Robustness In Data Analysis Criteria And Methods By Shevlyakov
Explore the essential criteria and methods for achieving robustness in data analysis, as presented in Shevlyakov's work. Learn how to ensure your analysis remains reliable and accurate even when dealing with outliers, errors, or deviations from standard assumptions. This approach focuses on techniques that mitigate the impact of problematic data points, leading to more dependable and trustworthy insights from your data analysis projects.