Bayesian Treatment of Model Uncertainty for Partially Applicable Models

This article discusses how analyst's or expert's beliefs on the credibility and quality of models can be assessed and incorporated into the uncertainty assessment of an unknown of interest. The proposed methodology is a specialization of the Bayesian framework for the assessment of model uncertainty presented in an earlier paper. This formalism treats models as sources of information in assessing the uncertainty of an unknown, and it allows the use of predictions from multiple models as well as experimental validation data about the models’ performances. In this article, the methodology is extended to incorporate additional types of information about the model, namely, subjective information in terms of credibility of the model and its applicability when it is used outside its intended domain of application. An example in the context of fire risk modeling is also provided.     

On the construction of optimal experimental designs: a penalty approach

Tn optimal experimental design theory there are well-known situations, in which additional constraints are implied to the design set. These constraints destroy in general the simplex structure of the set of feasible points of the design set. Thus the available iteration procedures for the unrestricted case are no longer applicable.

In this paper a penalty approach is suggested which transforms the restricted problem to the unrestricted case and allows the application of well-known algorithms such as the Fedorov-Wynn-type or the projected gradient procedure.     

关于留数定理的一个推广

研究了留数定理的有关问题,通过举例论证和归纳推理等证明方法,得到了单个留数定理的一个推广结论,最后将此结论应用于一些积分问题中.     

On asymptotic distribution of prediction in functional linear regression

There has been substantial recent attention on problems involving a functional linear regression model with scalar response. Among them, there have been few works dealing with asymptotic distribution of prediction in functional linear regression models. In recent literature, the centeral limit theorem for prediction has been discussed, but the proof and conditions under which the random bias terms for a fixed predictor converge to zero have been ignored so that the impact of these terms on the convergence of the prediction has not been well understood. Clarifying the proof and conditions under which the bias terms converge to zero, we show that the asymptotic distribution of the prediction is normal. Furthermore, we have derived those results related to other terms that already obtained by others, under milder conditions. Finally, we conduct a simulation study to investigate performance of the asymptotic distribution under various parameter settings.     

When is n large enough? Looking for the right sample size to estimate proportions

The central limit theorem indicates that when the sample size goes to infinite, the sampling distribution of means tends to follow a normal distribution; it is the basis for the most usual confidence interval and sample size formulas. This study analyzes what sample size is large enough to assume that the distribution of the estimator of a proportion follows a Normal distribution. Also, we propose the use of a correction factor in sample size formulas to ensure a confidence level even when the central limit theorem does not apply for these distributions.     

A general central limit theorem and a subsampling variance estimator for α‐mixing point processes

We establish a central limit theorem for multivariate summary statistics of nonstationary α‐mixing spatial point processes and a subsampling estimator of the covariance matrix of such statistics. The central limit theorem is crucial for establishing asymptotic properties of estimators in statistics for spatial point processes. The covariance matrix subsampling estimator is flexible and model free. It is needed, for example, to construct confidence intervals and ellipsoids based on asymptotic normality of estimators. We also provide a simulation study investigating an application of our results to estimating functions.     

关于Feigenbaum型泛函方程的C^1解

利用Schauder不动点定理、自同胚和紧凸子集的相关性质研究Feigenbaum型泛函方程的连续可微解的存在性和唯一性.     

我国排污权交易制度的应用研究

排污权交易制度作为一种新的治理企业污染的市场手段比政府行政管制手段具有更大的优势,它将市场机制充分地运用于环境资源的配置,在总量控制的前提下,通过排污权的交易,实现低成本治理污染,有利于环境保护和经济的可持续发展.我国已经具有了实施排污权交易的基本条件,根据我国的现实国情,设计了包括立法、信息公示、监控系统、总量控制、排污权的初始分配和市场交易等六部分构成的排污权交易制度,提出了我国实现排污权交易一些急待解决的问题.     

基于繁殖规律的生猪市场供应模型

生猪生产具有发散性蛛网效应的特点,目前市场的波动正在加剧之中。基于生猪繁殖的自然规律特点,构建了一个易于分析与预测的生猪生产模型。分析显示,目前的生猪市场正在酝酿一场新的危机。导致市场波动的根源在于及时、准确信息的缺失,致使养殖者对母猪盲目的补栏。     

模态系统T的Herbrand定理

模态Herbrand定理研究是模态逻辑领域的重要开放问题.不含Barcan公式的模态系统T的Herbrand定理的获得必须完成两个方向的证明工作:首先是证明相对简单的从右到左方向;而对于相对复杂的从左到右方向,则可以通过在加标公式表列系统K的基础上增加特殊规则获得加标公式表列系统T,证明T的可靠性和完全性,并在此基础上完成从左到右方向的证明.同样的方法还可以用来证明模态系统D、K4、s4的Herbrand定理.