线性模型中误差方差估计的相合性

本文研究在条件(1.2)(其中e₁,e₂,…,假定有不同的分布)之下,估计σ²(n)的大样本性质,得到了:
1.σ²(n)为σ²的弱相合估计的必要条件;
2.σ²(n)为σ²的强相合估计的必要条件和充分条件,二者差别不大(有可能,必要条件也是充分条件)。     

线性模型误差方差估计的分布的非一致性收敛速度

设是用线性模型的前n个观察值算出的,基于残差平方和的误差方差σ²的估计,并以G_n(x)记的分布函数,Φ(x)记标准正态分布函数。本文在试验误差序列e₁,e₂,…独立同分布的条件下,只需假定E(e₁⁶)<∞,证明了最佳的非一致性收敛速度其中C为一个与n和x都无关的常数。     

关于一个二元B样条基

连接矩形网剖分中每一矩形的两条对角线得到一个三角剖分,将它记为△_mn。当k≥3时,△_mn上不存在k—1阶光滑度的分片k次非平凡局部支集二元样条函数,所以本文给出了均匀剖分下的具有最小对称支集的二元二次一阶光滑度的B样条基。此外,作为一元样条的Marsden恒等式的推广,我们还得到了二元样条的相应形式以及其它一些恒等式。利用这些恒等式,我们在整个剖分△_mn的二次C¹样条函数空间上建立逼近误差估计以及相应的渐近公式。     

一类Ⅱ型剖分下的二元三次周期样条的超限插值和逼近(英)

本文讨论了Ⅱ一型三角剖分△^（2）_mn下的一类二元三次周期样条的超限插值和逼近,给出了它的表示以及存在唯一性,最后,估计了它的逼近阶．     

关于N参数d维Ornstein-Uhlenbeck过程样本轨道性质的注记

设{W(s),s∈R₊^N)是N参数Wiener过程,定义N参数Ornstein-Uhlenbeck过程如下:作X^N,d={(x¹(t),…,X^d(f)),t∈R₊^N),这里Sⁱ是1≤i≤d两两独立同分布的N参数OUP称之为N参数d维OUP.本文我们证明了X^N,d象集的d维Lebesgue测度为零。     

只有一个转变点的模型的假设检验和区间估计

对最多只含一个转变点t_o的模型X(i/n)=f(i/n)+e(i/n),其中f(t)=α+θI_(to,1)(t),0≤t≤1,e(1/n),…,e(n/n)独立同分布。本文讨论了关于转变点to,跃度θ以及e(t)的方差o²的假设检验和区间估计问题。     

二元三次样条空间S31,2(Δmn(2)) 的样条拟插值

本文首先利用由两组具有局部最小支集的样条所组成的基函数,构造非均匀2 型三角剖分上二元三次样条空间S₃^1,2(Δ_mn⁽²⁾)的若干样条拟插值算子. 这些变差缩减算子由样条函数B_ij¹支集上5 个网格点或中心和样条函数B_ij²支集上5 个网格点处函数值定义. 这些样条拟插值算子具有较好的逼近性,甚至算子V_mn（f） 能保持近最优的三次多项式性. 然后利用连续模,分析样条拟插值算子V_mn（f）一致逼近于充分光滑的实函数. 最后推导误差估计.     

关于k阶记录时间与k阶记录值的联合分布

设独立同分布随机变量序列{x_nj n≥1}的分布函数F(x)=p(x₁(k)(n);n≥1},{X^(k)(n);n≥1} 分别为{x_nj n≥1}的K阶记录时间序列和k阶记录值序列.本文我们用直接方法求出了{U^(k)(i),X^(k)(i);1≤i≤n}的联合分布，从而证明了k阶记录时间序列及k阶记录值序列的马氏性，并导出了它们之间的一     

非参数回归函数最近邻估计的强相合性

设(X,Y),(X₁,Y₁),…,(X_n,Y_n)为取值R^d×R的独立同分布随机向量,E|Y|<∞。设m_n(x)为m(x)=E(Y|X=x)的最近邻估计。本文在E|Y|^r<∞(对某个r>1)或E{exp(t|Y|^λ)}<∞(对某个λ>0及t>0)的条件下,建立了m_n(x)的强相合性。其它附加条件均与(X,Y)的分布无关。     

线性模型中误差方差估计的Berry-Esseen界限

在线性模型中,通常用残差平方和(除以适当的自由度)来估计模型中随机误差的方差,本文在误差独立但不必同分布的情况下,证明了在一定条件下,这种估计量经过规则化后,其分布以O(1/n^1/2)的理想速度收敛于标准正态分布。     

Bivariate inverse gaussian distribution

Summary A bivariate inverse Gaussian (IG) density function is constructed. Relations of the bivariate IG distribution to the normal and χ² distributions are established. The corresponding bivariate random walk (RW) density function is obtained. The properties and behaviour of bivariate IG distribution are studied for large parametric values. Moment estimates of the five parameters are given and applications are pointed out. A generalization to the multivariate IG distribution is proposed.     

Bivariate C1 Cubic Spline Spaces Over Even Stratified Triangulations

It is well-known that the basic properties of a bivariate spline space such as dimension and approximation order depend on the geometric structure of the partition. The dependence of geometric structure results in the fact that the dimension of a C ¹ cubic spline space over an arbitrary triangulation becomes a well-known open problem. In this paper, by employing a new group of smoothness conditions and conformality conditions, we determine the dimension of bivariate C ¹ cubic spline spaces over a so-called even stratified triangulation.     

Bivariate spline interpolation with optimal approximation order

Let Δ be a triangulation of some polygonal domain Ω ⊂ R² and let S_q^r(Δ) denote the space of all bivariate polynomial splines of smoothness r and degree q with respect to Δ. We develop the first Hermite-type interpolation scheme for S _q ^r (Δ), q ≥ 3r + 2, whose approximation error is bounded above by Kh ^q +1, where h is the maximal diameter of the triangles in Δ, and the constant K only depends on the smallest angle of the triangulation and is independent of near-degenerate edges and near-singular vertices. Moreover, the fundamental functions of our scheme are minimally supported and form a locally linearly independent basis for a superspline subspace of S _q ^r (Δ). This shows that the optimal approximation order can be achieved by using minimally supported splines. Our method of proof is completely different from the quasi-interpolation techniques for the study of the approximation power of bivariate splines developed in [7] and [18].     

Bivariate fibonacci like p-polynomials

In this article, we study the bivariate Fibonacci and Lucas p-polynomials (p ? 0 is integer) from which, specifying x, y and p, bivariate Fibonacci and Lucas polynomials, bivariate Pell and Pell-Lucas polynomials, Jacobsthal and Jacobsthal-Lucas polynomials, Fibonacci and Lucas p-polynomials, Fibonacci and Lucas p-numbers, Pell and Pell-Lucas p-numbers and Chebyshev polynomials of the first and second kind, are obtained. Afterwards, we obtain some properties of the bivariate Fibonacci and Lucas p-polynomials.     

Bivariate Density Estimation with an Application to Survival Analysis

Abstract

A procedure for estimating a bivariate density based on data that may be censored is described. After the data are transformed to the unit square, the bivariate density is estimated using linear splines and their tensor products. The combined procedure yields an estimate of the bivariate density on the original scale, which may provide insight about the dependence structure. The procedure can also be used to estimate densities that are known to be symmetric and to test for independence.     

Bivariate Lagrange interpolation at the Padua points: the ideal theory approach

The Padua points are a family of points on the square [−1, 1]² given by explicit formulas that admits unique Lagrange interpolation by bivariate polynomials. Interpolation polynomials and cubature formulas based on the Padua points are studied from an ideal theoretic point of view, which leads to the discovery of a compact formula for the interpolation polynomials. The L ^p convergence of the interpolation polynomials is also studied. S. De Marchi and M. Vianello were supported by the “ex-60%” funds of the University of Padua and by the INdAM GNCS (Italian National Group for Scientific Computing). Y. Xu was partially supported by NSF Grant DMS-0604056.     

Second-order Asymptotics on Distributions of Maxima of Bivariate Elliptical Arrays

Let {(ξni, ηni), 1 ≤ i ≤ n, n ≥ 1} be a triangular array of independent bivariate elliptical random vectors with the same distribution function as(S_1, ρ_n S_1 +(1-ρ_n~2S_2)~(1/2)), ρn∈(0, 1), where(S1, S2) is a bivariate spherical random vector. For the distribution function of radius (S_1~2+ S_2~2)~(1/2) belonging to the max-domain of attraction of the Weibull distribution, the limiting distribution of maximum of this triangular array is known as the convergence rate of ρn to 1 is given. In this paper,under the refinement of the rate of convergence of ρn to 1 and the second-order regular variation of the distributional tail of radius, precise second-order distributional expansions of the normalized maxima of bivariate elliptical triangular arrays are established.     

Bivariate polynomial and continued fraction interpolation over ortho-triples

By means of the barycentric coordinates expression of the interpolating polynomial over each ortho-triple, some properties are obtained. Moreover, the explicit coefficients in terms of B-net for one ortho-triple, and two ortho-triples are worked out, respectively. Thus the computation of multiple integrals can be converted into the sum of the coefficients in terms of the B-net over triangular domain much effectively and conveniently. Based on a new symmetrical algorithm of partial inverse differences, a novel continued fractions interpolation scheme is presented over arbitrary ortho-triples in R², which is a bivariate osculatory interpolation formula with one-order partial derivatives at all corner points in the ortho-triples. Furthermore, its characterization theorem is presented by three-term recurrence relations. The new scheme is advantageous over the polynomial one with some numerical examples.     

On Generating Discrete Orthogonal Bivariate Polynomials

In this paper we present an algorithm for recursively generating orthogonal bivariate polynomials on a discrete set S ². For this purpose we employ commuting pairs of real symmetric matrices H, K ^n×n to obtain, in a certain sense, a two dimensional Hermitian Lanczos method. The resulting algorithm relies on a recurrence having a slowly growing length. Practical implementation issues an applications are considered. The method can be generalized to compute orthogonal polynomials depending on an arbitrary number of variables.     

Bivariate spline method for numerical solution of steady state Navier–Stokes equations over polygons in stream function formulation

We use the bivariate spline finite elements to numerically solve the steady state Navier–Stokes equations. The bivariate spline finite element space we use in this article is the space of splines of smoothness r and degree 3r over triangulated quadrangulations. The stream function formulation for the steady state Navier–Stokes equations is employed. Galerkin's method is applied to the resulting nonlinear fourth‐order equation, and Newton's iterative method is then used to solve the resulting nonlinear system. We show the existence and uniqueness of the weak solution in H²(Ω) of the nonlinear fourth‐order problem and give an estimate of how fast the numerical solution converges to the weak solution. The Galerkin method with C¹ cubic splines is implemented in MATLAB. Our numerical experiments show that the method is effective and efficient. © 2000 John Wiley & Sons, Inc. Numer Methods Partial Differential Eq 16: 147–183, 2000