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On blocking rules for the bootstrap with dependent data
SUMMARY We address the issue of optimal block choice in applications of the block bootstrap to dependent data. It is shown that optimal block size depends significantly on context, being equal toExpand
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Jackknife Empirical Likelihood
Empirical likelihood has been found very useful in many different occasions. However, when applied directly to some more complicated statistics such as U-statistics, it runs into seriousExpand
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Self-normalized Cramér-type large deviations for independent random variables
Let X 1 , X 2 ,... be independent random variables with zero means and finite variances. It is well known that a finite exponential moment assumption is necessary for a Cramer-type large deviationExpand
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An Exponential Nonuniform Berry-Esseen Bound for Self-Normalized Sums
In this paper we shall derive exponential nonuniform Berry-Esseen bounds in the central limit theorem for self-normalized sums. We show that the size of the error can be reduced considerably byExpand
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Empirical likelihood confidence regions for comparison distributions and ROC curves
Abstract: The authors derive empirical likelihood confidence regions for the comparison distribution of two populations whose distributions are to be tested for equality using random samples. AnotherExpand
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Two-sample empirical likelihood method
The empirical likelihood method is applied to the two-sample problem and is shown to be Bartlett correctable.
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On the sampling window method for long-range dependent data
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On the jump activity index for semimartingales
Empirical evidence of asset price discontinuities or “jumps” in financial markets has been well documented in the literature. Recently, Ait-Sahalia and Jacod (2009b) defined a general “jump activityExpand
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On Sample Reuse Methods for Dependent Data
We suggest a sample reuse method for dependent data, based on a cross between the block bootstrap and Richardson extrapolation. Instead of simulating a same size resample by resampling blocks andExpand
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Nonparametric estimate of spectral density functions of sample covariance matrices: A first step
The density function of the limiting spectral distribution of general sample covariance matrices is usually unknown. We propose to use kernel estimators which are proved to be consistent. AExpand
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