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The Comparison of Survival approach with Fuzzy Logic for National Stock Exchange data during Covid-19 in India

N. Mohan, M. Ramakrishnan, R. Ravanan


The goal of this study is to compare the survival methods like Kaplan-Meier, Weibull Smoothing with Fuzzy logic approach to the data sets related to National Stock Exchange (NSE) during Covid-19 in India. The study of Survival Analysis differs from other classical approaches because of censoring. The author introduced the event of study in this data as five different percentage of increase of share price from the minimum price of the share of companies during the study period. Traditionally, the nonparametric method of estimation of survival function is used to compute probability structure from step function. This approach considers the same value of probability between two consecutive event time points. Weibull smoothing concept gives solution for this problem but it does not give probability beyond last event time of study period and keeps probability value of final ordered failure time for remaining time. Fuzzy linear regression approach gives the value for all time period of study. The author collected share price of 131 Companies from 10 Sectorial indices of NSE from 1st April, 2020 to 31st March, 2021 during covid-19 in India and applied all methods for this NSE data and compared the three estimates and there by validate the model.


Survival Function, Kaplan-Meier Estimator, Weibull smoothing, Fuzzy Logic, Fuzzy Linear Regression

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