线性混合效应模型中方差分量的估计

本文首先研究了含三个方差分量的线性混合随机效应模型改进的ANOVA估计, 此估计在均方损失下一致优于ANOVA估计. 由于这些方差估计取负值的概率大于零, 对得到的估计在某非负点采用截尾的方法得到非负估计是一种常用的方法. 对文章中提出的估计, 研究了此估计在某非负点截尾之后得到的估计在均方损失意义下优于截尾之前的估计的充分条件, 同时给出ANOVA估计在截尾之后优于它本身的充分条件, 而且将得到的结论推广到更一般的线性混合随机效应模型.     

On uniform positivity of a symmetrizer

We exhibit ah effective method of constructing a positive definite symmetrizer of a linear operator that is spectrally equivalent to a self-adjoint operator bundle whose operator coefficients satisfy certain conditions. It is proved that these conditions are sufficient for positive definiteness of the symmetrizer.Translated fromMatematicheskie Melody i Fiziko-Mekhanicheskie Polya, Issue 34, 1991, pp. 12–15.     

On Jacobi’s condition for the simplest problem of calculus of variations with mixed boundary conditions

The purpose of this paper is an extension of Jacobi’s criteria for positive definiteness of second variation of the simplest problems of calculus of variations subject to mixed boundary conditions. Both non constrained and isoperimetric problems are discussed. The main result is that if we stipulate conditions (21) and (22) then Jacobi’s condition remains valid also for the mixed boundary conditions.     

A Generalization of Schep’s Theorem on the Positive Definiteness of a Piecewise Linear Function

Mathematical Notes - Schep proved that, for a piecewise linear function with nodes at integer points, positive definiteness on ? is equivalent to positive definiteness on ?. In this...     

A restarting approach for the symmetric rank one update for unconstrained optimization

Two basic disadvantages of the symmetric rank one (SR1) update are that the SR1 update may not preserve positive definiteness when starting with a positive definite approximation and the SR1 update can be undefined. A simple remedy to these problems is to restart the update with the initial approximation, mostly the identity matrix, whenever these difficulties arise. However, numerical experience shows that restart with the identity matrix is not a good choice. Instead of using the identity matrix we used a positive multiple of the identity matrix. The used positive scaling factor is the optimal solution of the measure defined by the problem—maximize the determinant of the update subject to a bound of one on the largest eigenvalue. This measure is motivated by considering the volume of the symmetric difference of the two ellipsoids, which arise from the current and updated quadratic models in quasi-Newton methods. A replacement in the form of a positive multiple of the identity matrix is provided for the SR1 update when it is not positive definite or undefined. Our experiments indicate that with such simple initial scaling the possibility of an undefined update or the loss of positive definiteness for the SR1 method is avoided on all iterations.     

A Modified BFGS Formula Maintaining Positive Definiteness with Armijo-Goldstein Steplengths

1.IntroductionAlinesearchmethodforminimizingarealfunctionfgeneratesasequencex1,x2,.-.ofpointsbyapplyingtheiterationxk+i=xk+akpk,k=1,2,....(1)Inaquasi-NewtonmethodthesearchdirectionpkischosensothatBkpk=-gk,whereBkis(usually)apositivedefiIiltematrirandgkdenotesVf(xk)-FortheBFGSupdate,(see[21,forexample),thematricesBkaredefinedbytheformulawheresk=xk+1-xkandyk=gk+1-gk'ItiswellknownthatifB1ispositivedefiniteands[Y*>O(3)thenallmatricesBk+1,k=1,2,...generatedby(2)arepositivedefiinte.Thuspkisadi…     

Retaining positive definiteness in thresholded matrices

Positive definite (p.d.) matrices arise naturally in many areas within mathematics and also feature extensively in scientific applications. In modern high-dimensional applications, a common approach to finding sparse positive definite matrices is to threshold their small off-diagonal elements. This thresholding, sometimes referred to as hard-thresholding, sets small elements to zero. Thresholding has the attractive property that the resulting matrices are sparse, and are thus easier to interpret and work with. In many applications, it is often required, and thus implicitly assumed, that thresholded matrices retain positive definiteness. In this paper we formally investigate the algebraic properties of p.d. matrices which are thresholded. We demonstrate that for positive definiteness to be preserved, the pattern of elements to be set to zero has to necessarily correspond to a graph which is a union of complete components. This result rigorously demonstrates that, except in special cases, positive definiteness can be easily lost. We then proceed to demonstrate that the class of diagonally dominant matrices is not maximal in terms of retaining positive definiteness when thresholded. Consequently, we derive characterizations of matrices which retain positive definiteness when thresholded with respect to important classes of graphs. In particular, we demonstrate that retaining positive definiteness upon thresholding is governed by complex algebraic conditions.     

A Piecewise Line-Search Technique for Maintaining the Positive Definitenes of the Matrices in the SQP Method

A technique for maintaining the positive definiteness of the matrices in the quasi-Newton version of the SQP algorithm is proposed. In our algorithm, matrices approximating the Hessian of the augmented Lagrangian are updated. The positive definiteness of these matrices in the space tangent to the constraint manifold is ensured by a so-called piecewise line-search technique, while their positive definiteness in a complementary subspace is obtained by setting the augmentation parameter. In our experiment, the combination of these two ideas leads to a new algorithm that turns out to be more robust and often improves the results obtained with other approaches.     

Using Spherical–Radial Quadrature to Fit Generalized Linear Mixed Effects Models

Although generalized linear mixed effects models have received much attention in the statistical literature, there is still no computationally efficient algorithm for computing maximum likelihood estimates for such models when there are a moderate number of random effects. Existing algorithms are either computationally intensive or they compute estimates from an approximate likelihood. Here we propose an algorithm—the spherical–radial algorithm—that is computationally efficient and computes maximum likelihood estimates. Although we concentrate on two-level, generalized linear mixed effects models, the same algorithm can be applied to many other models as well, including nonlinear mixed effects models and frailty models. The computational difficulty for estimation in these models is in integrating the joint distribution of the data and the random effects to obtain the marginal distribution of the data. Our algorithm uses a multidimensional quadrature rule developed in earlier literature to integrate the joint density. This article discusses how this rule may be combined with an optimization algorithm to efficiently compute maximum likelihood estimates. Because of stratification and other aspects of the quadrature rule, the resulting integral estimator has significantly less variance than can be obtained through simple Monte Carlo integration. Computational efficiency is achieved, in part, because relatively few evaluations of the joint density may be required in the numerical integration.     

Scaled memoryless symmetric rank one method for large-scale optimization

This paper concerns the memoryless quasi-Newton method, that is precisely the quasi-Newton method for which the approximation to the inverse of Hessian, at each step, is updated from the identity matrix. Hence its search direction can be computed without the storage of matrices. In this paper, a scaled memoryless symmetric rank one (SR1) method for solving large-scale unconstrained optimization problems is developed. The basic idea is to incorporate the SR1 update within the framework of the memoryless quasi-Newton method. However, it is well-known that the SR1 update may not preserve positive definiteness even when updated from a positive definite matrix. Therefore we propose the memoryless SR1 method, which is updated from a positive scaled of the identity, where the scaling factor is derived in such a way that positive definiteness of the updating matrices are preserved and at the same time improves the condition of the scaled memoryless SR1 update. Under very mild conditions it is shown that, for strictly convex objective functions, the method is globally convergent with a linear rate of convergence. Numerical results show that the optimally scaled memoryless SR1 method is very encouraging.     

Conditions of Equivalence of Spinor and Tensor Methods in the Problem of Positive Definiteness of the Gravitational Problem

It is demonstrated that J. Nester's tensor method for proving the theorem on the positive definiteness of gravitational energy in an asymptotically Minkowsky space is equivalent to E. Witten's spinor method, in which the Saint–Witten equation must be completed with a linear term that includes the gradient of a certain scalar potential. A new proof of the theorem on the positive definiteness of energy is proposed.     

线性混合效应模型的正交经验似然推断

基于经验似然方法和QR分解技术, 对线性混合效应模型提出了一个基于正交经验似然的估计方法. 在一些正则条件下, 证明了所提出的经验对数似然比函数渐近服从卡方分布, 进而给出了模型固定效应的置信区间估计. 所提出估计过程不受模型随机效应的影响, 进而保证了所给出的估计是比较有效的. 一些数值模拟和实例分析进一步表明了所提出的估计方法是行之有效的.     

Strictly positive definite functions on the complex Hilbert sphere

We study strictly positive definite functions on the complex Hilbert sphere. A link between strict positive definiteness and (harmonic) polynomial interpolation on finite‐dimensional spheres is investigated. Sufficient conditions for strict positive definiteness are presented. This revised version was published online in June 2006 with corrections to the Cover Date.     

Quantale矩阵的广义逆及其正定性

给出Quantale矩阵{1}-广义逆的一种刻划以及存在的条件,给出Quantale矩阵M-P广义逆的定义,讨论Quantale矩阵M-P广义逆的若干性质,得到Quantale矩阵M-P广义逆的具体形式.引入Quantale矩阵正定性的概念,研究交换幂等Quantale上矩阵正定的一些性质,得到交换幂等Quantale上矩阵正定的一些等价刻画.     

平衡线性混合模型方差分量几种估计的优良性

对于平衡线性混合模型,本文提出了一组易验证的条件,在此条件下,方差分量的谱分解估计、方 差分析估计和最小范数二次无偏估计都相等且为一致最小方差无偏估计．同时证明了在此条件下,似然 方程和限制似然方程都有显式解,还给出了许多满足这组条件的平衡线性混合模型的例子．     

Boundary contraction solution of the Neumann and mixed boundary value problems of the Laplace equation

The boundary contraction method is generalized in such a way that it is applicable to the Neumann and mixed boundary value problems over regions of irregular shape. Various variants of mixed boundary value problems can be solved numerically in a unified way with mesh points fewer than those required of ADI and SOR. The method is not iterative and therefore does not require the positive definiteness of eigenvalues which is the necessary condition of the stability of ADI and SOR. The method is also applicable to exterior problems. Thus the applicability of the contraction method to problems of practical importance is substantially improved.     

Defect indices and definiteness conditions for a class of discrete linear Hamiltonian systems

This paper is concerned with a class of discrete linear Hamiltonian systems in finite or infinite intervals. A definiteness condition and its equivalent statements are discussed and three sufficient conditions for the definiteness condition are given. A precise relationship between the defect index of the minimal subspace generated by the system and the number of linearly independent square summable solutions of the system is established. In particular, they are equal if and only if the definiteness condition is satisfied. Finally, two criteria for the limit point case and one criterion for the limit circle case are obtained.     

On the calculation of minimum variance estimators for unobservable dependent variables

The determination of minimum variance estimators in an unusual context is considered. The problem arises from an attempt to perform a regression with an unobservable dependent variable. The required minimum variance estimator is shown to satisfy a linear system of equations where the coefficient matrix has a simple structure. Uniqueness of the estimator is established by determining necessary and sufficient conditions on the data which guarantee positive definiteness of this coefficient matrix. Numerical aspects of the method of computation are also briefly explored.     

Bi-block positive semidefiniteness of bi-block symmetric tensors

The positive definiteness of elasticity tensors plays an important role in the elasticity theory.In this paper,we consider the bi-block symmetric tensors,which contain elasticity tensors as a subclass.First,we define the bi-block M-eigenvalue of a bi-block symmetric tensor,and show that a bi-block symmetric tensor is bi-block positive(semi)definite if and only if its smallest bi-block M-eigenvalue is(nonnegative)positive.Then,we discuss the distribution of bi-block M-eigenvalues,by which we get a sufficient condition for judging bi-block positive(semi)definiteness of the bi-block symmetric tensor involved.Particularly,we show that several classes of bi-block symmetric tensors are bi-block positive definite or bi-block positive semidefinite,including bi-block(strictly)diagonally dominant symmetric tensors and bi-block symmetric(B)B0-tensors.These give easily checkable sufficient conditions for judging bi-block positive(semi)definiteness of a bi-block symmetric tensor.As a byproduct,we also obtain two easily checkable sufficient conditions for the strong ellipticity of elasticity tensors.     

AN OSCILLATION THEOREM FOR A TWO-PARAMETER SYSTEM OF DIFFERENTIAL EQUATIONS WITH PERIODIC BOUNDARY CONDITIONS

Abstract

The oscillation theory for two simultaneous systems of second order linear differential equations in two parameters with periodic boundary conditions is well known when the coefficients of the differential equations are subjected to the usual definiteness condition. However, in practical applications the usual definiteness condition may fail to hold, and hence in this paper we consider the oscillation theory under another important definiteness condition.