What are some robust methods for data analysis?

What are some robust methods for data analysis?

Robust methods are statistical techniques that provide reliable results even when assumptions are violated. Examples include robust regression, bootstrapping, and the use of M-estimators.

How does robust regression differ from ordinary least squares?

Robust regression minimizes the influence of outliers by using different loss functions, while ordinary least squares minimizes the sum of squared residuals, which can be heavily affected by outliers.

Can you explain bootstrapping in simple terms?

Bootstrapping is a resampling technique that involves repeatedly drawing samples from a dataset with replacement to estimate the distribution of a statistic.

What are M-estimators and when should I use them?

M-estimators are a generalization of maximum likelihood estimators and are used when you want to estimate parameters in a way that is robust to certain violations of model assumptions.