<p>The Multivariate version of Le&#xa0;Cam’s theorem states that any sum of shifted independent identically distributed random vectors can be approximated by an accompanying compound Poisson law with accuracy of the order <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="10959_2025_1435_Article_IEq1.gif" Format="GIF" Height="21" Rendition="HTML" Resolution="72" Type="Linedraw" Width="65" /> </InlineMediaObject> <EquationSource Format="TEX">\(O(n^{-1/3})\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>O</mi> <mo stretchy="false">(</mo> <msup> <mi>n</mi> <mrow> <mo>-</mo> <mn>1</mn> <mo stretchy="false">/</mo> <mn>3</mn> </mrow> </msup> <mo stretchy="false">)</mo> </mrow> </math></EquationSource> </InlineEquation>. We show that a suitably chosen asymptotic expansion can improve the accuracy of approximation. Results of this paper are closely related to the first uniform Kolmogorov theorem.</p>

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Asymptotics in Multivariate Le Cam’s Theorem

  • V. Čekanavičius,
  • S. Jokubauskienė

摘要

The Multivariate version of Le Cam’s theorem states that any sum of shifted independent identically distributed random vectors can be approximated by an accompanying compound Poisson law with accuracy of the order \(O(n^{-1/3})\) O ( n - 1 / 3 ) . We show that a suitably chosen asymptotic expansion can improve the accuracy of approximation. Results of this paper are closely related to the first uniform Kolmogorov theorem.