x~n+1在有理数域上的因式分解

从特殊情况研究多项式f(x)=xn+1在有理数域上的因式分解.对于正整数,设H(n)是n的大于1的奇约数的个数.本文用初等数论和近世代数的知识证明了:多项式xn+1在有理数域上可分解为H(n)+1个不可约因式的乘积,即D(f)=H(n)+1.     

自然数幂方和的一个巧妙猜想

文章将利用已知的几种特殊的自然数的幂方和,研究其算法特征,进而给出并证明了自然数幂方和的一个巧妙猜想,希望这一猜想能给自然数的幂方求和寻求更简单的方法提供一些新思路.     

Adaptive lasso for accelerated hazards models

The important feature of the accelerated hazards (AH) model is that it can capture the gradual effect of treatment. Because of the complexity in its estimation, few discussion has been made on the variable selection of the AH model. The Bayesian non-parametric prior, called the transformed Bernstein polynomial prior, is employed for simultaneously robust estimation and variable selection in sparse AH models. We first introduce a naive lasso-type accelerated hazards model, and later, in order to reduce estimation bias and improve variable selection accuracy, we further consider an adaptive lasso AH model as a direct extension of the naive lasso-type model. Through our simulation studies, we obtain that the adaptive lasso AH model performs better than the lasso-type model with respect to the variable selection and prediction accuracy. We also illustrate the performance of the proposed methods via a brain tumour study.     

Estimating the error distribution function in semiparametric additive regression models

We consider semiparametric additive regression models with a linear parametric part and a nonparametric part, both involving multivariate covariates. For the nonparametric part we assume two models. In the first, the regression function is unspecified and smooth; in the second, the regression function is additive with smooth components. Depending on the model, the regression curve is estimated by suitable least squares methods. The resulting residual-based empirical distribution function is shown to differ from the error-based empirical distribution function by an additive expression, up to a uniformly negligible remainder term. This result implies a functional central limit theorem for the residual-based empirical distribution function. It is used to test for normal errors.     

Restricted minimax robust designs for misspecified regression models

The authors propose and explore new regression designs. Within a particular parametric class, these designs are minimax robust against bias caused by model misspecification while attaining reasonable levels of efficiency as well. The introduction of this restricted class of designs is motivated by a desire to avoid the mathematical and numerical intractability found in the unrestricted minimax theory. Robustness is provided against a family of model departures sufficiently broad that the minimax design measures are necessarily absolutely continuous. Examples of implementation involve approximate polynomial and second order multiple regression.     

BCI-代数环与BCI-代数上的多项式

引入了BCI-代数上多项式的概念 ,并讨论了几种BCI -代数环     

一类非线性算子方程组的选代求解

在Ｂａｎａｃｈ空间上，利用锥理论与迭代方法研究了一类非线性算子方程组解的再在性与唯一性，推广了文［１］的主要结果，并给出了应用。     

回归分析在短跑教学中的应用

针对教学中学生100m跑的具体情况,采用逐步回归分析方法建立了回归方程;在此基础上,分别以各相关因素、100 m跑成绩作为因变量与自变量建立了多项式回归方程,并依据正态分布理论制定了各因素平衡发展的标准.上述方法在指导短跑教学中取得了较好的效果.     

关于中值定理“中间点”的渐近性质

文［１］给出了当区间长度趋于无穷大时Ｌａｇｒａｎｇｅ中值定理＂中间点＂的渐进性质。本文首先减弱了文［１］中主要定理的条件，从而改进了文［１］中的结果，然后给出积分中值定理当区间长度趋于无穷大时＂中间点＂的渐近性质．     

Inverse Problems of Matroid Intersection

In this paper we study the inverse problem of matroid intersection: Two matroids M ₁ = (E, ₁) and M ₂ = (E, ₂), their intersection B, and a weight function w on E are given. We try to modify weight w, optimally and with bounds, such that B becomes a maximum weight intersection under the modified weight. It is shown in this paper that the problem can be formulated as a combinatorial linear program and can be further transformed into a minimum cost circulation problem. Hence it can be solved by strongly polynomial time algorithms. We also give a necessary and sufficient condition for the feasibility of the problem. Finally we extend the discussion to the version of the problem with Multiple Intersections.