Possibilistic linear regression analysis for fuzzy data

Tanaka, Hideo; Hayashi, Isao; Watada, Junzo

doi:10.1016/0377-2217(89)90431-1

Cited by 331 publications

(142 citation statements)

References 7 publications

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“…However, fuzzy regression analysis views these errors as the underlying uncertainty or fuzziness that exists within the model structure, as proposed by Tanaka et al (1982Tanaka et al ( , 1988Tanaka et al ( , 1989. This being the case, according to Chang and Ayyub (2001), statistical regressions are meant for handling random errors determined from crisp estimated and observed data.…”

mentioning

confidence: 99%

Assessing productive efficiency of banks using integrated Fuzzy-DEA and bootstrapping: A case of Mozambican banks

Wänke

Barros²,

Emrouznejad

2016

European Journal of Operational Research

133

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mentioning

confidence: 99%

Assessing productive efficiency of banks using integrated Fuzzy-DEA and bootstrapping: A case of Mozambican banks

Wänke

Barros²,

Emrouznejad

2016

European Journal of Operational Research

133

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“…For the first and second states the same process is applied. Table 1 (information in Table 1 has been adopted from reference [20]). Table 1: Crisp input-fuzzy output data set from Using these data, develop an estimated fuzzy regression equation Y i by = .…”

Section: Weight Calculation Algorithm For the Third Statementioning

confidence: 99%

Numerical Solution of Fuzzy linear Regression using Fuzzy Neural Network Based on Probability Function

Ezadi¹,

Askari²

2017

Journal of Fuzzy Set Valued Analysis

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In this work, we consider the development of a fuzzy neural network based on probability function for Estimated output of fuzzy regression models with test real input and fuzzy output. The proposed approach is a fuzzification of the outputs and weights of conventional fuzzy neural network based on probability function. The error of the proposed method is based on total square error is minimized by optimization method in order to be able to obtain the optimal weights of the neural network. The advantage of the proposed approach is its simplicity and computation as well as its performance. To compare the performance of the proposed method with the other traditional methods given in the literature several numerical examples are presented.

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“…Fuzzy regression analysis was first introduced by Tanaka and Hayashi [7] and Tanaka et al [8] in order to apply linguistic or vague data to regression analysis. Fuzzy regression analysis using triangular fuzzy numbers and trapezoidal fuzzy number have been studied in many works [9][10][11][12][13][14][15].…”

Section: Fuzzy Regression Model Using Trapezoidal Fuzzy Numbersmentioning

confidence: 99%

“…Therefore, the value of real estate at re-auction can be expressed in term of trapezoidal fuzzy numbers as follows (See Figure 2): (8, 9, 10, 10)(very good), (7,8,8,9)(good), (5,6,7,8)(somewhat good), (4,5,5,6)(average), (2, 3, 4, 5) (somewhat bad), (1, 2, 2, 3)(bad), (0, 0, 1, 2)(very bad).…”

Section: Fuzzy Regression Model For Re-auction Datamentioning

confidence: 99%

Fuzzy Regression Model Using Trapezoidal Fuzzy Numbers for Re-auction Data

Kim¹,

Lee²,

Lee³

et al. 2016

IJFIS

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Re-auction happens when a bid winner defaults on the payment without making second in-line purchase declaration even after determining sales permission. This is a process of selling under the court's authority. Re-auctioning contract price of real estate is largely influenced by the real estate business, real estate value, and the number of bidders. This paper is designed to establish a statistical model that deals with the number of bidders participating especially in apartment re-auctioning. For these, diverse factors are taken into consideration, including ratio of minimum sales value from the point of selling to re-auctioning, number of bidders at the time of selling, investment value of the real estate, and so forth. As an attempt to consider ambiguous and vague factors, this paper presents a comparatively vague concept of real estate and bidders as trapezoid fuzzy number. Two different methods based on the least squares estimation are applied to fuzzy regression model in this paper. The first method is the estimating method applying substitution after obtaining the estimators of regression coefficients, and the other method is to estimate directly from the estimating procedure without substitution. These methods are provided in application for re-auction data, and appropriate performance measure is also provided to compare the accuracies.

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Possibilistic linear regression analysis for fuzzy data

Cited by 331 publications

References 7 publications

Assessing productive efficiency of banks using integrated Fuzzy-DEA and bootstrapping: A case of Mozambican banks

Assessing productive efficiency of banks using integrated Fuzzy-DEA and bootstrapping: A case of Mozambican banks

Numerical Solution of Fuzzy linear Regression using Fuzzy Neural Network Based on Probability Function

Fuzzy Regression Model Using Trapezoidal Fuzzy Numbers for Re-auction Data

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