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一、引言 设(X,Y),(X_1,Y_1),…,(X_n,Y_n)为取值R~d×R的i.i.d变量,以F记X的分布,Y对X的回归函数为m(x)=E(Y|X=x)。(1)最近,一些作者讨论了回归函数的估计问题。一类非参数核估计定义为 相似文献
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金融市场的数据大多不是iid正态的,但是通常针对CAPM的Wald检验和F检验均是基于这一假设的。本文采用一种不需要iid正态假定的基于GMM方法的检验统计量,对同一原假设进行了检验,并且将结果与美国和澳大利亚股市的实证结果进行了比较。结果表明上海股市的确不满足iid正态,而且GMM统计量的检验结果和通常的检验存在差异并有可能影响到判别的结果。 相似文献
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In this note, we propose a squared error loss empirical Bayes estimator of θ based on past experiences and a present observation X which has conditional distribution. U(θ, cθ+b), where b is an arbitary constant when c>1; b>c when c=1, θ∈Ω=(-b/c-1,∞). When unkown prior G(θ) of θ belongs to the family {G:integral from Ω (θ~2dG(θ)<∞)}, our estimator is asymptotically optimal (see [1]). Let K(x) and k(x) be marginal distribution and density of r. v. X. It is easily seen that 相似文献
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Let (X, Y), (X_1, Y_1), …, (X_n, Y_n) be i. i. d. random vectors taking values in R_d×Rwith E(|Y|)<∞. To estimate the regression function m (x) = E (Y|X= x), we use thekernel estimate m_n(x)= sum from i=1 to n K((X_j-x)/h_n) where K(x) is a kernel functionand h_n a window width. In this paper, we establish the strong consistency of m_n(x) whenE(|Y|~P)<∞ for some p>l or E{exp(t|Y|~λ)}<∞ for some λ>0 and t>O. It is remakablethat other conditions imposed here are independent of the distribution of (X, Y). 相似文献