A Modification to the Fuzzy Regression Discontinuity Model to Settings with Fuzzy Variables ()
ABSTRACT
Despite the fact that fuzzy regression discontinuity designs are growing
in popularity, a lot of research takes into account
treatment non-compliance difficulties, specifically the fuzziness of the treatment
impact. This paper took into account independent and dependent fuzzy factors
when creating these designs. Additionally we took into account treatment
non-compliance difficulties, specifically the fuzziness of the treatment
impact, as other research does. The modified Fuzzy Regression Discontinuity
model is preferable for modeling fuzzy data. It enables us to draw improved
causal effects accommodating fuzzy variables, not just the fuzziness of the
treatment effect as in Fuzzy Regression Discontinuity models. A fuzzy dataset
is converted into crisp data by the Centroid method of defuzzification. Once
the data is crisp, the traditional least squares methods of approximation are
used to estimate the parameters in the model
since these parameters are considered crisp whilst the error terms are
fuzzy. The Alcohol Use Disorders Identification Test score(AUDIT score) can be
used as a cutoff to initiate treatment in this case and can be used to predict the progression of HIV disease and/or
AIDS. Counseling helps to lower the use of alcohol in people living with
HIV/AIDS (PLWHA) as a result, improving the participants’ CD4 counts.
Share and Cite:
Mafukidze, P. , Mwalili, S. and Mageto, T. (2022) A Modification to the Fuzzy Regression Discontinuity Model to Settings with Fuzzy Variables.
Open Journal of Statistics,
12, 676-690. doi:
10.4236/ojs.2022.125040.
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