請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/56572完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 楊曙榮(Shu-Jung Sunny Yang) | |
| dc.contributor.author | Yi-Jen Luo | en |
| dc.contributor.author | 羅翊仁 | zh_TW |
| dc.date.accessioned | 2021-06-16T05:35:31Z | - |
| dc.date.available | 2023-07-30 | |
| dc.date.copyright | 2020-08-04 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-07-26 | |
| dc.identifier.citation | Abboud, N. E. (2001). A discrete-time Markov production-inventory model with machine breakdowns. Computers Industrial Engineering, 39(1-2), 95-107. Azoury, K. S., Miyaoka, J. (2009). Optimal policies and approximations for a Bayesian linear regression inventory model. Management Science, 55(5), 813-826. Azzimonti, L., Corani, G., Zaffalon, M. (2017, November). Hierarchical Multinomial-Dirichlet model for the estimation of conditional probability tables. In 2017 IEEE International Conference on Data Mining (ICDM) (pp. 739-744). IEEE. Bernard, J. M. (2005). An introduction to the imprecise Dirichlet model for multinomial data. International Journal of Approximate Reasoning, 39(2-3), 123-150. Betancourt, M. (2017). A conceptual introduction to Hamiltonian Monte Carlo. arXiv preprint arXiv:1701.02434. Bruni, R., Bianchi, G., Dolente, C., Leporelli, C. (2019). Logical Analysis of Data as a tool for the analysis of Probabilistic Discrete Choice Behavior. Computers Operations Research, 106, 191-201. Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., ... Riddell, A. (2017). Stan: A probabilistic programming language. Journal of Statistical Software, 76(1). Carr, S., Lovejoy, W. (2000). The inverse newsvendor problem: Choosing an optimal demand portfolio for capacitated resources. Management Science, 46(7), 912-927. Casella, G. (1985). An introduction to empirical Bayes data analysis. The American Statistician, 39(2), 83-87. Chang, S. H., Fyffe, D. E. (1971). Estimation of forecast errors for seasonal-style-goods sales. Management Science, 18(2), B-89. Cheung, Y. W., Lai, K. S. (1995). Lag order and critical values of the augmented Dickey–Fuller test. Journal of Business Economic Statistics, 13(3), 277-280. Cui, G., Wong, M. L., Lui, H. K. (2006). Machine learning for direct marketing response models: Bayesian networks with evolutionary programming. Management Science, 52(4), 597-612. Dong, C., Ng, C. T., Cheng, T. C. E. (2017). Electricity time‐of‐use tariff with stochastic demand. Production and Operations Management, 26(1), 64-79. Fornaciari, M., Grillenzoni, C. (2017). Evaluation of on-line trading systems: Markov-switching vs time-varying parameter models. Decision Support Systems, 93, 51-61. Frigyik, B. A., Kapila, A., Gupta, M. R. (2010). Introduction to the Dirichlet distribution and related processes. Department of Electrical Engineering, University of Washignton, UWEETR-2010-0006, (0006), 1-27. Galli, B. J. (2018). The future of economic decision making in project management. IEEE Transactions on Engineering Management. Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., Rubin, D. B. (2013). Bayesian Data Analysis. CRC press. Geunes, J., Merzifonluoğlu, Y., Romeijn, H. E., Taaffe, K. (2005). Demand selection and assignment problems in supply chain planning. In Emerging Theory, Methods, and Applications (pp. 124-141). INFORMS. Hankin, R. K. (2010). A generalization of the Dirichlet distribution. Journal of Statistical Software, 33(11), 1-18. Horn, R. A., Johnson, C. R. (2012). Matrix analysis. Cambridge university press. Kemp, C., Perfors, A., Tenenbaum, J. B. (2007). Learning overhypotheses with hierarchical Bayesian models. Developmental Science, 10(3), 307-321. Kilimci, Z. H., Akyuz, A. O., Uysal, M., Akyokus, S., Uysal, M. O., Atak Bulbul, B., Ekmis, M. A. (2019). An improved demand forecasting model using deep learning approach and proposed decision integration strategy for supply chain. Complexity, 2019. Kück, M., Freitag, M. (2020). Forecasting of customer demands for production planning by local k-nearest neighbor models. International Journal of Production Economics, 107837. Iyer, A. V., Deshpande, V., Wu, Z. (2003). A postponement model for demand management. Management Science, 49(8), 983-1002. Li, B., Li, J., Li, W., Shirodkar, S. A. (2012). Demand forecasting for production planning decision-making based on the new optimised fuzzy short time-series clustering. Production Planning Control, 23(9), 663-673. Li-Chung, J., Chen, C. I. (2007). Customer Value Migration Analysis: Markov Chain Model. National Taiwan University Management Reviews, 17(2), 133-158. Lynch, S. M. (2007). Introduction to applied Bayesian statistics and estimation for social scientists. Springer Science Business Media. Makridakis, S., Wheelwright, S. C., Hyndman, R. J. (2008). Forecasting Methods and Applications. John Wiley Wons. McElreath, R. (2016). Statistical Rethinking: A Bayesian Course with Examples in R and Stan. CRC press. Michna, Z., Disney, S. M., Nielsen, P. (2020). The impact of stochastic lead times on the bullwhip effect under correlated demand and moving average forecasts. Omega, 93, 102033. Mimno, D. M., McCallum, A. (2008, July). Topic models conditioned on arbitrary features with Dirichlet-multinomial regression. In UAI (Vol. 24, pp. 411-418). Minka, T. (2000). Estimating a Dirichlet distribution. Montoya, R., Gonzalez, C. (2019). A Hidden Markov Model to Detect On-Shelf Out-of-Stocks Using Point-of-Sale Data. Manufacturing Service Operations Management, 21(4), 932-948. Mostard, J., Teunter, R., De Koster, R. (2011). Forecasting demand for single-period products: A case study in the apparel industry. European Journal of Operational Research, 211(1), 139-147. Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT press. Pang, Z., Berman, O., Hu, M. (2015). Up Then Down: Bid‐Price Trends in Revenue Management. Production and Operations Management, 24(7), 1135-1147. Paton, L., Troffaes, M. C., Boatman, N., Hussein, M., Hart, A. (2014, July). Multinomial logistic regression on Markov chains for crop rotation modelling. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 476-485). Springer, Cham. Rahman, M. A., Sarker, B. R., Escobar, L. A. (2011). Peak demand forecasting for a seasonal product using Bayesian approach. Journal of the Operational Research Society, 62(6), 1019-1028. Snyder, L. V., Shen, Z. J. M. (2011). Fundamentals of Supply Chain Theory (p. 367). Hoboken: Wiley. Tang, Z. Z., Chen, G. (2019). Zero-inflated generalized Dirichlet multinomial regression model for microbiome compositional data analysis. Biostatistics, 20(4), 698-713. Tu, S. (2014). The dirichlet-multinomial and dirichlet-categorical models for bayesian inference. Computer Science Division, UC Berkeley. Retrieved from https://pdfs.semanticscholar.org/2c03/8d49cbd1a09fc5dcd47bc89d94d3b496d3a7.pdf Wadsworth, W. D., Argiento, R., Guindani, M., Galloway-Pena, J., Shelburne, S. A., Vannucci, M. (2017). An integrative Bayesian Dirichlet-multinomial regression model for the analysis of taxonomic abundances in microbiome data. BMC Bioinformatics, 18(1), 1-12 Walz, D. T., Walz, D. B. (1989). Combining forecasts: Multiple regression versus a Bayesian approach. Decision Sciences, 20(1), 77-89. Workshops: Dalhousie U October – Stan. (2017). Retrieved from https://mc-stan.org/workshops/halifax2017/hier-models-meetup-2017.pdf Yang, N., Zhang, R. (2014). Dynamic pricing and inventory management under inventory-dependent demand. Operations Research, 62(5), 1077-1094. Zais, M., Zhang, D. (2016). A Markov chain model of military personnel dynamics. International Journal of Production Research, 54(6), 1863-1885. Zhen, X., Basawa, I. V. (2009). Categorical time series models for contingency tables. Statistics Probability Letters, 79(10), 1331-1336. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/56572 | - |
| dc.description.abstract | 需求預測是營運管理中重要的課題，透過預測下游市場需求對產品價格的反應，得以建立良好的需求管理。在許多產業，市場需求會受到個別廠商的牌價制定決策以及當時經濟情況所影響，廠商可以透過對牌價的設定來影響供應鏈下游廠商的採購決策。傳統的需求預測模型較多偏重於產經數據對於銷量的影響,較少深入探討廠商如何影響市場預期。廠商若能觀察市場的經濟情形並制定適當的牌價，就可以達到有效的需求管理。本文以離散選擇統計模型為基礎，根據過去接單量之狀態遷移路徑，搭配層級貝氏計量模型，建立異質性轉移機率矩陣，進而預測接單量狀態的轉移，藉此優化需求管理。 | zh_TW |
| dc.description.abstract | Demand forecasting plays an important role in operations and supply chains. Firms can establish a full-fledged demand management if they can forecast the demand from downstream manufacturers responding to the price of the goods accurately. In many industries, firm’s policy of price tags and current economy situations have a great influence on market demand, and they can affect the purchasing decisions from the downstream manufacturers by declaring suitable price tags. Conventional empirical research on demand forecasting relies on investigating the effects of economic data on the order quantity rather than exploring how firms can manipulate the market expectation. Firms can achieve effective demand management by declaring suitable price tags after examining the economic situation. This study is based on discrete choice model, and construct heterogeneous transition probability matrix using the historical sales data of state migration. With our method, firms can adopt suitable strategy for price tag to forecast the state migration of order quantity accordingly. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T05:35:31Z (GMT). No. of bitstreams: 1 U0001-2507202014312900.pdf: 2686398 bytes, checksum: 30218e17f2b38c7fdae53926a9a5379e (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 謝辭 ii 摘要 iii Abstract iv Table of Contents v List of Figures vii List of Tables viii 1 Introduction 1 1.1 Research Background 1 1.2 Research Purpose 3 2 Literature Review 5 3 Model Development 7 3.1 The Discrete Choice Model 7 4 Statistical Specification 13 4.1 Posterior 13 4.2 Posterior Estimate 15 4.3 Posterior Predictive Distribution 17 4.4 Bayesian Estimation using Stan 17 5 Numerical Experiments 19 5.1 Synthetic Data 20 5.3 Simulation Results 21 6 Case Study 25 6.1 Data Set 25 6.2 Variable Definition and Summary Statistics 28 6.3 Estimation 31 6.4 Discussion 36 6.4.1 Categorical Time Series Model 37 6.4.2 Trajectory Analysis 38 7 Conclusion 40 References 43 Appendix A 48 | |
| dc.language.iso | en | |
| dc.subject | 商業數據分析 | zh_TW |
| dc.subject | 列聯表 | zh_TW |
| dc.subject | 馬可夫鏈 | zh_TW |
| dc.subject | 貝氏估計 | zh_TW |
| dc.subject | 離散選擇模型 | zh_TW |
| dc.subject | 需求管理 | zh_TW |
| dc.subject | 需求預測 | zh_TW |
| dc.subject | business analytics | en |
| dc.subject | demand management | en |
| dc.subject | discrete choice model | en |
| dc.subject | Bayesian estimation | en |
| dc.subject | Markov chains | en |
| dc.subject | contingency table | en |
| dc.subject | demand forecasting | en |
| dc.title | 使用列聯表建立需求管理為基之離散選擇統計模型 | zh_TW |
| dc.title | A Discrete Choice Model of Demand Management using Contingency Tables | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳立民(Li-Ming Chen),謝凱宇(Kai-Yu Hsieh) | |
| dc.subject.keyword | 需求預測,需求管理,離散選擇模型,貝氏估計,馬可夫鏈,列聯表,商業數據分析, | zh_TW |
| dc.subject.keyword | demand forecasting,demand management,discrete choice model,Bayesian estimation,Markov chains,contingency table,business analytics, | en |
| dc.relation.page | 93 | |
| dc.identifier.doi | 10.6342/NTU202001847 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2020-07-27 | |
| dc.contributor.author-college | 共同教育中心 | zh_TW |
| dc.contributor.author-dept | 統計碩士學位學程 | zh_TW |
| 顯示於系所單位： | 統計碩士學位學程 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-2507202014312900.pdf 未授權公開取用 | 2.62 MB | Adobe PDF |
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。