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Asymptotic Normality of Generalised Edge Frequency Polygon Estimator for Dependent Data

Yan Wang et al.

Australian and New Zealand Journal of Statistics2026https://doi.org/10.1111/anzs.70036article
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0.50

What the paper says

Density function estimation is a cornerstone of statistical analysis. This paper focuses on the generalised edge frequency polygon estimator, establishing its asymptotic normality for identically distributed ‐mixing random variables. This finding complements the asymptotic theory outlined by Dong and Zheng (2001. Generalized edge frequency polygon for density estimation. Statistics and Probability Letters, 55, 137–145). Theoretical results are substantiated through simulations that assess finite‐sample performance and an analysis of a real‐world dataset.

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https://doi.org/https://doi.org/10.1111/anzs.70036

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@article{yan2026,
  title        = {{Asymptotic Normality of Generalised Edge Frequency Polygon Estimator for Dependent Data}},
  author       = {Yan Wang et al.},
  journal      = {Australian and New Zealand Journal of Statistics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1111/anzs.70036},
}

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Asymptotic Normality of Generalised Edge Frequency Polygon Estimator for Dependent Data

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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