@article{article_437725, title={An Integrated approach for fuzzy logistic regression}, journal={İstatistikçiler Dergisi:İstatistik ve Aktüerya}, volume={11}, pages={42–54}, year={2018}, author={Pehlivan, Nimet Yapıcı and Şahin, Aynur}, keywords={Fuzzy Logistic Regression,Possibilistic odds,Fuzzy Least Squares}, abstract={<p> <span style="font-size:9pt;font-family:’Times New Roman’;">The aim of this study is to introduced an integrated
fuzzy logistic regression approach to describe the relationship between crisp
inputs and fuzzy binary output. For this reason, we integrated the fuzzy
logistic regression methods proposed by Pourahmad et al. [17]  and
Sohn et al. [24] to define a possibility
measure for each case and then used the logarithmic transformation of
possibilistic odds as fuzzy output observations. To estimate the parameters of
the fuzzy logistic regression model, Diamond [5]’s
Fuzzy Least Squares (FLS) approach is used. A numerical example is presented
and obtained results are compared with classic logistic regression model. </span> <br /> </p>}, number={1}, publisher={Aktüerya Derneği}