Construction Universities in Zhejiang Province, Collaborative Innovation Center of Statistical Data Engineering Technology and Application

المصدر: IEEE Transactions on Reliability ; volume 73, issue 2, page 967-977 ; ISSN 0018-9529 1558-1721

الإتاحة: https://doi.org/10.1109/tr.2023.3328369Test
http://xplorestaging.ieee.org/ielx7/24/10547161/10317807.pdf?arnumber=10317807Test

المؤلفون: Lio, Yuhlong, Chen, Ding-Geng, Tsai, Tzong-Ru, Wang, Liang

المصدر: AppliedMath; Mar2024, Vol. 4 Issue 1, p394-426, 33p

مصطلحات موضوعية: MULTIPHASE flow, STRAINS & stresses (Mechanics), MAXIMUM likelihood statistics, CONFIDENCE intervals, PARAMETER estimation

مستخلص: The reliability of the multicomponent stress–strength system was investigated under the two-parameter Burr X distribution model. Based on the structure of the system, the type II censored sample of strength and random sample of stress were obtained for the study. The maximum likelihood estimators were established by utilizing the type II censored Burr X distributed strength and complete random stress data sets collected from the multicomponent system. Two related approximate confidence intervals were achieved by utilizing the delta method under the asymptotic normal distribution theory and parametric bootstrap procedure. Meanwhile, point and confidence interval estimators based on alternative generalized pivotal quantities were derived. Furthermore, a likelihood ratio test to infer the equality of both scalar parameters is provided. Finally, a practical example is provided for illustration. [ABSTRACT FROM AUTHOR]

: Copyright of AppliedMath is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

المؤلفون: Chiang, Jyun-You, Lio, Yuhlong, Hsu, Chien-Ya, Ho, Chia-Ling, Tsai, Tzong-Ru

المصدر: Entropy; Jan2024, Vol. 26 Issue 1, p15, 16p

مصطلحات موضوعية: MONTE Carlo method, MAXIMUM likelihood statistics, LOGISTIC regression analysis, REGRESSION analysis, CLASSIFICATION, EXPECTATION-maximization algorithms

مستخلص: When the binary response variable contains an excess of zero counts, the data are imbalanced. Imbalanced data cause trouble for binary classification. To simplify the numerical computation to obtain the maximum likelihood estimators of the zero-inflated Bernoulli (ZIBer) model parameters with imbalanced data, an expectation-maximization (EM) algorithm is proposed to derive the maximum likelihood estimates of the model parameters. The logistic regression model links the Bernoulli probabilities with the covariates in the ZIBer model, and the prediction performance among the ZIBer model, LightGBM, and artificial neural network (ANN) procedures is compared by Monte Carlo simulation. The results show that no method can dominate the other methods regarding predictive performance under the imbalanced data. The LightGBM and ZIBer models are more competitive than the ANN model for zero-inflated-imbalanced data sets. [ABSTRACT FROM AUTHOR]

: Copyright of Entropy is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

المؤلفون: Wang, Liang, Lio, Yuhlong, Tripathi, Yogesh Mani, Tsai, Tzong-Ru

المساهمون: National Natural Science Foundation of China, Yunnan Fundamental Research Projects, Yunnan Key Laboratory of Modern Analytical Mathematics and Applications, Science and Engineering Research Board, Indi

المصدر: Quality Technology & Quantitative Management ; page 1-31 ; ISSN 1684-3703

الإتاحة: https://doi.org/10.1080/16843703.2023.2297126Test

المؤلفون: Qin, Shuidan, Wang, Bing Xing, Tsai, Tzong-Ru, Wang, Xiaofei

المصدر: Reliability Engineering & System Safety ; volume 233, page 109091 ; ISSN 0951-8320

مصطلحات موضوعية: Industrial and Manufacturing Engineering, Safety, Risk, Reliability and Quality

الإتاحة: https://doi.org/10.1016/j.ress.2023.109091Test
https://api.elsevier.com/content/article/PII:S0951832023000066?httpAccept=text/xmlTest
https://api.elsevier.com/content/article/PII:S0951832023000066?httpAccept=text/plainTest

المؤلفون: Tsai, Tzong-Ru, Lio, Yuhlong, Jiang, Nan, Lin, Yu-Jau, Fan, Ya-Yen

المصدر: The Journal of the Operational Research Society, 2015 Sep 01. 66(9), 1511-1518.

الوصول الحر: http://www.jstor.org/stable/24505460Test

المؤلفون: Zhou, Ying, Wang, Liang, Tsai, Tzong-Ru, Tripathi, Yogesh Mani

المصدر: Mathematics (2227-7390); Mar2023, Vol. 11 Issue 6, p1461, 30p

مصطلحات موضوعية: COMPETING risks, PROPORTIONAL hazards models, MAXIMUM likelihood statistics

مستخلص: The ranked set sampling (RSS) is an efficient and flexible sampling method. Based on a modified RSS named minimum ranked set sampling samples (MinRSSU), inference of a dependent competing risks model is proposed in this paper. Then, Marshall–Olkin bivariate distribution model is used to describe the dependence of competing risks. When the competing risks data follow the proportional hazard rate distribution, a dependent competing risks model based on MinRSSU sampling is constructed. In addition, the model parameters and reliability indices were estimated by the classical and Bayesian method. Maximum likelihood estimators and corresponding asymptotic confidence intervals are constructed by using asymptotic theory. In addition, the Bayesian estimator and highest posterior density credible intervals are established under the general prior. Furthermore, according to E-Bayesian theory, the point and interval estimators of model parameters and reliability indices are obtained by a sampling algorithm. Finally, extensive simulation studies and a real-life example are presented for illustrations. [ABSTRACT FROM AUTHOR]

: Copyright of Mathematics (2227-7390) is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

المؤلفون: Tsai, Tzong-Ru¹ (AUTHOR), Lio, Yuhlong² (AUTHOR), Chiang, Jyun-You³ (AUTHOR) jiangjy@swufe.edu.cn, Chang, Ya-Wen¹ (AUTHOR)

المصدر: Mathematics (2227-7390). Mar2023, Vol. 11 Issue 5, p1249. 17p.

مصطلحات موضوعية: *BAYES' estimation, *DISTRIBUTION (Probability theory), *MARKOV chain Monte Carlo, *MAXIMUM likelihood statistics

مستخلص: The stress–strength analysis is investigated for a multicomponent system, where all strength variables of components follow a generalized exponential distribution and are subject to the generalized exponential distributed stress. The estimation methods of the maximum likelihood and Bayesian are utilized to infer the system reliability. For the Bayesian estimation method, informative and non-informative priors combined with three loss functions are considered. Because the computational difficulty on working posteriors, the Markov chain Monte Carlo method is adopted to obtain the approximation of the reliability estimator posterior. In addition, the bootstrap method and highest probability density interval are used to obtain the reliability confidence intervals. The simulation study shows that the Bayes estimator with informative prior is superior to other competitors. Finally, two real examples are given to illustrate the proposed estimation methods. [ABSTRACT FROM AUTHOR]

المؤلفون: Tsai, Tzong-Ru, Xin, Hua, Fan, Ya-Yen, Lio, Yuhlong

المصدر: Stats; Dec2022, Vol. 5 Issue 4, p1079-1096, 18p

مصطلحات موضوعية: STATISTICAL bootstrapping, MAXIMUM likelihood statistics, INVERSE Gaussian distribution, MONTE Carlo method, PARAMETER estimation

مستخلص: In this study, the estimation methods of bias-corrected maximum likelihood (BCML), bootstrap BCML (B-BCML) and Bayesian using Jeffrey's prior distribution were proposed for the inverse Gaussian distribution with small sample cases to obtain the ML and Bayes estimators of the model parameters and the process performance index based on the lower specification process performance index. Moreover, an approximate confidence interval and the highest posterior density interval of the process performance index were established via the delta and Bayesian inference methods, respectively. To overcome the computational difficulty of sampling from the posterior distribution in Bayesian inference, the Markov chain Monte Carlo approach was used to implement the proposed Bayesian inference procedures. Monte Carlo simulations were conducted to evaluate the performance of the proposed BCML, B-BCML and Bayesian estimation methods. An example of the active repair times for an airborne communication transceiver is used for illustration. [ABSTRACT FROM AUTHOR]

: Copyright of Stats is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)