关于一个Pl型缺项插值问题

对基于节点组(1,4)的修改的(0;1;2)插值多项式Qn（f，x）的收敛性，本文改进了Akhlaghi的结果，也即对于［－1，1]上的r（≥2）次连续可微函数f（x），当n≥4/3（r+2）时，成立|f(x)-Qn(f,x)|=O(1)n~(2-r)ω(f~(r),1/n),x∈[-1,1]     

多项式的一种无重因式的分解方法

本文利用多项或重因式的判定法，论证一种更接近ｆ（ｘ）典型分解式的一种既无相同重数，又无重因式的分解方法。     

基于二次插值的GM(1,1)模型预测方法的改进

从理论上分析了GM(1,1)模型中背景值的计算,指出文[11]利用Newton-Cotes公式构造模型背景值的方法是不可靠的,提出用二次插值构造模型中的背景值,同时用最小二乘法对预测公式中的初值进行改进,并用改进的方法进行了短期预测.其理论分析及仿真结果均表明本文所提出的方法有效可靠,为提高预测精度提供了新的途径.     

Life Tables with Covariates: Dynamic Model for Nonlinear Analysis of Longitudinal Data

ABSTRACT

Life table models based on nonlinear dynamics of risk factors are developed using stochastic differential Equations for individual changes and on the resulting Fokker-Planck equation to describe population changes. Central to the model is a microsimulation strategy developed as a numerical procedure to represent a mortality effect when analytic approaches are not applicable. The model is applied to the Framingham Heart Study 46-year follow-up data. Life table functions and projections of risk factors are calculated to demonstrate the nonlinear effects on observable quantities over time. A set of statistically significant nonlinear contributions to covariate dynamics is identified. Their synergistic effect on dynamics and use of them as “new” risk factors are discussed. An important advantage of this approach is the ability to study the effects of health interventions at the individual level. This is illustrated in several examples.     

基于整数规划强对偶求解一类局域性资源受限项目调度问题

资源受限项目调度问题(简称RCPSP)是最具代表性的项目调度问题之一,调度过程可理解为,将受资源约束的平行工序调整为顺序工序。本文针对实际中广泛存在的资源局域、而非全局受限的情况,研究局域性RCPSP,并重点考虑一类问题：项目某环节的一系列平行工序,可用资源量只有一半,各资源可重复利用且具有相应多功能,但最多能承担2个工序,需将这些工序两两排列成对,实现项目工期最短。本文首先探索问题“局域性”特征,量化局域调度对项目工期的影响;基于此,构建只涵盖“局域调度工序”的0-1规划模型;再者,发展整数规划强对偶理论,结合Dangzig-Wolfe分解等方法,提出多项式时间的精确算法;最后通过算例测试,验证算法优势,例如,计算大规模算例的最优解,运用该算法比常规精确方法可快数万倍以上。     

Best quadratic predictions in finite populations

For a finite population and the resulting linear model Y=Xβ+e, the problem of the optimal invariant quadratic predictors including optimal invariant quadratic unbiased predictor and optimal invariant quadratic (potentially) biased predictor for the population quadratic quantities, f(H)=Y′HY , is of interest and has been previously considered in the literature for the case of HX=0. However, the special case does not contain all of situations at all. So, predicting f(H) in general situations may be of particular interest. In this paper, we make an effort to investigate how to offer a good predictor for f(H), not restricted yet to the mentioned case. Permutation matrix techniques play an important role in handling the process. The expected predictors are finally derived. In addition, we mention that the resulting predictors can be viewed as acceptable in all situations.     

Application domains for the Delta method

The Delta method uses truncated Lagrange expansions of statistics to obtain approximations to their distributions. In this paper, we consider statistics Y=g(μ+X), where X is any random vector. We obtain domains 𝒟 such that, when μ∈𝒟, we may apply the distribution derived from the Delta method. Namely, we will consider an application on the normal case to illustrate our approach.     

Local and Variable Bandwidths and Local Linear Regression

Two types of non-global bandwidth, which may be called local and variable, have been defined in attempts to improve the performance of kernel density estimators. In nonparametric regression, local linear fitting has become a method of much popularity. It is natural, therefore, to consider the use of non-global bandwidths in the local linear context, and indeed local bandwidths are often used. In this paper, it is observed that a natural proposal in the literature for combining variable bandwidths with local linear fitting fails in the sense that the resulting mean squared error properties are those normally associated with local rather than variable bandwidths. We are able to understand why this happens in terms of weightings that are involved. We also attempt to investigate how the bias reduction expected of well-chosen variable bandwidths might be achieved in conjunction with local linear fitting.     

Order statistics from extreme value distribution,ii: best linear unbiased estimates and some other uses

Let Y_r+1:n ≤ _Y:r+2:n ≤≤… <Y_n?6:n-<: TYPE-II censored sample from an extreme value population with µ and α as the location and scale parameters, respectively. Tables of coefficients for the best linear unbiased estimators (BLUEs) of µ and α are presented for various choices of censoring and sample sizes n = 2(1)15(5)30; variances and covariance of these estimators are also presented. The computational formulae and procedure used and some checks employed are explained. We finally illustrate some uses of the tables by taking examples.     

Goodness-of-Fit Testing for Exponential Polynomial Growth Curves

A goodness-of-fit test is proposed for the family of exponential polynomial growth curve models (EPGCM; Heinen, 1999 Heinen , M. ( 1999 ). Analytical growth equations and their Genstat 5 equivalents . Netherlands J. Agricult. Sci. 47 : 67 – 89 .[Web of Science ®] , [Google Scholar]), which has wide applications in different areas of science. The exponential growth curve model (EGCM), the most prominent member of the EPGCM family, is a simple and biologically meaningful growth model. Other members of the EPGCM family also cover many realistic growth processes. Thus, a goodness-of-fit test for the EPGCM class has substantial practical value. The goodness-of-fit test developed here is based on the properties of finite differences. The performance of the theory developed is illustrated through simulation and analysis of real data.