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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 鄭欽龍(Chinlong Zheng) | |
dc.contributor.author | Jun-Xiang Chang | en |
dc.contributor.author | 張竣翔 | zh_TW |
dc.date.accessioned | 2021-06-13T03:43:28Z | - |
dc.date.available | 2007-07-03 | |
dc.date.copyright | 2007-07-03 | |
dc.date.issued | 2006 | |
dc.date.submitted | 2006-07-25 | |
dc.identifier.citation | 1. 官俊榮(1990),中小企業的合理成長。台灣銀行季刊,第41卷第4期。
2. 劉錦龍(2001),應用非線性隨機效用模型探討台灣森林資源的遊憩價值。臺大農業經濟系。農業與經濟第27期。 3. 郭斯傑,陳信夫(1998) 以類神經網路估算建築工程成本之比較研究,建築學報第22期。 3. Akintoye,A. (2002a) Analysis of factors influencing project cost estimating practice. Construction Management and Economics 18(1):77-89. 4. Akintoye,A. (2002b) Analysis of factors influencing project cost estimating practice. Construction Management and Economics 18(1):77-89. 5. Austin, P. C., and Tu, J. V. (2004) Bootstrap methods for developing predictive models.The American Statistician 58: 131-137. 6. Bryan,S. (1991) Assembly pricing in construction cost estimating. Cost Engineering 33(8):17-21. 7. Chan, S.L and Park,M. (2005) Project cost estimation using principal component regression. Construction Management and Economics 23(3):295-304. 8. Cheng, H.W and Chang, N.B. (2002) A comparative analysis of methods to represent uncertainty in estimating the cost of constructing wastewater treatment plants. Journal of Environmental Management.65:392pp. 9. Dashbach, J.M. and Apgar,H. (1988) Design analysis through techniques of parametric cost estimating. Engineering Costs and Production Economics 14(1), 87-93. 10. Eva,C and R,Elvezio (2006) A robust approach for skewed and heavy-tailed outcomes in the analysis of health care expenditures. Journal of Health Economics 25(2):198pp. 11. Fox,J. (1997) Applied regression analysis, linear model and related methods. Sage. 12. Green,S. (1989) Tendering: optimization and rationality. Construction Management and Economics 7(1):54-63. 13. Hamilton,L.C. (2006) Statistics with stata. Brooks/Cole. 14. Hampel, F.R., ,E.M.Ronchetti ,P.J. Rousseuw and W.Stahel (1986) Robust statistics. NY: Wiley. 15. Hicks,J.C. (1992) Heavy construction estimates with and without computers. Journal of Construction Engineering and Management 118(3):545-555. 16. Hogg,R.V. (1979) Statistical robusteness: one view of its use in application today. American Statiscian 33(3):108-115. 17. Karshena,S. (1984) Predesign cost estimating method for multistory building. Journal of Construction Engineering and Management 100(1):79-86. 18. Kling, C.L. and R.J. Sexton (1990) Bootstrapping in applied welfare analysis. American Journal of Agricultural Economics 72(2):406-418. 19. Koenigseker, N.A. (1982) Parametric estimating of buildings. Cost Engineering 24(6):327-332. 20. Lowe,D and M.Skitmore (1994) Experimental learning in cost estimating. Construction Management and Economics 12(5): 423-431. 21. Lowe,D, M.W.Emsley and A,Harding (2006) Predicting construction cost using multiple regression techniques. Journal of Construction Engineering and Management 132(7): 750pp. 22. Martin, C. A. and S.F. Witt (1989) Accuracy of econometric forecasts of tourism. Annals of Tourism Research 16:407-428. 23. Martin,R.D. and T.Simin (1999) Estimates of small-stock betas are often very distorted by outliers. University of Washington Statistics Department Tech. Report no. 351. Professional Activities. 24. Mentzer, J.T. and Kahn, K.B. (1995) Forecasting technique familiarity, satisfaction, usage, and application. Journal of Forecasting 14:465-76. 25. Momani,A. and Ayman, H. (1996) Construction cost prediction for public school buildings in Jordan. Construction Management and Economics 14(4):311-317. 26. Newtow,S. (1992) Method of analyzing risk exposure in the cost estimates of quality offices. Construction Management and Economics 10(5):431-449. 27. Ntuen,C.A and A.K. Mallik (1987) Applying artificial intelligence to project cost estimating. Cost Engineering 29 (5):9-13. 28. Phaobunjong,K and C.M. Popescu (2003) Parametric cost estimating method for building. AACE International Transaction.EST.13.1. 29. Pindyck, R.S. and D.L. Rubinfeld (1998) Econometric Models and Economic Forecasts. NY: McGraw-Hill. 30. Quantitative Micro Software , LLC(2002) Eviews 4 User’s Guide. 31. Revay,S.G. (1993) Can construction claims be avoided?. Building Research and Information 12:56-58. 32. Ritschard, G. and G. Antille (1992) A Robust Look at the Use of Regression Diagnostics. The Statistician 41(1): 41-53. 33. Rousseeuw, P.J and A.M. Leroy (1987) Robust regression and outlier detection. Wiley-Interscience. 34. SAS Institute Inc.(2004) STAT/STAT® User's Guide Version 9.1 User's Guide.Gary,NC:SAS Institue Inc.3993pp. 35. Shafer,S.L. (1991) Estimate and project risk analysis. AACE Transcations,pp.K.5.1-K.5.5 36. Shash,A.A. (1993) Factors considered in tendering decisions by top UK contractors. Construction Management and Economics 11(2):111-18. 37. Silberberg,E and W. Suen (2000) The structure of economics: A mathematical analysis.McGraw-Hill.176pp. 38. Silva,D.G, T.D. Jeitschko and G. Kosmopoulou (2005) Stochastic synergies in sequential auctions. International Journal of Industrial Organization 23(3–4): 183–201. 39. Spady,R.H and A.F. Friedlaender (1978) Hedonic cost functions for the regulated trucking industry. The Bell Journal of Economics 9(1):161pp. 40. STATA 8 (2003) Reference Manual A-F, STATA Press, College Station. Vol(1). 41. Thomas,E.U. (1996) A probabilistic cost estimating Model. Cost Engineeri- 42. Varian,H.R. (1999) Intermediate microeconomics: a modern approach 5th Edn. Norton. 43. Verbyla,D.L. and J.A. Litvaitis (1989) Resampling methods for evaluating class accuracy of wildlife habitat models. Environmental Management 13(6):783-787. 44. Wanous,M., A.H. Boussabaine and J.Lewis (2000) To bid or not to bid: a parametric .Construction Management and Economics 18(4):457-466 45. Wooldridge, J.M. (2001) Econometric Analysis of Cross Section and Panel Data. The MIT Press. 46. Wooldridge, J.M.(2002) Introductory econometrics. South-Western. 281-283. 47. Zoubir, A.M. and B. Boashash (1998) The bootstrap and its application in signal processing. IEEE Signal Proces Mag 15:56-76. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/32336 | - |
dc.description.abstract | 本論文的主要研究目的在於分析治山防洪工程成本的影響因素。首先,本研究蒐集2001年至2005年間台大實驗林執行的治山防洪工程資料,接續藉由文獻回顧與成本理論建構成本模型。為減緩資料中異質變異與離群值的問題,本研究採以加權最小平方法及穩健迴歸方法估計成本模型,之後再以拔靴法評估成本模型的預測精確性。
實證結果顯示,治山防洪成本與施工規模、施工方法、施工區位、及施工年度顯著相關。成本模式明確顯示施工規模為最重要的因素,而施工規模達到某一面積時,工程將享有明顯的規模經濟報酬。此外,除施工規模外,加入其它因素能改善成本估計的準確水準。若施工地點遭受嚴重的自然災害,施工成本顯著升高,且成本與施工區位密切相關。研究顯示溪頭與和社營林區有較高的工程成本。最後,拔靴法模擬的結果顯示,本研究所建構的成本模型具有良好的預測精確性。 | zh_TW |
dc.description.abstract | The main purpose of this thesis is to analyze affecting factors of the cost of watershed conservation and flood control projects. First of all, we collected data of the projects carried out in National Taiwan University experimental forest from 2001 to 2005, then set up a cost model by reviewing literatures and cost theory. To alleviate problems of heteroskedasticity and outliers embedded in the data, we applied weighted least squares (WLS) and robust approaches to estimate the cost model. We then assessed prediction accuracy of the cost model with bootstrapping technique.
The result of the empirical study showed that the cost of watershed conservation was significantly related to construction scale, the type of construction, the location and time period of projects implemented. Obviously, the cost model indicated that scale to be the most important factor; a project had significant increasing return to scale if its scale over a certain acreage. Furthermore, the additional factors other than scale could improve the level of accuracy in cost estimation. The cost increased significantly if the project site damaged severely by natural disasters, and related positively to the location of project. It showed that the Chi-Tou and Ho-She districts had higher project cost. Finally, the result of bootstrapping approach suggested that the cost model developed by this study had good accuracy in prediction. | en |
dc.description.provenance | Made available in DSpace on 2021-06-13T03:43:28Z (GMT). No. of bitstreams: 1 ntu-95-R93625042-1.pdf: 2053843 bytes, checksum: c81f5721bc1f96bad09f74366d0b47af (MD5) Previous issue date: 2006 | en |
dc.description.tableofcontents | 目次
第一章 緒論 1 一、 研究背景及動機 1 二、 研究問題與目的 1 三、 研究方法與流程 2 第二章 文獻回顧 5 一、 成本估價 5 二、 經驗法則估價法 6 三、 參數估價法 8 第三章 理論與研究方法 13 一、 最適規模 13 二、 成本函數理論 14 三、 統計理論 15 四、 統計分析流程 21 第四章 治山防洪工程成本分析 24 一、 台大實驗林概況 24 二、 崩塌地源頭緊急水土保持 27 三、 國有林崩塌地復育造林 38 四、 討論 49 第五章 結論與建議 52 參考文獻 56 附錄 60 | |
dc.language.iso | zh-TW | |
dc.title | 治山防洪工程成本分析-以台大實驗林為例 | zh_TW |
dc.title | Cost Analysis of Watershed Conservation Projects in National Taiwan University Experimental Forest | en |
dc.type | Thesis | |
dc.date.schoolyear | 94-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 葉家瑜,劉宜君 | |
dc.subject.keyword | 治山防洪,成本估價,穩健迴歸,拔靴法, | zh_TW |
dc.subject.keyword | watershed conservation,flood control,cost appraisal,robust regression,bootstrapping, | en |
dc.relation.page | 62 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2006-07-26 | |
dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
dc.contributor.author-dept | 森林環境暨資源學研究所 | zh_TW |
顯示於系所單位: | 森林環境暨資源學系 |
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