部分线性回归模型中的自适应估计

考虑半参数回归模型y_i=x_iβ+g(t_i)+e_i,1≤i≤n;其中g(·)是未知函数,β是待估参数,e_i是随机误差. 本文首先在(x_i,t_i)是i.i.d设计点列下,当g(·)的估计取一类核估计序列时,构造了β的自适应估计β_n,同时给出了β_n及g_n^*(g的最终估计)的渐近最优强弱收敛速度;然后在(x_i,t_i)是固定非随机设计点列下,当g(·)的估计取一般非参数估计(包括常见核估计和近邻估计),讨论了β的最小二乘估计的渐近正态性.     

Gauss-Markov条件下最小二乘估计的强相合性

设Y_i=x＇_iβ+e_i,1≤i≤n为线性模型,β_n=(β_n1,…,β_np)＇为β=(β₁,…,β_p)＇的最小二乘估计,以u_n记(sum from i=1 to n(x_ix＇_i))的(1,1)元,v_n=u_n^-1.证明了在Ee_i=O且{e_i}满足Gauss-Markov条件时,v_i→∞及sum from i=2 to ∞(v_i^-2(v_i-v_i-1)log~2i<∞)为β_n1强相合的充分条件,且对任何ε_n→0,v_i→∞及sum from i=2 to ∞(ε_iv_i^-2(v_i-v_i-1)log²i<∞)已不再充分.提出了β_n1强相合的一个充要条件,它把β_n1强相合归结为正交随机变量级数的收敛问题.     

一类非线性高阶波动方程的初边值问题

该文研究一类非线性高阶波动方程u_tt－a_1^Uxx+a_₂u_x4+a₃u_x4_tt=φ(u_{x ⁾}x+f(u,u_x,u_xxu_xxx,u_x4)的初边值问题.证明整体古典解的存在唯一性并给出古典解爆破的充分条件.     

两类广义Petersen 图的Euler亏格

广义 Petersen 图 P(n, m) 是这样的一个图:它的顶点集是{u_i, v_i | i=0,1, ^… , n-1}, 边集是 {u_iu_i+1, v_iv_i+m, u_iv_i | i=0,1, ^…, n-1}, 这里 m, n 是正整数、加法是在模n 下且 m<|n/2| . 这篇文章证明了P(2m+1, m)(m≥ 2) 的 Euler 亏格是1, 并且 P(2m+2, m)(m≥ 5) 的 Euler 亏格是2.     

半参数回归模型小波估计的强逼近 *

考虑半参数回归模型y_i=x^T_iβ +g( t_i ) +e_i,i=1 ,2 ,… ,n ,其中 β∈R^d 为未知回归参数 ,g(·)为 [0 ,1 ]上的未知Borel函数 ,{x^T_i}为R^d 上的随机设计 ,{t_i}为常数序列 ,{e_i}为i.i.d .随机误差 ,Eei=0 .在适当的条件下 ,证明了 β和g(·)的小波估计 ^{^}β和^{^}g(·)的强相合性 ,并且得到了^{^}β和^{^}g(·)的强相合速度 .     

类对称函数的Schur凸性和不等式

对x = (x₁, x₂,···, x_n) ∈ (0,1)ⁿ 和 r ∈ {1, 2,···, n} 定义对称函数 F_n(x, r) = F_n(x₁, x₂,···, x_n; r) =∏_{1≤i1∑j=1^r(1+xi3/1- xi3)^1/r, 其中i1, i2, ···, ir 是整数. 该文证明了Fn(x, r) 是(0,1)ⁿ 上的Schur凸、Schur乘性凸和Schur调和凸函数. 作为应用,利用控制理论建立了若干不等式.}     

具有高阶非线性项的广义KdV方程的

研究具有高阶非线性项的广义KdV方程 u_t + a (1 + buⁿ)uⁿ u_x + u_xxx = 0, 这里n ≥1, a, b是实数且a ≠ 0. 用动力系统的定性理论和分支方法, 讨论了该方程的孤立波解的解析表达式和孤立波的分支, 并给出了孤立波的分支图, 解决了孤立波的存在性及其个数等问题.     

随机中止试验的线性模型中参数估计的几个性质

When sample size is a sequence of r.v., the parametric estimates in linear models are interesting both theoretically and practically. For linear models Y_i=x_iβ+e_i,i=1,2,…where {e_i} are p-dimensional i .i .d.r.v′.s with Ee₁=0. Var e₁=σ²,0<σ²<∞. we consider estimate σ²(v_n) of error variance σ² and least squares estimate β(v_n) of regression coefficient β, here {v_n} is a sequence of r.v. with positve integers larger than P. In this paper some properties of estimates are discussed . Those are unbiasedness, consistency and normality . For σ²(v_n), we also obtain some orders for convergence to normal distribution.     

正交非均衡Procrustes问题的持续投影算法

研究正交约束下的Procrustes问题：给定矩阵A∈R^n×n, B∈ ^n×k, n>k, 找一个Q∈R^n×k}, 使得在列单位正交约束Q^TQ=I_k下, 残量‖AQ-B‖_F达到最小. 给出了求解该问题的持续投影算法, 该算法的每一次扫描由求解k个二次约束下的最小二乘问题以及一个扩充后的均衡Procrustes问题组成; 也给出了详细的收敛性分析. 文中的数值例子表明新的迭代算法优于已有的其他方法.     

带强迫项的二阶非线性椭圆方程的区域振动准则

对二阶非线性椭圆型方程∑ _i,j=1ⁿ D_i[A_ij(x)D_jy]+∑_i=1ⁿ b_i(x)D_iy+q(x)f(y)=e(x)建立了若干新的振动准则, 所得结果仅依赖于方程在外区域Ω С Rⁿ的一个子区域序列的信息而有别于已知的大多数结论.     

WAVELET ESTIMATION IN NONPARAMETRIC MODEL UNDER MARTINGALE DIFFERENCE ERRORS

§1　IntroductionConsider the following heteroscedastic regression model:Yi =g(xi) +σiei,　1≤i≤n,(1.1)whereσ2i=f(ui) ,(xi,ui) are nonrandom design points,0≤x0 ≤x1 ≤...≤xn=1and0≤u0≤u1 ≤...≤un=1,Yi are the response variables,ei are random errors,and f(·) andg(·) are unknown functions defined on closed interval[0 ,1] .It is well known thatregression model has many applications in practical problems,sothe model (1.1) and its special cases have been studied extensively. For instance,…     

Simultaneous determination of the source terms in a linear hyperbolic problem from the final overdetermination: weak solution approach

The problem of determining the pair w:={F(x, t);f(t)} of sourceterms in the hyperbolic equation u_tt = (k(x)u_x)_x + F(x, t) andin the Neumann boundary condition k(0)u_x(0, t) = f(t) from themeasured data µ(x):=u(x, T) and/or (x):=u_t(x, t) at thefinal time t = T is formulated. It is proved that both componentsof the Fréchet gradient of the cost functionals J₁(w)= ||u(x, t;w) – µ(x)||₀² and J₂(w) = ||u_t(x, T;w)– (x)||₀² can be found via the solutions of correspondingadjoint hyperbolic problems. Lipschitz continuity of the gradientis derived. Unicity of the solution and ill-conditionednessof the inverse problem are analysed. The obtained results permitone to construct a monotone iteration process, as well as toprove the existence of a quasi-solution.     

Necessary and Sufficient Conditions for Exponential Stability and Ultimate Boundedness of Systems Governed by Stochastic Partial Differential Equations

Consider the following infinite dimensional stochastic evolutionequation over some Hilbert space H with norm |·|: It is proved that under certain mild assumptions, the strongsolution X_t(x₀)VHV*, t 0, is mean square exponentially stableif and only if there exists a Lyapunov functional (·,·):HxR₊R¹ which satisfies the following conditions: (i)c₁|x|²–k₁e^–µ₁t(x,t)c₂|x|²+k₂+k₂e^–µ₂t; (ii) L(x,t)–c₃(x,t)+k₃e^–µ₃t, xV, t0; where L is the infinitesimal generator of the Markov processX_t and c_i, k_i, µ_i, i = 1, 2, 3, are positive constants.As a by-product, the characterization of exponential ultimateboundedness of the strong solution is established as the nulldecay rates (that is, µ_i = 0) are considered.     

Adaptive numerical schemes for a parabolic problem with blow-up

In this paper we present adaptive procedures for the numericalstudy of positive solutions of the following problem: u_t = u_xx (x, t) (0, 1) x [0, T), u_x(0, t) = 0 t [0, T), u_x(1, t) = u^p(1, t) t [0, T), u(x, 0) = u₀(x) x (0, 1), with p > 1. We describe two methods. The first one refinesthe mesh in the region where the solution becomes bigger ina precise way that allows us to recover the blow-up rate andthe blow-up set of the continuous problem. The second one combinesthe ideas used in the first one with moving mesh methods andmoves the last points when necessary. This scheme also recoversthe blow-up rate and set. Finally, we present numerical experimentsto illustrate the behaviour of both methods.     

Asymptotic simplification for a reaction-diffusion problem with a nonlinear boundary condition

We study non-negative solutions of the porous medium equationwith a source and a nonlinear flux boundary condition, u_t =(u^m)_xx + u^p in (0, ), x (0, T); – (u^m)_x (0, t) = u^q (0,t) for t (0, T); u (x, 0) = u₀ (x) in (0, ), where m > 1,p, q > 0 are parameters. For every fixed m we prove thatthere are two critical curves in the (p, q-plane: (i) the criticalexistence curve, separating the region where every solutionis global from the region where there exist blowing-up solutions,and (ii) the Fujita curve, separating a region of parametersin which all solutions blow up from a region where both globalin time solutions and blowing-up solutions exist. In the caseof blow up we find the blow-up rates, the blow-up sets and theblow-up profiles, showing that there is a phenomenon of asymptoticsimplification. If 2q < p + m the asymptotics are governedby the source term. On the other hand, if 2q > p + m theevolution close to blow up is ruled by the boundary flux. If2q = p + m both terms are of the same order.     

A Necessary and Sufficient Condition for a Linear Differential System to be Strongly Monotone

In order to present the results of this note, we begin withsome definitions. Consider a differential system [formula] where IR is an open interval, and f(t, x), (t, x)IxRⁿ, is acontinuous vector function with continuous first derivativesf_r/x_s, r, s=1, 2, ..., n. Let D_xf(t, x), (t, x)IxRⁿ, denote the Jacobi matrix of f(t,x), with respect to the variables x₁, ..., x_n. Let x(t, t₀,x₀), tI(t₀, x₀) denote the maximal solution of the system (1)through the point (t₀, x₀)IxRⁿ. For two vectors x, yRⁿ, we use the notations x>y and x>>yaccording to the following definitions: [formula] An nxn matrix A=(a_rs) is called reducible if n2 and there existsa partition [formula] (p1, q1, p+q=n) such that [formula] The matrix A is called irreducible if n=1, or if n2 and A isnot reducible. The system (1) is called strongly monotone if for any t₀I, x₁,x₂Rⁿ [formula] holds for all t>t₀ as long as both solutions x(t, t₀, x_i),i=1, 2, are defined. The system is called cooperative if forall (t, x)IxRⁿ the off-diagonal elements of the nxn matrix D_xf(t,x) are nonnegative. 1991 Mathematics Subject Classification34A30, 34C99.     

A new methodology for the stability analysis of pairwise triangularizable and related switching systems

We consider the asymptotic stability of the time-varying dynamicsystem : = A(t)x, A(t) R^{n x n}, A(t) A= {A₁, ..., A_m}, where A_i is Hurwitz and where a set of non-singularmatrices T_{i j} exist such that any pair of matrices {T_{i j} A_iT_{i j}^–1, T_{i j} A_j T_{i j}^–1}, i, j {1, ..., m}, areupper triangular. Switching systems of this form are referredto as pairwise triangularizable switching systems. It can beestablished that (a) pairwise triangularizability is not sufficientto guarantee the existence of a common quadratic Lyapunov functionfor the linear time-invariant dynamic systems A_i : = A_i x; (b) additional conditions can be specified which guaranteeasymptotic stability of the switching system . In this paperwe also show that pairwise triangularizability is not even sufficientto guarantee asymptotic stability of the switching system .We also show that the method of proof of stability in (b), whichdoes not assume the existence of a common quadratic Lyapunovfunction, can be used to prove the asymptotic stability of moregeneral switching systems (systems that are not pairwise triangularizable).Finally, we show that our results can be used as the basis forthe design of practical control systems; namely, for the designof an automobile speed switched controller with guaranteed stabilityproperties.     

部分线性模型中估计的强相合性

考虑回归模型：ｙｉ＝ｘｉβ＋ｇ（ｔｉ）＋σｉｅｉ，１ｉｎ，其中σ２ｉ＝ｆ（ｕｉ），（ｘｉ，ｔｉ，ｕｉ）是固定非随机设计点列，ｆ（·）和ｇ（·）是未知函数，β是待估参数，ｅｉ是随机误差．对文［１］给出的基于ｇ（·）及ｆ（·）的一类非参数估计的β的最小二乘估计＾βｎ和加权最小二乘估计βｎ，我们在适当条件下证明了它们的强相合性．