| dc.contributor.advisor | 翁久幸 | zh_TW |
| dc.contributor.advisor | Weng, Chui-Hsing | en_US |
| dc.contributor.author (Authors) | 謝家銘 | zh_TW |
| dc.contributor.author (Authors) | Xie, Jia-Ming | en_US |
| dc.creator (作者) | 謝家銘 | zh_TW |
| dc.creator (作者) | Xie, Jia-Ming | en_US |
| dc.date (日期) | 2018 | en_US |
| dc.date.accessioned | 2-May-2019 14:41:35 (UTC+8) | - |
| dc.date.available | 2-May-2019 14:41:35 (UTC+8) | - |
| dc.date.issued (上傳時間) | 2-May-2019 14:41:35 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0105354002 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/123222 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 統計學系 | zh_TW |
| dc.description (描述) | 105354002 | zh_TW |
| dc.description.abstract (摘要) | 連鎖銷售商店動輒上百家分店，商店銷售額的預測是重要的目的，一般是以個別分店的銷售資料，找出統計模型，對個別分店的銷售額做預測是一種簡單的方法，然而，因為這些分店之間可能有某些相似性，若能找到一個可以同時運用多個店家資料的統計模型，可能有機會改進模型的預測能力與模型係數的適切性，有助商家因應節日及進行促銷時的行銷策略。本論文使用回歸分析對銷售資料進行預測，對不同店家的銷售額所做的回歸分析的參數，用貝氏方法來做進一步的處理，透過將多家店家的回歸係數縮減(shrinkage)，以達到較合理的參數，此方法的主要目的是尋找較合理的參數，其次則是探討迴歸係數縮減下模型預測能力的表現。本研究發現在多家分店的原始迴歸係數相當接近時，使用貝氏方法的改進空間有限，其中階層貝氏方法能夠將若干家商店資訊納入，能對迴歸係數產生較大的縮減，因此有機會改進預測能力，而James-Stein 估計並沒有參考多家商店資訊，因此對於迴歸係數產生較小的縮減，故其預測能力並無太大改進。 | zh_TW |
| dc.description.abstract (摘要) | The prediction of sales is important. It is common to do regression analysis to predict sales for a store using its own data. However, for a chain with hundreds of stores, it may be possible to improve prediction accuracy and obtain more reasonable regression coefficients by combining data from different stores. We propose to achieve these goals by using two shrinkage methods: hierarchical Bayesian method and James-Stein estimator.We found that the shrinkage methods yield limited improvement when the regression coefficients in separate models are rather close. Moreover, the hierarchical method incorporated data from different stores and improve predictions, while James-Stein estimator did not improve much. | en_US |
| dc.description.tableofcontents | 誌謝 1中文摘要 2ABSTRACT 3LIST OF FIGURES 6LIST OF TABLES 7第一章 緒論 8第一節 研究背景 8第二節 研究目的 8第三節 研究架構 9第二章 文獻探討 10第三章 研究方法 12第一節 Shrinkage 估計 12第二節 馬可夫蒙地卡羅(Markove chain Monte Carlo) 14第四章 實證分析 19第一節 數據模擬 19第二節 Rossmann Store資料集 23第三節 實驗結果 26第五章 結論 44第一節 結論 44第二節 後續探討 45文獻參考 46 | zh_TW |
| dc.format.extent | 2177061 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0105354002 | en_US |
| dc.subject (關鍵詞) | 貝氏方法 | zh_TW |
| dc.subject (關鍵詞) | James-Stein 估計 | zh_TW |
| dc.subject (關鍵詞) | Gibbs sampler | zh_TW |
| dc.subject (關鍵詞) | Shrinkage | zh_TW |
| dc.subject (關鍵詞) | Bayesian method | en_US |
| dc.subject (關鍵詞) | James-Stein estimator | en_US |
| dc.subject (關鍵詞) | Gibbs sampler | en_US |
| dc.subject (關鍵詞) | Shrinkage | en_US |
| dc.title (題名) | 貝氏方法應用於連鎖商店銷售額預測 | zh_TW |
| dc.title (題名) | Application of Bayesian Method for Chain Store Sales Prediction | en_US |
| dc.type (資料類型) | thesis | en_US |
| dc.relation.reference (參考文獻) | 1. Jun Shao, Mathematical Statistics, 19992. Robert C. Blattberg and Edward I. George,1991, Shrinkage Estimation of Price and Promotional Elasticities: Seemingly Unrelated Equations3. Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B.Rubin 2013, Bayesian Data Analysis, Third Edition4. Donald B. Rubin 1980, Using Empirical Bayes Techniques in the Law School Validity Studies5. Andrew McCallum, Ronald Rosenfeld, Tom Mitchell, Andrew Y. Ng, Improving Text Classification by Shrinkage in a Hierarchy of Classes6. John Barnard, Robert McCulloch and Xiao-Li Meng 1999, Modeling Covariance Matrices in Terms of standard deviations and correlations, with application to shrinkage | zh_TW |
| dc.identifier.doi (DOI) | 10.6814/THE.NCCU.STAT.004.2019.B03 | en_US |
