关于有界核两样本U统计量余项的指数收敛速度

设Ｕｍｎ是以有界对称函数　（ｘ，ｙ）为核的两样本Ｕ－统计量．本文讨论了Ｕｍｎ与其投影之差的指数收敛速度问题，它有别于Ｈｏｅｆｆｄｉｎｇ关于有界核Ｕ－统计量的指数不等式．是两样本Ｕ－统计量所特有的．     

两个连续分布函数交点的估计

利用两样本的秩统计量和次序统计量 ,对两连续分布交点提出了一种新的点估计量和区间估计量 ,并论证了点估计的强相合性及渐近正态性 ,区间估计具有指定的渐近置信系数 ,还给出了其渐近精度     

两样本队列半数生存期检验:反射和检基统计量

依据反射或检基原理，本文提出用于截尾资料的两样本队列半数生存期（ＣＨＬ）检验．两样本合并ＣＨＬ经指数内插取自Ｋａｐｌａｎ－Ｍｅｉｅｒ或Ｂｅｒｋｓｏｎ－Ｇａｇｅ估计值．连续性校正，经以有效样本容量取代样本容量，扩展自Ｙａｔｅｓ校正．合并标准误来自同源性生存率方差估计值，后者经有效样本容量扩展自二项分布方差．无截尾时，这些统计量还原为经典中位数检验．与反射统计量相比，检基统计量具有更高的功效．附有工作实例描述其临床应用．     

极值分布和威布尔分布异常数据的检验方法

本文对威布尔分布的极值分布异常数据的检验给出了一系列的方法，首先，导入了极值分布下一般Ｄｉｘｏｎ型统计量的精确分布，同时还给出了改进的Ｇ型统计量，及它们的分位点表。最后本文提出了一个新的统计量；Ｆ型统计量，并用Ｍｏｎｔｅ－Ｃａｒｌｏ模拟的方法给出其分位点表，从而首次给出威布尔分布异常值的直接检验方法。本文进一步讨论了这些检验方法的功效，且表明Ｆ型检验是最优的。     

一种Wilcoxon型统计量的中心极限定理

本文给出了一种类似于两样本Ｗｉｌｃｏｘｅｎ统计量的秩和统计量的中心极限定理，特别地当变量服从球对称分布Ｐ时，其极限分布与分布Ｐ无关。此统计量可对分布的球对称性进行初步的检验。     

次序统计量线性组合之分布及其在可靠性统计中的应用

本文利用特征函数的方法,给出了来自于自由度为自然数r的Γ—分布的次序统计量线性组合之精确分布;并将其用于可靠性定数截尾试验中,得到了指数分布的似然比检验统计量之精确分布。     

加权PP型Crmer-Von Mises统计量的极限分布及其展开式

给出了高维分布假设检验中的加权ＰＰ型Ｃｒａｍｅｒ－Ｖｏｎ Ｍｉｓｅｓ检验统计量，并在零假设为一般分布情形下获得其统计量的极限分布及其分布展开式．     

方差未知时两样本正态混合模型齐-性的修正似然比检验

本文讨论了方差未知时检验两样本正态混合模型齐一性的修正似然比统计量的极限性质,证明了原假设下修正似然比统计量的渐近分布为自由度为1的卡方分布.     

方差未知时两样本正态混合模型齐一性的修正似然比检验

本文讨论了方差未知时检验两样本正态混合模型齐一性的修正似然比统计量的极限性质,证明了原假设下修正似然比统计量的渐近分布为自由度为1的卡方分布.     

关于拟合优度检验的EDF统计量的若干评注

拟合优度检验是建立统计模型的一个重要手段，很多检验统计量用一个理想样本能达到它们自己的极值，但ＥＤＦ统计量做不到，这无疑会影响检验的势，在本文中，我们将提出某些调整型ＥＤＦ统计量，它们具有这些性质，并改进了ＥＤＦ检验，蒙得卡罗模拟表明，调整型ＥＤＦ统计量在很多场合要必ＥＤＦ具有更高的优势，特别对重尾的备选分布更是这样，我们还考察了检验的形态与它们的极值点之间的关系。     

A note on the asymptotic distribution of the maximum likelihood estimator in a non-regular case

We consider the asymptotic distribution of the maximum likelihood estimator (MLE), when the log-likelihood ratio statistic weakly converges to the non-degenerated Gaussian process. We provide a simple expression for the density function of the asymptotic distribution by fundamental stochastic results. This note is helpful to investigate asymptotic properties of the MLE in a certain non-regular case.     

Asymptotic properties of sample quantiles of discrete distributions

The asymptotic distribution of sample quantiles in the classical definition is well-known to be normal for absolutely continuous distributions. However, this is no longer true for discrete distributions or samples with ties. We show that the definition of sample quantiles based on mid-distribution functions resolves this issue and provides a unified framework for asymptotic properties of sample quantiles from absolutely continuous and from discrete distributions. We demonstrate that the same asymptotic normal distribution result as for the classical sample quantiles holds at differentiable points, whereas a more general form arises for distributions whose cumulative distribution function has only one-sided differentiability. For discrete distributions with finite support, the same type of asymptotics holds and the sample quantiles based on mid-distribution functions either follow a normal or a two-piece normal distribution. We also calculate the exact distribution of these sample quantiles for the binomial and Poisson distributions. We illustrate the asymptotic results with simulations.     

Asymptotics for M/G/1 low-priority waiting-time tail probabilities

We consider the classical M/G/1 queue with two priority classes and the nonpreemptive and preemptive-resume disciplines. We show that the low-priority steady-state waiting-time can be expressed as a geometric random sum of i.i.d. random variables, just like the M/G/1 FIFO waiting-time distribution. We exploit this structures to determine the asymptotic behavior of the tail probabilities. Unlike the FIFO case, there is routinely a region of the parameters such that the tail probabilities have non-exponential asymptotics. This phenomenon even occurs when both service-time distributions are exponential. When non-exponential asymptotics holds, the asymptotic form tends to be determined by the non-exponential asymptotics for the high-priority busy-period distribution. We obtain asymptotic expansions for the low-priority waiting-time distribution by obtaining an asymptotic expansion for the busy-period transform from Kendall's functional equation. We identify the boundary between the exponential and non-exponential asymptotic regions. For the special cases of an exponential high-priority service-time distribution and of common general service-time distributions, we obtain convenient explicit forms for the low-priority waiting-time transform. We also establish asymptotic results for cases with long-tail service-time distributions. As with FIFO, the exponential asymptotics tend to provide excellent approximations, while the non-exponential asymptotics do not, but the asymptotic relations indicate the general form. In all cases, exact results can be obtained by numerically inverting the waiting-time transform. This revised version was published online in June 2006 with corrections to the Cover Date.     

一类Gauss序列极值的极限律

本文讨论一类非平稳Gauss序列的极值．利用点过程收敛定理得到多水平超过的点过程的收敛性,同时得到在不相交区间上最大值的联合渐近分布,第k个最大值的渐近分布以及前r个极值的联合渐近状态．     

L_1 regression estimate and its bootstrap

We study the asymptotic distribution of the L1 regression estimator under general condi-tions with matrix norming and possibly non i.i.d.errors.We then introduce an appropriate bootstrap procedure to estimate the distribution of this estimator and study its asymptotic properties.It is shown that this bootstrap is consistent under suitable conditions and in other situations the bootstrap limit is a random distribution.     

The effects of nonnormality on asymptotic distributions of some likelihood ratio criteria for testing covariance structures under normal assumption

This paper examines asymptotic distributions of the likelihood ratio criteria, which are proposed under normality, for several hypotheses on covariance matrices when the true distribution of a population is a certain nonnormal distribution. It is well known that asymptotic distributions of test statistics depend on the fourth moments of the true population's distribution. We study the effects of nonnormality on the asymptotic distributions of the null and nonnull distributions of likelihood ratio criteria for covariance structures.     

Asymptotics for the ratio and the zeros of multiple Charlier polynomials

We investigate multiple Charlier polynomials and in particular we will use the (nearest neighbor) recurrence relation to find the asymptotic behavior of the ratio of two multiple Charlier polynomials. This result is then used to obtain the asymptotic distribution of the zeros, which is uniform on an interval. We also deal with the case where one of the parameters of the various Poisson distributions depends on the degree of the polynomial, in which case we obtain another asymptotic distribution of the zeros.     

On the asymptotic behavior of eigenvalues of the radial p-laplacian

We study the asymptotic behavior and distribution of the eigenvalues of the singular radial p-laplacian. We prove a Weyl type asymptotic formula for the number of eigenvalues less than a given value.     

On the asymptotic distribution of the Koenker–Bassett estimator for a parameter of the nonlinear model of regression with strongly dependent noise

We prove that, under certain regularity conditions, the asymptotic distribution of the Koenker – Bassett estimator coincides with the asymptotic distribution of the integral of indicator process generated by a random noise weighted by the gradient of the regression function.     

On the asymptotic properties of multivariate sample autocovariances

We show that if a process can be obtained by filtering an autoregressive process, then the asymptotic distribution of sample autocovariances of the former is the same as the asymptotic distribution of linear combinations of sample autocovariances of the latter. This result is used to show that for small lags the sample autocovariances of the filtered process have the same asymptotic distribution as estimators utilizing more information (observations on the associated autoregression process and knowledge of the parameters of the filter). In particular, for a Gaussian ARMA process the first few sample autocovariances are jointly asymptotically efficient.