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- W2942127943 abstract "Summary We establish an approximation theory for Pearson’s chi-squared statistics in situations where the number of cells is large, by using a high-dimensional central limit theorem for quadratic forms of random vectors. Our high-dimensional central limit theorem is proved under Lyapunov-type conditions that involve a delicate interplay between the dimension, the sample size, and the moment conditions. We propose a modified chi-squared statistic and introduce an adjusted degrees of freedom. A simulation study shows that the modified statistic outperforms Pearson’s chi-squared statistic in terms of both size accuracy and power. Our procedure is applied to the construction of a goodness-of-fit test for Rutherford’s alpha-particle data." @default.
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- W2942127943 date "2019-04-22" @default.
- W2942127943 modified "2023-10-14" @default.
- W2942127943 title "Pearson’s chi-squared statistics: approximation theory and beyond" @default.
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- W2942127943 doi "https://doi.org/10.1093/biomet/asz020" @default.
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