ЛИНЕЙНАЯ РЕГРЕССИЯ С ОТСУТСТВИЕМ ДАННЫХ НЕ СЛУЧАЙНО: МЕТОД БУТСТРАПА

Аннотация

OLS regressions have a set of assumption in order to have its point and interval estimates to be unbiased and efficient. Data missing not at random (MNAR) can pose serious estimations issues in the linear regression. In this study we evaluate the performance of OLS confidence interval estimates with MNAR data. We also suggest bootstrapping as a remedy for such data cases and compare the traditional confidence intervals against bootstrap ones. As we need to know the true parameters, we carry out a simulations study. Research results indicate that both approaches show similar results having similar intervals size. Given that bootstrap required a lot of computations, traditional methods is still recommended to be used even in case of MNAR Key words: linear model, sample size, confidence Interval, bootstrap, accuracy, interval size, missing not at random