含有线性约束及非负回归系数的回归模型

本文考虑如下的回归模型 Y=Xβ 8 β≥0,Hβ=C ε～N_n(0,σ~2l_n) 其中Y:n×1,X:n×p,ε:n×1,H:q×p(q相似文献   

变换数据对线性模型拟合值的影响

一、引言考虑线性回归模型其中Y是n维观测向量,X是n×p阶段设计矩阵,且其秩为R(X)=p,β为p维未知参数向量,e为n维随机误差向量、对于模型(1.1),β的最小二乘估计(The Least Squares Estimate,以下简记为LS估计)     

种公牛育种值预测的BLUP方法及应用一例

设一般的混合线性模型是 Y=Xβ Zu e (1)其中Y是n维观测值向量,X、Z分别是已知的n×p和n×q矩阵,β是一个p维(未知)固定参数向量,u是一个q维随机参数向量,e是n维随机误差向量,且有 E=0 Var (2)在实际问题中,常有R=σ_6~2I,G也常常是对角阵。七十年代初,C.R.Henderson提出用混合模型来预测种公牛育种值,他把环境效应(为牧场一年一季效应)、固定遗传效应(为公牛组效应)看作固定参数β,而把公牛效应及其与环境、遗传的交互效应看作随机参数向量u(交互效应一般不显著,故实际上常略去),目的是要     

相依误差下回归系数 LS 估计的收敛速度

其中 x(ij)(j=1,…,p,i=1,2,…)是已知常数,常称之为模型(1)的设计常数或设计点列,β_1,…,β_p,为未知的回归系数,y_i,e_i 分别为第 i 次量测时的量测值和量测随机误差。以下,我们记设计矩阵(x_(ij))(?)≤(?)≤n,(?)≤j p 为 X_n,Y_n=(y_1,…,y_n)′,β=(β_1,…,β_p)′.并假定对某N,X′_N X_N 非退化,那么当 n≥N 时,X′_n X_n 亦非退化,且回归系数β的基于前 n 次量测值 Y_(?)及设计矩阵 X_(?)的最小二乘估计(通常简记为 LS 估计) b_(?)=(b_(?)1, …,b_((?)p)'为     

偶特征有限域上对称矩阵结合方案

设S(n,q)是偶特征有限域F_q上n×n对称矩阵所成的集合.令R_i={(X,Y)|X,Y∈S(n,q),rank(Y-X)=2i-1,2i},0≤i≤[(n+1)/2]采用矩阵方法,证明了Sym(n,q)={s(n,q),{R_i}_(0≤i≤)[(n+1)/2]}是[(n+1)/2]个结合类的P—多项式对称结合方案,而Sym(n,q)的结合关系的图Γ~((1))是正则的,并且它同构于交错矩阵结合方案.此外,又给出Sym(n,q)的自同构形式.     

线性模型中一些无偏损失估计的不可容许性和可容许性

设 Y～N_n(Xβ,σ~2V),此处 X 和 V 分别是已知的 n×P 和 n×n 矩阵,rank(X)=p≤n,V>0(即 V 是正定的),β∈R~P 是参数向量,σ>0已知或未知.记(?)=(X′V~(-1)X)~(-1)X′V~(-1)Y,S~2=Y′[V~(-1)-V~(-1)X(X′V~(-1)X)~(-1)X′V~(-1)]Y.对于σ已知情形,本文证明了,在均方误差损失[α-((?)-β)′((?)-β)]~2之下,损失((?)-β)′((?)-β)的无偏估计σ~2tr(X′V~(-1)X)~(-1)在 P≤4时是可容许的,而当P≥5时不可容许.对于σ也是未知参数且 P相似文献   

关于可估函数LS估计相和条件的一个问题

设有线性回归M=xi^'β0 ei,1≤i≤n．n≥1,本文在Sn=∑i=1^n xixi^'的逆可以不存在的情况下，对任一特定的可估函数c'β0，给出了其LS估计为相合的充要条件。     

含发散维数自变量的单指标模型中方向向量的稳健估计

在回归分析中,常常引入大量的自变量来减少模型拟合的误差.本文考虑如下非常一般的单指标模型:在给定自变量X的线性组合β0τX的条件下,响应变量Y和维数发散的自变量X相互独立,其中β0是pn维向量.本文在这样的单指标模型假设下讨论当pn→∞时单指标模型中方向向量的稳健估计问题.我们发现,当pn=o(√n)时,最小二乘估计βn0能够相合地估计β0的方向.为了剔除不相关的自变量,从而提高回归模型的可解释能力,我们提出基于1-正则化的算法,通过加二次限制得到稀疏的最小二乘估计.1-正则化的解βn不仅可以相合地估计β0的方向,而且可以产生稀疏的估计.因此,我们可以选择一些重要的自变量,在保持预测准确度的同时使模型解释变得容易.模拟分析和汽车价格数据应用分析表明,我们所提出的估计方法在有限样本场合具有良好的表现.     

线性模型回归系数最小二乘估计的平均相合性

设线性回归模型其中β为未知的回归系数向量。用_n表示基于n个观察点y1,…,y_n的β的LS估计,假使对某个r>0有则称_N是β的r-平均相合估计。 本文作者建立了当1≤r≤2和0相似文献   

PC准则下回归系数的一类线性估计的优良性

设线性回归模型为,此处n≥p,X的秩为R(X)=s,00为常数,∑_0为正定阵。本文证明了:在适当条件下(?)于PC准则下优于(?)并将这一结果应用于回归系数的岭估计、广义岭估计、压缩估计和Bayes估计。     

A rank test based on the moments of order statistics of the modified Makeham distribution

Single moments of order statistics from the modified Makeham distribution (MMD) are derived, an identity about the single moments of order statistics is given, and the specific expected value and variance of the single moments of order statistics from the MMD are calculated. In this study, the order statistic from the MMD was applied to the rank sum test in a two-sample problem. The exact critical values of the designated statistics were evaluated. Simulations were used to investigate the power of these statistics for the two-sided alternative with several population distributions. The powers of the statistics were compared with the Wilcoxon rank sum statistic, the Lepage statistic, the modified Baumgartner statistic, the Savage test and the normal score test. The Edgeworth expansion was used to evaluate the upper tail probability for the preferred statistic, given finite sample sizes.     

A Quantile Goodness-of-Fit Test for Cauchy Distribution, Based on Extreme Order Statistics

A test statistic for testing goodness-of-fit of the Cauchy distribution is presented. It is a quadratic form of the first and of the last order statistic and its matrix is the inverse of the asymptotic covariance matrix of the quantile difference statistic. The distribution of the presented test statistic does not depend on the parameter of the sampled Cauchy distribution. The paper contains critical constants for this test statistic, obtained from 50,000 simulations for each sample size considered. Simulations show that the presented test statistic is for testing goodness-of-fit of the Cauchy distributions more powerful than the Anderson-Darling, Kolmogorov-Smirnov or the von Mises test statistic.     

Improved omnibus test statistic for normality

We propose a new omnibus test statistic for normality based on the Jarque–Bera test statistic. We give the exact first four moments of the null distribution for the statistic using a computer algebra system. Our proposed statistic is an improvement of Jarque–Bera test statistic. Then the cumulants of the standardized statistic satisfy the Cornish–Fisher assumption. We give a normalizing transformation of the statistic based on the Wilson–Hilferty transformation.     

A VIEW OF THE CHI-SQUARE STATISTIC

Statistical analysis of contingency tables is essentially a discrete multivariate problem. To test independence in a two-way contingency table, one can use different principles such as general class of distance or Mahalanobis distance to derive test statistic. Most of them result in the chi-square statistic, which is a simple test statistic. The disadvantage of the chi-square test is discussed from a multivariate point of view. A new test statistic is proposed for testing independence. This statistic is more sensitive to dependence than the chi-square statistic.     

Wilcoxon signed rank test using ranked-set sample

Ranked-set sampling is useful when measurements are destructive or costly to obtain but ranking of the observations is relatively easy. The Wilcoxon signed rank test statistic based on the ranked-set sample is considered. We compared the asymptotic relative efficiencies of the RSS Wilcoxon signed rank test statistic with respect to the SRS Wilcoxon signed rank test statistic and the RSS sign test statistic. Throughout the ARE’s, the proposed test statistic is superior to the SRS Wilcoxon signed rank test statistic and the RSS sign test statistic.     

A semiparametric Wald statistic for testing logistic regression models based on case-control data

We propose a semiparametric Wald statistic to test the validity of logistic regression models based on case-control data. The test statistic is constructed using a semiparametric ROC curve estimator and a nonparametric ROC curve estimator. The statistic has an asymptotic chisquared distribution and is an alternative to the Kolmogorov-Smirnov-type statistic proposed by Qin and Zhang in 1997, the chi-squared-type statistic proposed by Zhang in 1999 and the information matrix test statistic proposed by Zhang in 2001. The statistic is easy to compute in the sense that it requires none of the following methods: using a bootstrap method to find its critical values, partitioning the sample data or inverting a high-dimensional matrix. We present some results on simulation and on analysis of two real examples. Moreover, we discuss how to extend our statistic to a family of statistics and how to construct its Kolmogorov-Smirnov counterpart. This work was supported by the 11.5 Natural Scientific Plan (Grant No. 2006BAD09A04) and Nanjing University Start Fund (Grant No. 020822410110)     

至多一个分布变点的非参数检验及其渐近性质

本文研究了连续分布函数变点的假设检验问题,通过秩统计量和次序统计量方法,得到了相应的检验统计量及其渐近性质.     

The distribution of the maximum likelihood-ratio statistic used for testing a normal sample for three outliers

We study the exact distribution of the likelihood-ratio statistic used for testing a normal sample for three upper (lower) outliers. We obtain recursive correlations for the integral distribution function of this statistic. We apply the obtained correlations for calculating critical values of the likelihood-ratio statistic which appear to be close to critical values of this statistic simulated by the Monte Carlo method. We give an example of the joint use of the likelihood-ratio statistic for testing a sample for more than one outlier.     

基于变点检验的卵巢癌早期筛查法刘媚1张晓冉2,3房祥忠3简

In the paper a change-point model of ca125 longitudinal data for single subject was established. The formulae of the maximum likelihood estimate for model parameters were presented. A likelihood ratio test statistic for early screening of ovarian cancer was proposed. The critical values of likelihood ratio test statistic was calculated by Monte Carlo method in some cases. At last the power and robustness of the likelihood ratio test were discussed.     

On tests for the mean direction of the Langevin distribution

The asymptotic expansions of the distribution of a sum of independent random vectors with Langevin distribution are given. The power functions of the likelihood ratio criterion, Watson statistic, Rao statistic and the modified Wald statistic for testing the hypothesis of the mean direction are obtained asymptotically and a numerical comparison is made.