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Divergences test statistics for discretely observed diffusion processes

机译:离散观察扩散过程的散度检验统计

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摘要

In this paper we propose the use of φ{symbol} - divergences as test statistics to verify simple hypotheses about a one-dimensional parametric diffusion process d Xt = b (Xt, α) d t + σ (Xt, β), α ∈ Rp, β ∈ Rq, p, q > = 1, from discrete observations { Xti, i = 0, ..., n } with ti = i Δn, i = 0, 1, ..., n, under the asymptotic scheme Δn → 0, n Δn → ∞ and n Δn 2 → 0. The class of φ{symbol} - divergences is wide and includes several special members like Kullback-Leibler, Rényi, power and α - divergences. We derive the asymptotic distribution of the test statistics based on the estimated φ{symbol} - divergences. The asymptotic distribution depends on the regularity of the function φ{symbol} and in general it differs from the standard χ2 distribution as in the i.i.d. case. Numerical analysis is used to show the small sample properties of the test statistics in terms of estimated level and power of the test. © 2010 Elsevier B.V. All rights reserved.
机译:在本文中,我们建议使用φ{symbol}-散度作为检验统计量,以验证关于一维参数扩散过程d Xt = b(Xt,α)dt +σ(Xt,β),α∈Rp的简单假设,β∈Rq,p,q> = 1,在渐近格式下,来自离散观测{Xti,i = 0,...,n}且ti = iΔn,i = 0,1,...,n Δn→0,nΔn→∞和nΔn2→0。φ{symbol}-散度的类别很宽,包括Kullback-Leibler,Rényi,幂和α-散度等几个特殊成员。我们基于估计的φ{symbol}-散度推导检验统计量的渐近分布。渐近分布取决于函数φ{symbol}的正则性,通常与i.i.d中的标准χ2分布不同。案件。数值分析用于显示测试统计量的小样本属性,以估计的水平和测试能力为依据。 ©2010 Elsevier B.V.保留所有权利。

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