Evaluating A New Adaptive Group Lasso Imputation Technique For Handling Missing Values In Compositional Data
Pie chart is a widely used statistical chart to represent the proportions of various components in a certain entity. The shares of data in a pie chart, also known as compositional data, consist of non-negative values, containing only relative information. However, in many real-life domains, a sub...
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| Format: | Thesis |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | http://eprints.usm.my/62560/ http://eprints.usm.my/62560/1/TIAN%20YING%20-%20TESIS%20cut.pdf |
| Summary: | Pie chart is a widely used statistical chart to represent the proportions of
various components in a certain entity. The shares of data in a pie chart, also known
as compositional data, consist of non-negative values, containing only relative
information. However, in many real-life domains, a substantial amount of missing
values is often collected. The complexity of compositional data with missing values
renders traditional estimation methods inadequate. In this thesis, a compositional
data imputation method designed based on LASSO is proposed combining group
LASSO and adaptive LASSO analysis methods. The estimation effects of highdimensional
and low-dimensional compositional data with missing values are
compared through simulation studies and case analyses under different missing rates,
dimensions, and correlation coefficients. Considering the impact of outliers on the
accuracy of estimation, both simulation and case analysis are conducted to compare
the proposed algorithm against four existing methods. The experimental results
demonstrate that the proposed adaptive group LASSO method produces a better
imputation performance, MSE, MADE, RMSE and NRMSE increased by up to
26.6% at selected missing rates. Future work analyses the effect of imputation under
continuous missing rates, MAR missing mechanism and more model evaluation
criteria. |
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