Modelling the CPI using a lognormal diffusion process and implications on forecasting inflation

The Maximum Likelihood estimator is used within a lognormaldiffusion process and closed form analytical solutions are obtained.The monthly CPI forecasts are estimated for the period between1970 and 2002. The quarterly estimates of inflation rates areobtained from monthly forecasts rather than from quarterly data.This has significantly improved the estimates of inflation rates.The model also produced a superior fit as compared to randomwalk and GARCH(p,q)-M models. The adopted approach is foundto be simple, economical and generally suitable for modellingstochastic processes that reflect aggregation over time stemmingfrom many factors, and in which the transition path betweenconsecutive states is relatively smooth.     

SAMPLING INSPECTION OF RELIABILITY IN （LOG）NORMAL CASE WITH TYPE I CENSORING

This article proposes a statistical method for working out reliability sampling plans under Type I censored sample for items whose failure times have either normal or lognormal distributions. The quality statistic is a method of moments estimator of a monotonous function of the unreliability. An approach of choosing a truncation time is recommended. The sample size and acceptability constant are approximately determined by using the Cornish-Fisher expansion for quantiles of distribution. Simulation results show that the method given in this article is feasible.     

Identifiability of finite mixtures using a new transform

Identifiability of finite mixtures of the following families of distributions are proved: Weibull, normal log, chi, pareto and power function.     

幂型支付的欧式期权定价公式

在等价鞅测度框架下,讨论了(在到期时刻)期权处于实值状态时支付函数为幂型的股票欧式期权定价公式.这里我们假设无风险利率,股票预期收益率和股价波动率都是时间的确定性函数.本文结果不但包含了原始的Black-Scholes公式,而且可用于上封顶与下保底(幂型)欧式看涨期权的定价.     

Mean squared prediction error in the spatial linear model with estimated covariance parameters

The problem considered is that of predicting the value of a linear functional of a random field when the parameter vector of the covariance function (or generalized covariance function) is unknown. The customary predictor when is unknown, which we call the EBLUP, is obtained by substituting an estimator j for in the expression for the best linear unbiased predictor (BLUP). Similarly, the customary estimator of the mean squared prediction error (MSPE) of the EBLUP is obtained by substituting j for in the expression f for the BLUP's MSPE; we call this the EMSPE. In this article, the appropriateness of the EMSPE as an estimator of the EBLUP's MSPE is examined, and alternative estimators of the EBLUP's MSPE for use when the EMSPE is inappropriate are suggested. Several illustrative examples show that the performance of the EMSPE depends on the strength of spatial correlation; the EMSPE is at its best when the spatial correlation is strong.This research was partially supported by a University of Iowa Old Gold Fellowship (Zimmerman) and by the NSF under grant DMS-8703083 (Cressie).     

保险索赔额的分布及其应用

索赔额的数量描述是保险人估算危险损失关键。本文提出了索赔额的分布拟合，它修正了传统的有关索赔额分布的不足，使保险人能够据此对保险责任作出更准确的估计。     

重尾随机变量和的大偏差

This paper is a further investigation of large deviations for sums of random variables S_n=sum form i=1 to n X_i and S(t)=sum form i=1 to N(t) X_i,(t≥0), where {X_n,n≥1) are independent identically distribution and non-negative random variables, and {N(t),t≥0} is a counting process of non-negative integer-valued random variables, independent of {X_n,n≥1}. In this paper, under the suppose F∈G, which is a bigger heavy-tailed class than C, proved large deviation results for sums of random variables.     

一种新钻孔孔位设计的最优化方法

本文给出了一种寻求新钻孔孔位的最优化方法。该方法将最优钻孔孔位问题转化为一个可计算的最优化问题,并用数论中的一致分布点列求解之。     

一种结构响应预测策略与新可靠度分析方法

工程结构在服役期不可避免地遭受各种不确定因素的侵害,为客观科学地描述不确定因素的影响,可靠度理论得以产生和发展。但传统的可靠度分析方法囿于精度和效率等原因,难以应用于实际工程结构。近年来基于概率守恒原理,概率密度演化理论提出并得到发展。但对复杂的工程结构,效率低下等问题依然有待解决。鉴于此,本文提出两级剖分概率空间的概念,以粗剖分获得的少量代表样本构成训练集,训练Kriging模型;然后细剖分概率空间,通过训练的Kriging模型预测加密样本响应提高分析效率。基于此,结合以概率密度演化理论为基础的物理综合法,提出了一种新的可靠度分析方法。通过对一解析系统和一幢钢筋混凝土框架结构的响应预测与可靠度分析,证明了新提出方法的精度和效率。     

自然与社会环境中的幂律现象和双帕累托对数正态分布

幂律是在许多自然和社会环境中都能观察到的现象。但如何精确地描述这种现象并合理地解释这种现象的成因却一直令人困扰。双帕累托对数正态分布从随机过程的角度对这一问题给出了一个新的思路。本文首先描述双帕累托对数正态分布的数学推导与生成模型，然后解释此分布为什么会在社交网朋友的数量、互联网文件的大小、股票市场的回报、社会财富的占有、城市人口的规模、油田的储量及森林火灾焚烧的面积等现象中出现的可能原因。