A ESTIMATING MALAYSIAN ECOTOURISM DEMAND USING FUZZY MODEL (CASE STUDY OF STATE OF MALACCA)
Fuzzy regression model (FRM) is an alternative to evaluate the relation between variables among the forecasting models, when the data are not sufficient to identify the relation and we have uncertainty in data. Behavior and thinking of the people, with their differences on emotions and intuitions, produce the ambiguity in social studies. Instead of using point estimation in conventional probability theory, fuzzy set theory can be used to granulate a concept into a set with membership function and thus decreases the amount of required data. In the last two decades, Fuzzy Sets Theory that was introduced in 1965 by Prof. Lotfi A. Zadeh has received wide attention particularly with successful applications in various fields ranging from engineering to social science studies. Besides being an alternative to Conventional Regression Model (CRM), the Fuzzy Regression Model (FRM) has the ability to evaluate relationships between variables when the data series are limited and uncertain. Moreover, the fuzzy regression is able to address the ambiguity in the variables, such as human behavior, thinking and subjectivity. In this paper, we estimate the demand model for ecotourism in Malacca state by using FRM as an alternative approach. Results show that trip cost, opportunity cost, time and income are the significant variables that could explain variation in number of trip as dependent variable.
Badaruddin Mohamed. (2002). The development of ecotourism in Malaysia – Is it really sustainable? Paper presented at Conference in Chiang Mai, Thailand, 3-7 March.
Bardossy A., I. Bogardi, and L. Duckstein. (1990). Fuzzy regression in hydrology. Water Resources Res, 26, 1497-1508.
Chang, P., Lee, S.E., and Konz, S.A. (1996), Applying fuzzy linear regression to VDT legibility. Fuzzy Sets and Systems, 80, 197-204.
Hsiao-Fan Wang and Ruey-Chyn Tsaur. (2002). Theory and methodology resolution of fuzzy regression model. European Journal of Operation Research 126, 637
Koo H. J., Shin H. S. and Yoo H. C. (1991). Multiobjective siting planning for a regional hazardous waste treatment center. Waste Management Research, 9, 205-218
Julien, B. (1994). Water quality management with important information. European Journal of Operational Research. 76, 15-27.
Moskowitz H. and K. Kim, On. (1993). Assessing the h value in fuzzy linear regression. Fuzzy Sets System, 58,303-327.
Ni-Bin Chang, C. G. Wen, Y. L. Chen, and Y. C. Yong. (1996). A gray fuzzy multiobjective programming approach for the optimal planning of a reservoir watershed. Water Resource Journal, 30 (10), 2329-2334.
Tanaka H,. and Watada J. (1988). Possibilistic linear systems and their application to the linear regression model. Fuzzy Sets System, 27, 275-289.
Toshiro Terano, Kiyoji Asai, Michio Sugeno (1992). Fuzzy Systems Theory and Its Application.
Y.H. Chen , Wen-June Wang and Chih-Hui Chiu. (2000). New estimation method for the membership values in fuzzy sets. Fuzzy Sets and Systems, 112, 521-525.
Zadeh L. A. (1965). Fuzzy Sets, Inform and Control, 8, 338-353.
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