Statistical analysis of non-normal distributions in high-dimensional data
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Abstract
This study addresses the challenges of analyzing high-dimensional insurance data characterized by non-normal distributions. By employing advanced statistical techniques, including transformation methods and dimensionality reduction strategies like Principal Component Analysis (PCA), we normalize skewed variables and simplify complex datasets effectively. Generalized Linear Models (GLMs) are used to identify significant predictors of policy charges, revealing how demographic factors such as age, sex, BMI, and lifestyle choices influence premium costs. The findings support personalized pricing strategies that reflect individual risk profiles more accurately. This research contributes to the development of sophisticated statistical models capable of managing complex datasets in the insurance sector, enhancing both operational efficiency and customer satisfaction.
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