Bootstrap as a one-size-fits-all approach to quantifying uncertaintyZrimšek, Urša (Avtor) Demšar, Jure (Mentor) Uncertaintyconfidence intervalsbootstrap.Quantifying uncertainty is a vital part of every statistical study. There are many different methods, but in the hands of an inexperienced user, most of them can lead to big mistakes in the interpretation. Bootstrap is a favorable method for this task because of its robustness, versatility, ease of understanding and lack of stringent distributional assumptions. But even after 40 years of existence, it is not clear if this general method is accurate enough to substitute the traditional methods specialized for specific parameters of interest. To answer this, we designed an extensive simulation study that assesses the methods' confidence interval prediction for six different parameters on samples of multiple sizes, generated from seven diverse distributions. We chose the double bootstrap as the best general bootstrap method, additionally recommending the standard bootstrap for confidence intervals for extreme percentiles. We compared the best bootstrap methods to the traditional methods and found out that for almost all of the parameters no traditional method is practically better. Moreover, bootstrap gives good predictions even on the distributions where traditional methods fail because of broken assumptions. Our work thus suggests that estimates generated by the proposed bootstrap methods are comparable to or even better than the ones made by the traditional methods.20232023-12-22 07:30:00Magistrsko delo/naloga153314sl