一类密度估计的收敛速度

ＰｒａｋａｓａＲａｏ在文献［１］中提出一类密度估计ｆｎ（ｘ），我们得到当ｘ固定时ｆｎ（ｘ）－ｆ（ｘ）的ａ．ｓ．收敛速度及ｆｎ（ｘ）正态逼近的Ｂｅｒｒｙ－Ｅｓｓｅｅｎ界，同时，给出ｓｕｐｘ｜ｆｎ（ｘ）－ｆ（ｘ）｜的一致收敛速度     

密度泛函核估计的Bootstrap逼近

设Ｘ１，．．．，Ｘｎ是从分布密度函数为ｆ的总中抽取的ｉｉｄ样本，μ＝ＥＸ１本文研究了密度泛函θ＝ｆ（μ）的核型估计，ｆｎ（ｘ）为通常的Ｒｏｓｅｎｂｌａｔｔ－Ｐａｒｚｅｎ核估计。     

空间R~m上分布的中性卷积

取定具下述性质的函数ｒ（ｘ）∈Ｃ∞（Ｒ）：（ｉ），τ（ｘ）＝τ（－ｘ），（ｉｉ）０≤τ（ｘ）≤１，（ｉｉｉ）τ（ｘ）＝１，当｜ｘ｜≤１／２，（ｉｖ）τ（ｘ）＝０，当｜ｘ｜≥１．单位序列｛τｎ（ｘ）｝，ｘ∈Ｒｍ和ｎ∈Ｉｍ，定义作τｍ（ｘ）＝τ（ｘ１／ｎ１）…τ（ｘｍ／ｎｍ），ｎ１，…，ｎｍ＝１，２，…．空间Ｄ’（Ｒｍ）中分布ｆ和ｇ的中性卷积ｆｇ定义作序列｛ｆｎ＊ｇ｝的极限，其中ｆｎ＝ｆ·τｎ．作者给出了一些新的卷积．     

关于相关收敛函数序列的一个命题

本文在［１］，［２］的基础上，给出了满足ｇｎ（ｘ）＝Ｃｏｆｎ（ｘ）＋Ｃ１ｆｎ－１（ｘ）＋…＋Ｃｋｆｎ－ｋ（ｘ）的相关函数序列｛ｆｎ（ｘ）｝和｛ｇｎ（ｘ）｝的极限间制约关系的一个命题，从而得到一种判断函数序列一致收敛并且求其极限函数的方法。     

相依样本下条件密度双重核估计的强相合性

设（Ｘ１，Ｙ１），…，（Ｘｎ，Ｙｎ）是从取值于Ｒ＾ｐ×Ｒ＾ｑ的随机向量（Ｘ，Ｙ）中抽取的随机样本，在给定Ｘ＝ｘ的条件下Ｙ具有条件密度ｆ（ｙ│ｘ）。在本文中，我们考虑ｆ（ｙ│ｘ）的通常的和递归形式的双重核估计ｆｎ（ｙ│ｘ）＝ｎ∑ｉ＝１Ｋ１（Ｘｉ－ｘ／ａｎ）Ｋ２（Ｙｉ－６／ｂｎ）／〔ｂｎ＾ｑｎ∑ｊ＝１Ｋ１（Ｘｊ－ｘ／ａｎ）〕ｆｎ（ｙ│ｘ）＝ｎ∑ｉ－１Ｋ１（Ｘｉ－ｘ／ａｉ）Ｋ２（Ｙｉ－ｙ／ｂｉ）／ｎ∑ｊ     

对《关于f~n(x)=x的讨论》的商榷

对《关于ｆｎ（ｘ）＝ｘ的讨论》的商榷郎永发（安徽省铜陵县教委督学２４４１００）本刊１９９６年第９期发表张伟年先生的《关于ｆｎ（ｘ）＝ｘ的讨论》一文，以下简称文［１］．其中，定理１（ｂ）原意是：“ｆ（ｘ）＝ａｘ＋ｂｘ＋ｃ（ａ，ｂ，ｃ∈Ｒ且ａｃ－ｂ≠０）...     

相依样本下密度函数及其导函数的相合随机窗宽核估计

设（Ｘｎ）是Ｒ＾１中的平稳，强混合序列，具有公共的密度ｆ（ｘ），则可定义ｆ（ｘ）及其导函数ｆ＾（ｒ）（ｘ）的核估计与最近邻估计ｆ＾（ｒ）ｎ（ｘ）＝（ｎｈ＾ｒ＋１ｎ（ｘ））＾－１ｎ∑ｉ＝１Ｋ＾（ｒ）（Ｘｉ－Ｘ／ｈｎ（ｘ）），ｆｎ（ｘ）＝（ｎａｎ（ｘ））＾－１ｎ∑ｉ＝１Ｋ（Ｘｉ－ｘ／ａｎ（ｘ））其中核函数Ｋ（Ｘ）为取定的概率密度函数，且具有ｒ（ｒ≥０）阶导数，窗宽ｈｎ（ｘ）＝ｈｎ（ｘ；Ｘ１，．．．，Ｘ     

广义Hausdorf矩中基函数的唯一性

本文通过定义左连续单调函数的广义反函数，讨论了形式为ａｎ＝∫ｂａΨ（ｘ）ｆｎ（ｘ）ｄｘ，ｎ＝１，２，３，…（１）的广义Ｈａｕｓｄｏｒｆ矩的基函数ｆ（ｘ）的唯一性问题。在Ψ（ｘ）为Ｌｅｂｅｓｇｕｅ正可积的条件下，我们证明了当ｆ（ｘ）为单调函数时，满足（１）式的基函数ｆ（ｘ）是唯一的     

数学问题解答

９９７年４月号问题解答（解答由问题提供人给出）１０６６已知ｆ（ｘ）＝ｘ－１ｘ＋１，记ｆｎ（ｘ）＝ｆ｛ｆ…［ｆ（ｘ）］｝ｎ个，求方程［ｆ１９（ｘ）＋ｆ９７（ｘ）］ｆ７１（ｘ）＝３的根．（１９９７年７月１日是香港回归祖国日，故创造此题纪念）解∵ｆ２（ｘ）...     

部分线性模型中估计的渐近正态性

考虑回归模型其中是未知函数，（ｘ＿ｉ，ｔ＿ｉ，ｕ＿ｉ）是固定非随机设计点列，β是待估参数，ｅ＿ｉ是随机误差。基于ｇ（·）及ｆ（·）的一类非参数估计（包括常见的核估计和近邻估计），我们构造了β的加权最小二乘估计，并证得了最小二乘估计和加权最小二乘估计的渐近正态性。     

回归函数核估计的随机加权法

本文利用随机加权法的思想,构造了回归函数g(x)的核估计     

APPROXIMATION RATES OF ERROR DISTRIBUTION OF DOUBLE KERNEL ESTIMATES OF CONDITIONAL DENSITY

§ 1　IntroductionLet(X,Y) be a random vector taking values Rp×Rqand assume that with given X=x,f(y|x) is the conditional density of Y,the Borel-measurable function on(x,y) ,X has amarginal distribution function F(x) and a marginal density function f(x) .Let(X1 ,Y1 ) ,...,(Xn,Yn) be i.i.d.sample taking values in(X,Y) .A class of double kernel esti-mates of f(y|x) proposed by Zhao Linchang and Liu Zhijun[1 ] has the formfn(y|x) = ni=1K1Xi -xan K2Yi -ybn bqn nj=1K1Xj-xan ,(1 .1 )where…     

条件密度双重核估计误差分布的逼近速度

In this paper, the normal approximation rate and the random weighting approximation rate of error distribution of the kernel estimator of conditional density function f(y!|x) are studied. The results may be used to construct the confidence interval of f(y|x).     

密度核估计的随机加权法

利用随机加权法的思想,找出概率密度函数估计的随机加权统计量,在适当的条件下证明随机加权分布逼近核估计误差分布的精度为     

L1-Norm Estimation and Random Weighting Method in a Semiparametric Model

In this paper, the L_1-norm estimators and the random weighted statistic for a semiparametric regression model are constructed, the strong convergence rates of estimators are obtain under certain conditions, the strong efficiency of the random weighting method is shown. A simulation study is conducted to compare the L_1-norm estimator with the least square estimator in term of approximate accuracy, and simulation results are given for comparison between the random weighting method and normal approximation method.     

Integrated squared error of kernel-type estimator of distribution function

Let X ₁ ,...,X _n be a random sample drawn from distribution function F(x) with density function f(x) and suppose we want to estimate X(x). It is already shown that kernel estimator of F(x) is better than usual empirical distribution function in the sense of mean integrated squared error. In this paper we derive integrated squared error of kernel estimator and compare the error with that of the empirical distribution function. It is shown that the superiority of kernel estimators is not necessarily true in the sense of integrated squared error.     

NA误差下半参数回归模型估计的相合性

对半参数回归模型:Y(xin,tin)=tinb+g(xin)+e(xin),1≤j≤m,1≤i≤n,本文在NA相依样本下讨论了g的加权估计及b的最小二乘估计的强相合性与r(>2)阶平均相合性,使得文献犤2犦在独立样本下的相应结果得到推广     

Maximum likelihood estimation of covariance matrices under simple tree ordering

The closed-form maximum likelihood estimators for the completely balanced multivariate one-way random effect model are obtained by Anderson et al. (Ann. Statist. 14 (1986) 405). It remains open whether there exist the closed-form maximum likelihood estimators for the more general completely balanced multivariate multi-way random effects models. In this paper, a new parameterization technique for covariance matrices is used to grasp the inside structure of likelihood function so that the maximum likelihood equations can be dramatically simplified. As such we obtain the closed-form maximum likelihood estimators of covariance matrices for Wishart density functions over the simple tree ordering set, which can then be applied to get the maximum likelihood estimators for the completely balanced multivariate multi-way random effects models without interactions.     

单向分类随机效应模型中方差分量的渐近最优经验Bayes估计

本文在加权平方损失下导出了单向分类随机效应模型中方差分量的Bayes估计, 利用多元密度及其偏导数的核估计方法构造了方差分量的经验Bayes(EB)估计,证明了 EB估计的渐近最优性.文末还给出了一个例子说明了符合定理条件的先验分布是存在 的.     

Approximation to the distribution of the least squares estimators in two dimensional cosine models by randomly weighted bootstrap

Recently, Kundu and Gupta （Metrika, 48：83 C 97, 1998） established the asymptotic normality of the least squares estimators in the two dimensional cosine model. In this paper, we give the approximation to the general least squares estimators by using random weights which is called the Bayesian bootstrap or the random weighting method by Rubin （Annals of Statistics, 9：130 C 134, 1981） and Zheng （Acta Math. Appl. Sinica （in Chinese）, 10（2）： 247 C 253, 1987）. A simulation study shows that this approximation works very well.