一元整系数多项式一个结论的推广应用

将一元整系数多项式有理根的一个结论在多元多项式上进行了推广,从而得到多元多项式因式分解的一种方法。     

Goodness—of—fit for the two—parameter weibull distribution with estimated parameters

Goodness—of—fit statistics based on the empirical distribution function (EDF) are not distribution—free when parameters for the hypothesized distribution are estimated. Tables are percentile values of several EDF statistics are available for the two—parameter Weibull distribution when parameters are estimated by maximum likelihood. To determine how these tabled values change when simpler estimators are employed, percentile scores for EDF goodness—of—fit tests were obtained by Monte—Carlo simulation for maximum likelihood estimators (MLEs), good linear unbiased estimators (GLUEs), and modified Cramer—von Mises, Anderson—Darling, and Watson statistics are presented for GLUEs for both complete and censored samples. Critical values for Kolmogorov—Smirnov statistics were less affected by the method of estimation than were closer for MLEs and MGLUEs than for MGLUEs and GLUEs. On the other hand, MGLUE and GLUE results were much more similar to each other than to the MLE results when censoring was light and sample sizes were large.     

Exponential Inequalities in Stochastic Inverse Problems Using an Iterative Method

We consider an iterative method in order to solve linear inverse problems. We establish exponential inequalities for the probability of the distance between the approximated solution and the exact one for a calibration problem. The approximate is given by an iterative method with Gaussian errors. We treat an operator equation of the form Ax = u, where A is a compact operator.     

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.     

论机关管理的价值目标取向

作为与广义行政管理具有密切联系的机关管理,其价值取向必然与行政管理的价值取向具有一致性,与农业、工业、信息社会相对应,行政管理的价值取向经历了由稳定→效率→治理服务的转变,相应的机关管理的价值目标也应经历这三个阶段和境界。当前机关管理的价值目标取向是以以人为本为核心,以治理理念为框架的服务社会理念。     

Finding environmentally important industry clusters: Multiway cut approach using nonnegative matrix factorization

This paper proposes an optimal combinatorial method for finding groups of industries with relatively large CO₂ emissions through industrial relations. Using an economic input–output table, we estimated a non-symmetric matrix describing how much CO₂ is emitted in producing the commodity of industry i, which was purchased to produce commodity of industry j, to meet the final demand for a specific commodity. A symmetric strength of relations matrix describing the CO₂ emissions associated with the industrial relations was further estimated using the non-symmetric matrix. The strength of relations matrix can be viewed as a representation of the supply-chain network of the final commodity. In this study, we estimated the strength of relations matrix associated with the final demand for automobiles and applied the multiway cut approach using nonnegative matrix factorization to the matrix in order to find environmentally important industry clusters in the Japanese automobile supply chain. According to our empirical results, the optimal number of industry clusters is 19, and 4 industry clusters are playing a key role in CO₂ emission reduction.     

Automatic selection of a linear predictor through frequency domain cross-validation

Given data from a weakly stationary stochastic process in discrete time, and any L-step ahead linear predictor estimated from that data, we will construct an approximately unbiased estimator of the resulting mean squared error of L-step ahead linear prediction. The motivation for the estimator is based on frequency domain cross-validation, and hence the range of validity and applicability of the resulting selection method is not limited by particular assumptions about the structure of the underlying stochastic process or the form of the fitted linear predictors. We also propose a new frequency domain predictor fitting method. The method provides a natural finite-past analog to the existing spectral factorization techniques, and it compares favorably with the existing techniques, both asymptotically and for finite samples. In a Monte Carlo study, we compare several predictor selection methods, at lead times one and five. The performance criterion used is the mean squared prediction error of the selected predictor. The new selection methods work well, and a comparison of results for the two different lead times underscores the need for tailoring the selection criterion to suit the lead time.     

唯一分解环上的一元多项式环的因子分解

本文提供了一个求唯一分解环Ｉ上的多项式的落在Ｉ的商域中的全部根的方法并得到了一个判别商域上的多项式不可约的充分条件     

An Empirical Analysis of Some Nonparametric Goodness-of-Fit Tests for Censored Data

In this article, we consider some nonparametric goodness-of-fit tests for right censored samples, viz., the modified Kolmogorov, Cramer–von Mises–Smirnov, Anderson–Darling, and Nikulin–Rao–Robson χ² tests. We also consider an approach based on a transformation of the original censored sample to a complete one and the subsequent application of classical goodness-of-fit tests to the pseudo-complete sample. We then compare these tests in terms of power in the case of Type II censored data along with the power of the Neyman–Pearson test, and draw some conclusions. Finally, we present an illustrative example.     

“Mad Money” madness: Jim Cramer's image restoration effort

The following is an evaluation of “Mad Money” host Jim Cramer's image restoration attempts during his March 2009 week-long media battle with Jon Stewart, host of “The Daily Show.” Using Benoit's (1995) image restoration theory as a conceptual framework, it is apparent that Cramer failed in restoring his reputation because he frequently coupled incongruent strategies and was unable to grasp the dynamic nature of communicative image restoration.