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| Title: | 條件評估法中處理「不知道」回應之研究
Analysis of contingency valuation survey data with “Don’t Know” responses |
| Authors: | 王昱博
Wang, Yu Bo |
| Contributors: | 江振東
Chiang, Jeng Tung,
王昱博
Wang, Yu Bo |
| Keywords: | EM演算法
條件評估法
三要素混合模型
加速失敗模型
EM algorithm
CV survey
Three-component mixture model
AFT model |
| Date: | 2008 |
| Issue Date: | 2009-09-18 20:09:54 (UTC+8) |
| Abstract: | 本文主要著重在處理條件評估法下，「不知道」受訪者的回應。當「不知道」受訪者的產生機制並未符合完全隨機時，考量他們的真實意向就顯得極為重要。 文中使用中央研究院生醫所在其研究計畫「竹東及朴子地區心臟血管疾病長期追蹤研究」（CardioVascular Disease risk FACtor Two-township Study，簡稱CVDFACTS）第五循環中的研究調查資料。
由於以往的文獻對於「不知道」受訪者的處理，皆有不足之處。如Wang (1997)所提出的方法，就只能針對某種特定的「不知道」受訪者來做處理；而Caudill and Groothuis (2005)所提的方法，由於將「不知道」受訪者的差補與願付價格的估計分開，亦使其估計結果不具備一些好的性質。在本文中，我們提出一個能同時處理「不知道」受訪者且估計願付價格的方法。除了使得統計上較有效率外，也保有EM演算法的一個特性：願付價格模型中的估計參數為最大概似估計值。此外，在加入三要素混合模型(Tsai (2005))後，我們也可避免用到極端受訪者的訊息去差補那些「不知道」受訪者的意向。
在分析願付價格的過程中，我們發現此筆資料的「不知道」受訪者，其產生的機制為隨機，而非為完全隨機，這意謂著不考量「不知道」受訪者的分析結果，必定會產生偏差。而在比較有考量「不知道」受訪者與沒有的情況後，其結果確實應證了我們的想法：只要「不知道」受訪者不是完全隨機產生的，那麼不考量他們必定會產生某種程度的偏差。
This paper investigates how to deal with “Don’t Know” (DK) responses in contingent valuation surveys, which must be taken into consideration when they are not completely at random. The data we use is collected from the fifth cycle of the Cardiovascular Disease Risk Factor Two-township Study (CVDFACTS), which is a series of long-term surveys conducted by the Institute of Biomedical Sciences, Academia Sinica.
Previous methods used in dealing with DK responses have not been satisfactory because they only focus on some types of DK respondents (Wang (1997)), or separate the imputation of DK responses from the WTP estimation (Caudill and Groothuis (2005)). However, in this paper, we introduce an integrated method to cope with the incomplete data caused by DK responses. Besides being more efficient, the single-step method guarantees maximum likelihood estimates of the WTP model to be obtained due to the good property that the EM algorithm possesses. Furthermore, by adding the concept of the three-component mixture model (Tsai (2005)), some extreme information are drawn out when imputing the DK inclinations.
In this hypertension data, the mechanism of the DK responses is “Don’t know at random”, which means the analysis of DK-dropped results in a bias. By using our method, the difference between DK-dropped and DK-included is actually revealed, which proves our suspicion that a DK-dropped analysis is accompanied by a biased result when DK is not completely at random. |
| Reference: | Reference
Caudill, S. B., and P. A. Groothuis (2005), “Modeling Hidden Alternatives in Random Utility Models: An Application to ‘Don’t Know’ Responses in Contingent Valuation,” Land Economics 81(3): 445-454.
Cramer, J. S., and G. Ridder (1991), “Pooling States in the Multinomial Logit Model,” Journal of Econometrics 47 (2-3): 267-72.
Dempster, A. P., Laird, N. M. and Rubin, D. B. (1997), “Maximum likelihood from incomplete data via the EM algorithm (with discussion),” J. R. Statist. Soc. B, 39, 1-38.
Farewell, V. T. and Prentice, R. L. (1977), “A Study of Distributional Shape in Life Testing,” Technometrics, 19：69-75.
Groothuis, P. A., and J. C. Whitehead (2002), “Does Don’t Know Mean No? Analysis of ‘Don’t Know’ Responses in Dichotomous Choice Contingent Valuation Questions,” Applied Economics 34(15): 1935-40.
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Magder, L. S. and J. P. Hughes (1997), “Logistic Regression When the Outcome is Measured with Uncertainty,” American Journal of Epidemiology, 146(3): 195-203.
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Tsai, I.-L. (2005), “A Three-component Mixture Model in Willingness-to-pay
Analysis for General Interval Censored Data,” unpublished master’s thesis, National
Chengchi University, Taiwan.
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.
Wang, H. (1997), “Treatment of ‘Don’t Know’ Responses in Contingent Valuation Surveys: A Random Valuation Model,” Journal of Environmental Economics and Management 32(2): 219-32.
Yamaguchi, K. (1992), “Accelerated Failure-time Regression Models with a Regression Model of Surviving Fraction,” Journal of the American Statistical Association, 87: 284-292. |
| Description: | 碩士
國立政治大學
統計研究所
95354002
97 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0095354002 |
| Data Type: | thesis |
| Appears in Collections: | [統計學系] 學位論文
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