Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/30950
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dc.contributor.advisor江振東zh_TW
dc.contributor.advisorChiang,Jeng-tungen_US
dc.contributor.author蔡依倫zh_TW
dc.contributor.authorTsai,I-lunen_US
dc.creator蔡依倫zh_TW
dc.creatorTsai,I-lunen_US
dc.date2004en_US
dc.date.accessioned2009-09-14-
dc.date.available2009-09-14-
dc.date.issued2009-09-14-
dc.identifierG0923540191en_US
dc.identifier.urihttps://nccur.lib.nccu.edu.tw/handle/140.119/30950-
dc.description碩士zh_TW
dc.description國立政治大學zh_TW
dc.description統計研究所zh_TW
dc.description92354019zh_TW
dc.description93zh_TW
dc.description.abstract在探討願付價格的條件評估法中一種常被使用的方法為“雙界二分選擇法”,並且一個隱含的假設是,所有研究對象皆願意支付一個合理的金額。然而對於某些商品,有些人也許願意支付任何金額;相對的,有些人可能不願意支付任何金額。分析願付價格時若不考慮這兩類極端反應者,則可能會得到一個偏誤的願付價格。本篇研究中,我們提出一個“混合模型”來處理此議題,其中以多元邏輯斯迴歸模型來描述不同反應者的比例,並以加速失敗時間模型來估計願意支付合理金額者其願付價格的分布。此外,我們以關於治療高血壓新藥之願付價格實例,作為實證分析。zh_TW
dc.description.abstractOne commonly used method in contingent valuation (CV) survey for WTP (willingness-to-pay) is the “double-bound dichotomous choice approach” and an implicit assumption is that all study subjects are willing to pay a reasonable price. However, for certain goods, some subjects may be willing to pay any price for them, while some others may be unwilling to pay any price. Without considering these two types of the extreme respondents, a wrongly estimated WTP value will be obtained. We propose a “mixture model” to handle the issues in this study, in which a multinomial logistic model is taken to specify the proportions of different respondents and an accelerated failure time model is utilized to describe the distribution of WTP price for subjects who are willing to pay a reasonable price. In addition, an empirical example on WTP prices for a new hypertension treatment is provided to illustrate the proposed methods.en_US
dc.description.tableofcontentsSection\r\n1. Introduction 1\r\n2. Review of Literature 4\r\n3. A Three-component Mixture Model 6\r\n4. Simulation Studies 12\r\n5. An Application 17\r\n6. Concluding Remarks 26\r\nReferences 28\r\n\r\nAppendix\r\nⅠ. The First and Second Derivatives of the\r\n Log-likelihood Function 30\r\n\r\nⅡ. Derivatives Related to the Log-likelihood Function 37\r\n\r\nⅢ. Setting Initial Values for Parameter Estimation 43\r\nⅣ. The WTP questionnaire 46\r\nⅤ. Computer programming 47zh_TW
dc.language.isoen_US-
dc.source.urihttp://thesis.lib.nccu.edu.tw/record/#G0923540191en_US
dc.subject願付價格zh_TW
dc.subject加速失敗時間模型zh_TW
dc.subject一般化gamma模型zh_TW
dc.subjectwilling to payen_US
dc.subjectaccelerated failure time modelen_US
dc.subjectgeneralized gamma distributionen_US
dc.title三要素混合模型於設限資料之願付價格分析zh_TW
dc.titleA three-component mixture model in willingness-to-pay analysis for general interval censored dataen_US
dc.typethesisen
dc.relation.reference1. Agresti, A. (1996), An Introduction to Categorical Data Analysis, New York:zh_TW
dc.relation.referenceJohn Wiley.zh_TW
dc.relation.reference2. Alberini, A. (1995), “Efficiency vs. Bias of Willingness-to-Pay Estimates: Bivariate and Interval-Data Models,” Journal of Environmental Economics and Management, 29, 169-180.zh_TW
dc.relation.reference3. Casella, G. and Berger, R. L. (2002), Statistical Inference, 2nd edition. Pacific Grove, Calif.: Duxbury.zh_TW
dc.relation.reference4. Chen, C. H., Horng, C. F. and Wu, Y. C. (2004), “A Mixture Regression Model in Event History Analysis with Non-Susceptibility and General Interval Censorship”, unpublished manuscript.zh_TW
dc.relation.reference5. Farewell, V. T. and Prentice, R. L. (1977) “A Study of Distributional Shape in Life Testing”, Technometrics, 19, 69-75.zh_TW
dc.relation.reference6. Hanemann, W. M., Loomis, J. and Kanninen, B. (1991), “Statistical Efficiency of Double-Bounded Dichotomous Choice Contingent Valuation,” American Journal Agricultural Economics, 73, 1255-1263.zh_TW
dc.relation.reference7. Klein, J. P. and Moeschberger, M. L. (1997) Suvival Analysis: Techniques for Censored and Truncated Data, New York: Springer.zh_TW
dc.relation.reference8. Lawless, J. F. (2003), Statistical Models and Methods for Lifetime Data, 2nd edition. New Jersey: John Wiley.zh_TW
dc.relation.reference9. Miller, R. G. (1981), Survival Analysis, New York: John Wiley.zh_TW
dc.relation.reference10. Moore, R. J. (1982), “Algorithm AS 187: Derivatives of the Incomplete Gamma Integral”, Applied Statistics, 31, 330-333.zh_TW
dc.relation.reference11. Turnbull, B. W. (1976), “The Empirical Distribution Function with Arbitrarily Grouped Censored and Truncated Data”, Journal of the Royal Statistical Society, Series B, 38, 290-295.zh_TW
dc.relation.reference12. Yamaguchi, K. (1992), “Accelerated Failure-time Regression Models with a Regression Model of Surviving Fraction,” Journal of the American Statistical Association, 87, 284-292.zh_TW
dc.relation.reference13. Yeh, P. W. (2002), “The Study of Decision Making and Willingness to Pay in Risky Behavior”, unpublished Ph. D thesis, 80-131.zh_TW
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item.openairecristypehttp://purl.org/coar/resource_type/c_46ec-
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