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Vasiliy Vel'dyaksov1, Alexey Shvedov1On Fuzzy Least-squares Regression Analysis
2014.
Vol. 18.
No. 2.
P. 328–344
[issue contents]
The data used in regression analysis may be inexact or uncertain. Uncertainty of data comes from randomness and from fuzziness. Statistical regression has many applications. But problems can occur, for instance, if the data set is too small, or there is difficulty verifying distribution assumptions. The standard econometric estimation is used when both the independent and dependent variables are given as real numbers. However, in many real-life situations only fuzzy data is available. The statistical techniques can be extended to include ambiguity of events. Fuzzy linear regression is a modelling techniques based on fuzzy set theory. It is applied to different areas such as finance, business administration and so on. The regression model with fuzzy data has been treated from diffferent points of view. Models where the variables are fuzzy or models where the relation of the variables is fuzzy may be considered.
Citation:
Vel'dyaksov V., Shvedov A. (2014) O metode naimen'shikh kvadratov pri regressii s nechetkimi dannymi [On Fuzzy Least-squares Regression Analysis]. HSE Economic Journal, vol. 18, no 2, pp. 328-344 (in Russian)
Keywords:
fuzzy linear regression;
least-squares estimates
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