請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/7338完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 蔡欣甫 | |
| dc.contributor.author | Tse-Le Huang | en |
| dc.contributor.author | 黃則仂 | zh_TW |
| dc.date.accessioned | 2021-05-19T17:41:45Z | - |
| dc.date.available | 2022-07-10 | |
| dc.date.available | 2021-05-19T17:41:45Z | - |
| dc.date.copyright | 2019-07-10 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-06-28 | |
| dc.identifier.citation | [1] Chen, C. L., Ou, S. L. and Liao, C. T. (2015). Interval estimation for conformance proportions of multiple quality characteristics. Journal of Applied Statistics, 42, 1829-1841.
[2] Crawford, S. L., DeGroot, M. H., Kadane, J. B. and Small, M. J. (1994). Modeling lake-chemistry distributions: approximate Bayesian methods for estimating a finitemixture model. Technometrics, 34, 441-453. [3] Dellaportas, P. and Papageorgiou, I. (2006). Multivariate mixtures of normals with unknown number of components. Statistics and Computing, 16, 57-68. [4] Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of Royal Statistical Society. Series B, 39, 1-38. [5] Gewekw, J. (1992). Evaluation the accuracy of sampling-based approaches to the calculation of posterior moments. Bayesian Statistics, 4, 169-193. [6] Hannig, J. (2009). On generalized fiducial inference. Statistica Sinica, 19, 491-544. [7] Izenman, A. J. and Sommer, C. J. (1988). Philatelic mixtures and multimodal densities. Journal of the American Statistical Association, 83, 941-953. [8] Krishnamoorthy, K. and Mathew, T. (2009). Statistical tolerance regions. Hoboken NJ: John, Wiley & Sons, Inc. [9] Lee, H. I. and Liao, C. T. (2012). Estimation for conformance proportions in a normal variance components model. Journal of Quality Technology, 44, 63-79. [10] Lee, H. I. and Liao, C. T. (2014). Unilateral conformance proportions in balanced and unbalanced normal random effects models. Journal of Agricultural, Biological, and Environment Statistic, 19, 202-218. [11] Lee, H. I., Chen, H., Kishino, H. and Liao, C. T. (2016). A reference populationbased conformance proportion. Journal of Agricultural, Biological, and Environmental Statistics, 21, 684-697. [12] Louis, T. A. (1982). Finding the observed information matrix when using the EM algorithm. Journal of the Royal Statistical Society. Series B, 44, 226-223. [13] McLachlan, G. J. and Peel, D. (2000). Finite mixture models. New York, NY: John Wiley & Sons, Inc. [14] Ott, E. R., Schilling, E. G. and Neubauer, D. V. (2005). Process quality control, 4th Edition. Milwaukee, WI: ASQ Quality Press. [15] Perakis, M. and Xekalaki, E. (2002). A process capability index that is based on the proportion of conformance. Journal of Statistical Computation and Simulation, 72, 707-718. [16] Perakis, M. and Xekalaki, E. (2005). A process capability index for discrete process. Journal of Statistical Computation and Simulation, 75, 175-187. [17] Perakis, M. and Xekalaki, E. (2015). Assessing the proportion of conformance of a process from a Bayesian perspective. Quality Reliability and Engineering International, 31, 381-387. [18] Perakis, M. and Xekalaki, E. (2016). On the relationship between process capability indices and the proportion of conformance. Quality Technology and Quantitative Management, 13, 207-220. [19] Richardson, S. and Green, P. J. (1997). On the Bayesian analysis of mixtures with an unknown number of components (with discussion). Journal of the Royal Statistical Society. Series B, 59,731-792. [20] Tsai, S. F. (2019). Comparing coefficients across subpopulations in gaussian mixture regression models. Journal of Agricultural, Biological and Environmental Statistics, to appear. [21] Wang, C. M. and Lam, C. T. (1996). Confidence limits for proportion of conformance. Journal of Quality Technology, 28, 439-445. [22] Zimmer, Z., Park, D. and Mathew, T. (2016). Tolerance limits under normal mixtures: application to the evaluation of nuclear power plant safety and to the assessment of circular error probable. Computational Statistics and Data Analysis, 103, 304-315. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/7338 | - |
| dc.description.abstract | 良率為評估製程能力與品質的重要指標，目前被廣泛地應用於品質管制、環境監控與其他研究領域。假設目標族群服從常態混合分佈時，如何有系統地建構良率的信賴區間是目前尚未解決的問題。本研究提出一個針對常態混合分佈良率的區間估計方法，利用馬可夫鍊蒙地卡羅法自參數的廣義置信分佈抽樣並計算信賴區間。透過分析一筆實際環境監控的資料說明新方法的可行性，並藉由模擬評估新方法的表現。根據模擬結果，新方法所建構的信賴區間能提供足夠的覆蓋率。 | zh_TW |
| dc.description.abstract | Conformance proportions, which are often employed in quality control, environmental monitoring, and many other areas, are important indices for evaluating product quality and process capability. When the population of interest is assumed to have a normal mixture distribution and specification limits are set by a quality engineer, estimating conformance proportions can be a practical issue. Under the framework of normal mixture distributions, a new method is proposed in this study to obtain confidence intervals for conformance proportions. More specifically, a Markov chain Monte Carlo sampler is developed to generate realizations from the generalized fiducial distributions. The required interval estimates can then be calculated by using the obtained realizations. A real-world environmental monitoring example is used to demonstrate that the proposed method is feasible in practice. Based on simulation results, it is shown that the proposed method can maintain the empirical coverage rate sufficiently close to the nominal level. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-19T17:41:45Z (GMT). No. of bitstreams: 1 ntu-108-R06621208-1.pdf: 2393712 bytes, checksum: d011b875a6bb911b421ac1d50705f118 (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 口試委員會審定書i
致謝ii 摘要iii Abstract iv 1 Introduction 1 2 Methods 3 2.1 Notation and Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.1.1 Normal Mixture Distributions . . . . . . . . . . . . . . . . . . . 3 2.1.2 Conformance Proportions . . . . . . . . . . . . . . . . . . . . . 4 2.1.3 Generalized Fiducial Inference . . . . . . . . . . . . . . . . . . . 7 2.2 The Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3 Results 13 3.1 An Application to Lake Acidity Data . . . . . . . . . . . . . . . . . . . . 13 3.2 Simulation Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 4 Discussion 24 Bibliography 26 | |
| dc.language.iso | en | |
| dc.title | 常態混合分佈良率之區間估計 | zh_TW |
| dc.title | Interval Estimation for Conformance Proportions in Normal Mixture Distributions | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 廖振鐸,劉力瑜 | |
| dc.subject.keyword | 置信推論,信賴區間,潛在變數,馬可夫鍊蒙地卡羅法,品質管制。, | zh_TW |
| dc.subject.keyword | Fiducial inference,Confidence interval,Latent variable,Markov chain Monte Carlo,Quality control., | en |
| dc.relation.page | 28 | |
| dc.identifier.doi | 10.6342/NTU201900936 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2019-06-28 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 農藝學研究所 | zh_TW |
| 顯示於系所單位： | 農藝學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-108-1.pdf | 2.34 MB | Adobe PDF | 檢視/開啟 |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。