| Title |
Methods of multiple imputation / |
| Translation of Title |
Daugialypiai praleistų reikšmių įrašymo metodai. |
| Authors |
Silkauskaitė, Iveta |
| Full Text |
|
| Pages |
47 |
| Keywords [eng] |
Praleistos reikšmės, Įrašymas, Daugialypis įrašymas, MICE. |
| Abstract [eng] |
Missing data is a major problem affecting decision-making processes and analysis results. The thesis reviews existing literature on multiple imputation applications and gives insights about the usage of MICE and other methods for data imputation in Python. The study consists of a simulation study and case study where real-world data was analysed and simulation results were confirmed. Imputation methods are compared by different data scenarios: data size, missingness rate, missingness type. The thesis could help practitioners while considering which data imputation method in which case to choose. The study showed that the most effective and universal method currently is MICE with Linear regression, Bayesian Ridge and Lasso estimators. |
| Dissertation Institution |
Vilniaus universitetas. |
| Type |
Master thesis |
| Language |
English |
| Publication date |
2025 |
