Talha Nur Omer

Abstract This research proposes four new optional and partial quantitative randomized response models to be used for the estimation of mean and sensitivity level of quantitative variables. These models are constructed based on the current quantitative scrambling and randomization methods and seek to produce unbiased estimators with better efficiency and privacy.

1 Introduction Crude oil represents a critical macroeconomic factor in global commodity markets, with price volatility significantly influencing the global economy (Ahmed et al. 2012; Pinno and Serletis 2013). Hence, accurate forecasts of crude oil price volatility are essential for asset pricing (Arouri et al. 2012; Xiao et al. 2018), asset allocation (Arouri et al. 2011; Lin et al. 2014; Hadhri 2021), and risk management (Masih et al. 2011; Choi and Kim 2018), as indicated by various studies.

@ DataLLigence My guess is, the definition of churners should be modified. Well what we are trying to predict is 'soft' churn i.e. our analysis showed that 20 days of inactivity eventually leads to hard churn. So we actually are trying to model the 20 day dormant behavior and predict subscribers with an inactivity of 20 days. Could you suggest any alternative churn definitions from your experiences? I have a few questions/suggestions: 1.