A sharper Lyapunov–Katz central limit error bound for i.i.d. summands Zolotarev-close to normal

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The analysis demonstrates a sharpened central limit error bound for convolution powers of laws, indicating improved approximations to normality.

Sharpened error bounds improve the understanding of convolution powers, enhancing insights on normal closure.
The analysis focuses on laws with finite moments, showing adaptability in accounting for proximity to normal distribution.
Partial generalisation of established theorems enhances the existing framework of the Katz error bound on i.i.d. cases.
These results point to potential improvements in statistical inference where convolution powers are relevant.

Jonas et al. (2025) studied this question.

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View Full Paper

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Authors

The analysis demonstrates a sharpened central limit error bound for convolution powers of laws, indicating improved approximations to normality.

Sharpened error bounds improve the understanding of convolution powers, enhancing insights on normal closure.
The analysis focuses on laws with finite moments, showing adaptability in accounting for proximity to normal distribution.
Partial generalisation of established theorems enhances the existing framework of the Katz error bound on i.i.d. cases.
These results point to potential improvements in statistical inference where convolution powers are relevant.

Jonas et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark