Monte Carlo Based Ensemble Forecasting

Ensemble forecasting involves the use of several integrations of a numerical model. Even if this model is assumed to be known, ensembles are needed due to uncertainty in initial conditions. The ideas discussed in this paper incorporate aspects of both analytic model approximations and Monte Carlo arguments to gain some efficiency in the generation and use of ensembles. Efficiency is gained through the use of importance sampling Monte Carlo. Once ensemble members are generated, suggestions for their use, involving both approximation and statistical notions such as kernel density estimation and mixture modeling are discussed. Fully deterministic procedures derived from the Monte Carlo analysis are also described. Examples using the three-dimensional Lorenz system are described.     

Learning rates of regularized regression for exponentially strongly mixing sequence

The study of regularized learning algorithms associated with least squared loss is one of very important issues. Wu et al. [2006. Learning rates of least-square regularized regression. Found. Comput. Math. 6, 171–192] established fast learning rates m^-θ$m^{-\theta }$ for the least square regularized regression in reproducing kernel Hilbert spaces under some assumptions on Mercer kernels and on regression functions, where m   denoted the number of the samples and θ$\theta$ may be arbitrarily close to 1. They assumed as in most existing works that the set of samples were drawn independently from the underlying probability. However, independence is a very restrictive concept. Without the independence of samples, the study of learning algorithms is more involved, and little progress has been made. The aim of this paper is to establish the above results of Wu et al. for the dependent samples. The dependence of samples in this paper is expressed in terms of exponentially strongly mixing sequence.     

半模糊超球支持向量机多类分类方法研究

针对支持向量机在大类别模式分类中存在的问题,提出了一种基于半模糊核聚类的超球支持向量机分类方法.该方法首先利用半模糊核聚类方法对样本进行预处理,完成边缘样本的选取,进而以所选样本为训练样本进行超球支持向量机训练,从而有效提高分类器的性能.实验表明,该方法比标准支持向量机多类分类方法具有更高的速度和精度.     

全要素生产率影响行业收入差距的实证研究

利用２００３～２０１１年中国工业行业大类面板数据,运用ＭＬＤ指数、非参数核密度估计和变系数模型从全要素生产率的角度考察了中国行业收入差距的影响因素。研究发现,中国工业行业的ＭＬＤ指数呈下降趋势,但收入分布集中程度降低、尾部变厚;面板数据变系数模型结果表明全要素生产率是行业收入差距的重要影响因素,在不考虑其他因素的情况下对不同行业影响强度的差距在６．９％左右;随着其他影响因素的纳入,全要素生产率对单个行业影响程度在逐渐降低,其影响强度在行业间差距呈上升趋势。     

Using Asset Prices to Measure the Persistence of the Marginal Utility of Wealth

We derive a lower bound for the volatility of the permanent component of investors' marginal utility of wealth or, more generally, asset pricing kernels. The bound is based on return properties of long‐term zero‐coupon bonds, risk‐free bonds, and other risky securities. We find the permanent component of the pricing kernel to be very volatile; its volatility is about at least as large as the volatility of the stochastic discount factor. A related measure for the transitory component suggest it to be considerably less important. We also show that, for many cases where the pricing kernel is a function of consumption, innovations to consumption need to have permanent effects.     

试论宋明理学之“天人合一”仁学生态伦理思想

宋明理学家"天人合一"的仁学生态环境伦理哲学观,提出"仁者浑然与物同体",人与自然同本同源,德治不仅是对社会还包括以仁爱原则对自然万物;提出"仁者,天地生物之心"、人"为天地立心",实现以人为本、爱物与爱人的统一,达到人的道德完善。宋明理学在生态环境伦理观上的独特认识,既有理论也有实践价值,对我们今天解决生态危机,促进环境保护和社会经济的可持续发展具有明显现实意义。     

Functional Discriminant Coordinates

In this article we propose a new method of construction of discriminant coordinates and their kernel variant based on the regularization (ridge regression). Moreover, we compare the case of discriminant coordinates, functional discriminant coordinates and the kernel version of functional discriminant coordinates on 20 data sets from a wide variety of application domains using values of the criterion of goodness and statistical tests. Our experiments show that the kernel variant of discriminant coordinates provides significantly more accurate results on the examined data sets.     

Discrete Nonparametric Kernel and Parametric Methods for the Modeling of Pavement Deterioration

This article is concerned with one discrete nonparametric kernel and two parametric regression approaches for providing the evolution law of pavement deterioration. The first parametric approach is a survival data analysis method; and the second is a nonlinear mixed-effects model. The nonparametric approach consists of a regression estimator using the discrete associated kernels. Some asymptotic properties of the discrete nonparametric kernel estimator are shown as, in particular, its almost sure consistency. Moreover, two data-driven bandwidth selection methods are also given, with a new theoretical explicit expression of optimal bandwidth provided for this nonparametric estimator. A comparative simulation study is realized with an application of bootstrap methods to a measure of statistical accuracy.     

Learning rates of multi-kernel regularized regression

Learning the kernel function has recently received considerable attention in machine learning. In this paper, we consider the multi-kernel regularized regression (MKRR) algorithm associated with least square loss over reproducing kernel Hilbert spaces. We provide an error analysis for the MKRR algorithm based on the Rademacher chaos complexity and iteration techniques. The main result is an explicit learning rate for the MKRR algorithm. Two examples are given to illustrate that the learning rates are much improved compared to those in the literature.     

个体与社会:孔子重“礼”原因双维度

孔子重"礼",对周礼的迷恋近乎痴狂,但他对周礼进行了与时俱进的修改和充实,一方面扩大了"礼"的社会基础,打破了"礼不下庶人"的等级限制;另一方面提升了"礼"的内涵,将"礼"与"仁"有机结合起来,赋予了"礼"以"仁"的灵魂.基于对"礼"的重新阐释和改造,我们可以从两个维度研探出孔子重"礼"的原因:一是孔子认为"礼"为个体趋"仁"的必然途径和桥梁;另一是孔子认为"礼"是社会和谐的必要工具.