政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/30950
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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/30950


    Title: 三要素混合模型於設限資料之願付價格分析
    A three-component mixture model in willingness-to-pay analysis for general interval censored data
    Authors: 蔡依倫
    Tsai,I-lun
    Contributors: 江振東
    Chiang,Jeng-tung
    蔡依倫
    Tsai,I-lun
    Keywords: 願付價格
    加速失敗時間模型
    一般化gamma模型
    willing to pay
    accelerated failure time model
    generalized gamma distribution
    Date: 2004
    Issue Date: 2009-09-14
    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。
    One 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.
    Reference: 1. Agresti, A. (1996), An Introduction to Categorical Data Analysis, New York:
    John Wiley.
    2. 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.
    3. Casella, G. and Berger, R. L. (2002), Statistical Inference, 2nd edition. Pacific Grove, Calif.: Duxbury.
    4. 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(2004), “A Mixture Regression Model in Event History Analysis with Non-Susceptibility and General Interval Censorship”, unpublished manuscript.
    5. Farewell, V. T. and Prentice, R. L. (1977) “A Study of Distributional Shape in Life Testing”, Technometrics, 19, 69-75.
    6. 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.
    7. Klein, J. P. and Moeschberger, M. L. (1997) Suvival Analysis: Techniques for Censored and Truncated Data, New York: Springer.
    8. Lawless, J. F. (2003), Statistical Models and Methods for Lifetime Data, 2nd edition. New Jersey: John Wiley.
    9. Miller, R. G. (1981), Survival Analysis, New York: John Wiley.
    10. Moore, R. J. (1982), “Algorithm AS 187: Derivatives of the Incomplete Gamma Integral”, Applied Statistics, 31, 330-333.
    11. 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.
    12. Yamaguchi, K. (1992), “Accelerated Failure-time Regression Models with a Regression Model of Surviving Fraction,” Journal of the American Statistical Association, 87, 284-292.
    13. Yeh, P. W. (2002), “The Study of Decision Making and Willingness to Pay in Risky Behavior”, unpublished Ph. D thesis, 80-131.
    Description: 碩士
    國立政治大學
    統計研究所
    92354019
    93
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0923540191
    Data Type: thesis
    Appears in Collections:[Department of Statistics] Theses

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