Abstract
Our method of estimation of parameters in statistics uses a set of confidence intervals producing a triangular shaped fuzzy number for the estimator. Using this fuzzy estimator in hypothesis testing produces a fuzzy test statistic and fuzzy critical values in fuzzy hypothesis testing. We show how these fuzzy sets may be used to derive the usual conclusion of: (1) reject the null hypothesis, or (2) do not reject the null hypothesis.
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Buckley JJ (2003) Fuzzy probabilities: new approach and applications. Physica-Verlag, Heidelberg
Buckley JJ, Eslami E (2003) Uncertain probabilities I: the discrete case. Soft Comput 7: 500–505
Buckley JJ, Eslami E (2004) Uncertain probabilities II: the continuous case. Soft Comput 8: 193–199
Buckley JJ (2004) Uncertain probabilities III: the continuous case. Soft Comput 8: 200–206
Buckley JJ, Reilly K, Zheng X Fuzzy probabilities for web planning. Soft Comput (To appear)
Buckley JJ (2004) Fuzzy probabilities and fuzzy sets for web planning. Springer, Berlin Heidelberg. New York
Finney RL, Thomas GB (1994) Calculus, 2nd edn. Addison-Wesley, New York
Maple 6, Waterloo Maple Inc., Waterloo, Canada
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Buckley, J. Fuzzy statistics: hypothesis testing. Soft Comput 9, 512–518 (2005). https://doi.org/10.1007/s00500-004-0368-5
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DOI: https://doi.org/10.1007/s00500-004-0368-5