Edgeworth Expansion of Densities of Order Statistics with Fixed Rank

We give an Edgeworth expansion for densities of order statistics with fixed rank k.The Edgeworth expansion for densities of extreme values is then obtained as a special case k=1.     

An Edgeworth Expansion for Studentized Finite Population Statistics

We show the validity of the one-term Edgeworth expansion for Studentized asymptotically linear statistics based on samples drawn without replacement from finite populations. Replacing the moments defining the expansion by their estimators we obtain an empirical Edgeworth expansion. We show the validity of the empirical Edgeworth expansion in probability.     

Edgeworth Expansions for Studentized Versions of Symmetric Finite Population Statistics

We show the validity of the one-term Edgeworth expansion for Studentized asymptotically linear statistics based on samples drawn without replacement from finite populations. Replacing the moments defining the expansion by their estimators, we obtain an empirical Edgeworth expansion. We show the validity of the empirical Edgeworth expansion in probability.     

An Asymptotic Expansion for the Distribution of Hotelling'sT-Statistic under Nonnormality

In this paper we obtain an asymptotic expansion for the distribution of Hotelling'sT²-statisticT²under nonnormality when the sample size is large. In the derivation we find an explicit Edgeworth expansion of the multivariatet-statistic. Our method is to use the Edgeworth expansion and to expand the characteristic function ofT².     

Edgeworth expansion in partial linear models

In this paper, under some fairly general conditions, a first-order Edgeworth expansion for the standardized statistic of β in partial linear models is given, then a non-residual type of consistent estimation for the error variance is constructed, and finally an Edgeworth expansion for the corresponding studentized version is presented.     

Improved confidence intervals for quantiles

We derive the Edgeworth expansion for the studentized version of the kernel quantile estimator. Inverting the expansion allows us to get very accurate confidence intervals for the pth quantile under general conditions. The results are applicable in practice to improve inference for quantiles when sample sizes are moderate.     

The Edgeworth expansion for distributions of extreme values

We present necessary and sufficient conditions of Edgeworth expansion for distributions of extreme values. As a corollary, rates of the uniform convergence for distributions of extreme values are obtained.     

部分线性模型中的Edgeworth展开

本文在相当一般的条件下，首先给出了部分线性模型中有关参数β的标准化统计量的一阶Ｅｄｇｅｗｏｒｔｈ展开，然后构造了误差方差的一个非残差型相合估计，最后给出了相应的学生化统计量的Ｅｄｇｅｗｏｒｔｈ展开．     

Edgeworth expansion in censored linear regression model

For the censored simple linear regression model, we establish a oneterm Edgeworth expansion for the Koul, Susarla and Van Ryzin type estimator of the regression coefficient. Our approach is to represent the estimator of the regression coefficient as an asymptoticU-statistic plus some ignorable terms and hence apply the known results on the Edgeworth expansions for asymptoticU-statistic. The counting process and martingale techniques are used to provide the proof of the main results.     

Onm-dependence and Edgeworth expansions

This paper contains two results. The first establishes, under mild assumptions, the validity of an Edgeworth expansion with remaindero(N ^–1/2) for aU-statistic with a kernel of degree two using observations from anm-dependent shift. The second result gives a necessary and sufficient condition for the distribution of a sum ofm-dependent random variables to possess an Edgeworth expansion. This generalizes a result of Bickel and Robinson from the i.i.d. case to them-dependent case.This research was supported in part by National Science Foundation, Grant DMS 89-23071.     

On the First-Order Edgeworth Expansion for a Markov Chain

We consider the first-order Edgeworth expansion for summands related to a homogeneous Markov chain. Certain inaccuracies in some earlier results by Nagaev are corrected and the expansion is obtained under relaxed conditions. An application of our result to the distribution of the mle of a transition probability in the countable state space case is also considered.     

Empirical Edgeworth Expansion for Finite Population Statistics. II

For symmetric asymptotically linear statistics based on simple random samples, we construct a one–term empirical Edgeworth expansion, where the moments defining the true Edgeworth expansion are replaced by their jackknife estimators. In order to establish the validity of the empirical Edgeworth expansion (in probability) we prove the consistency of the jackknife estimators.     

Edgeworth expansion for the kernel quantile estimator

Using the kernel estimator of the pth quantile of a distribution brings about an improvement in comparison to the sample quantile estimator. The size and order of this improvement is revealed when studying the Edgeworth expansion of the kernel estimator. Using one more term beyond the normal approximation significantly improves the accuracy for small to moderate samples. The investigation is non- standard since the influence function of the resulting L-statistic explicitly depends on the sample size. We obtain the expansion, justify its validity and demonstrate the numerical gains in using it.     

High-dimensional Edgeworth expansion of a test statistic on independence and its error bound

In this paper, we calculate Edgeworth expansion of a test statistic on independence when some of the parameters are large, and simulate the goodness of fit of its approximation. We also calculate an error bound for Edgeworth expansion. Some tables of the error bound are given, which show that the derived bound is sufficiently small for practical use.     

One- and Two-Term Edgeworth Expansions for Finite Population Sample Mean. Exact Results. II

We prove the validity of one- and two-term Edgeworth expansions under optimal conditions (a Cramer-type smoothness condition and the minimal moment conditions) and provide precise bounds for the remainders of expansions. The bounds depend explicitly on the ratio p=N/n, where N denotes the sample size and n the population size, respectively.     

Interpretation and manipulation of Edgeworth expansions

With a given Edgeworth expansion sequences of i.i.d. r.v.'s are associated such that the Edgeworth expansion for the standardized sum of these r.v.'s agrees with the given Edgeworth expansion. This facilitates interpretation and manipulation of Edgeworth expansions. The theory is applied to the power of linear rank statistics and to the combination of such statistics based on subsamples. Complicated expressions for the power become more transparent. As a consequence of the sum-structure it is seen why splitting the sample causes no loss of first order efficiency and only a small loss of second order efficiency.     

Empirical Edgeworth Expansion for Finite Population Statistics. I

For symmetric asymptotically linear statistics based on simple random samples, we construct the one-term empirical Edgeworth expansion, where the moments defining the true Edgeworth expansion are replaced by their jackknife estimators. In order to establish the validity of the empirical Edgeworth expansion (in probability), we prove the consistency of the jackknife estimators.     

半参数回归模型L-估计的渐近展开

对妆参数回归模型Ｙ＝x^Tβ＋g（ｔ）＋ε,构造了参数向量β的Ｌ－估计量－／λn,获得了－／λn的渐近正态性及分布的Ｅdgeworth展开,其速度可达到Ｏ(n^-1)。     

Weighted bootstrap for U-statistics

In this paper we investigate the weighted bootstrap for U-statistics and its properties. Under very general choices of random weights and certain regularity conditions, we show that the weighted bootstrap method with U-statistics provides second-order accurate approximations to the distribution of U-statistics. We shall prove this via one-term Edgeworth expansions of weighted U-statistics.     

Edgeworth expansion for ergodic diffusions

The Edgeworth expansion for an additive functional of an ergodic diffusion is validated under fairly weak conditions. The validation procedure does not depend on the stationarity or the geometric mixing property, but exploits the strong Markov property of the process. In particular for an Itô-diffusion of dimension one, verifiable conditions for the validity of the expansion are given in terms of the coefficients of the corresponding stochastic differential equation. The maximum likelihood estimator for the CIR process is treated as example.