季节指数的极大似然估计

文章探索运用数理统计的极大似然估计法计算季节指数,得出的计算公式与传统的算术方法完全一致,从直观上保持了与传统算法的衔接性,又可以得出季节指数的区间估计,提高了季节指数计算的完备性.     

平滑转移空间随机前沿模型的贝叶斯估计

经济数据常存在空间相关性,忽略空间相关性会引发内生性问题,导致相应估计量有偏且不一致。空间随机前沿模型在随机前沿模型的基础上考虑了生产单元的空间相关性,更利于效率测算。然而现有空间随机前沿模型的生产函数形式单一,适用性较差,实证分析存在局限性。文章在空间随机前沿模型中引入平滑转移效应,构建了平滑转移空间随机前沿模型,该模型同时考虑了空间相关性和个体异质性,适用性较佳。为丰富估计方法,同时采用极大似然方法和贝叶斯方法估计模型,其中极大似然估计的核心在于推导对数似然函数、对数似然函数的最优化以及使用JLMS法估计技术效率,贝叶斯估计的核心在于推导未知参数的后验分布及执行MCMC抽样。数值模拟结果显示：（1）极大似然估计和贝叶斯估计的估计精度均较高,其中贝叶斯估计的估计精度略高于极大似然估计;增加样本容量,贝叶斯估计和极大似然估计的估计精度更高。（2）若忽略空间效应或者平滑转移效应,则估计精度较低。     

全样本几何分布串-并联系统产品的统计分析

针对几何分布串-并联系统产品,在全样本场合下给出了参数的矩估计、极大似然估计和近似区间估计,从理论上证明了矩估计和极大似然估计的唯一性,同时还通过大量Monte-Carlo模拟分别考察了参数的点估计和近似区间估计的精度,从中可以看到参数的矩估计和极大似然估计的精度相差无几。     

全样本指数分布并-串联系统产品的统计分析

文章对指数分布并一串联系统产品在全样本场合下给出了参数的矩估计、极大似然估计、精确的区间估计和近似区间估计,并通过Monte-Carlo模拟考察了参数点估计和区间估计的精度,得出矩估计和极大似然估计的效果没有太大差异,但精确区间估计的效果要优于近似区间估计.     

平方误差和LINEX损失函数下逆Rayleigh分布参数的经验Bayes估计

在平方误差和LINEX损失函数下,导出了逆Rayleigh分布参数的极大似然估计、Bayes估计和经验Bayes估计,并给出了Monte Carlo数值模拟比较结果.     

正态总体下参数的优化极大似然估计方法

文章讨论了一种新的抽样方法,基于这一抽样方法提出了样本参数的优化极大似然估计,并进一步与简单随机抽样下的参数的极大似然估计结果作比较,从估计渐进效率的角度说明了该方法的优良性。     

分组数据情形下一般指数分布刻度参数的极大似然估计

文章研究了分组数据情形下,一般指数分布(GED)刻度参数的极大似然估计存在且唯一的充要条件,进而得到了极大似然估计具有强相合性.     

几何分布冷贮备产品的统计分析

文章给出了全样本场合几何分布冷贮备系统产品在开关寿命为几何型且开关失效时产品不立即失效的情形下参数的矩估计和极大似然估计,并通过大量Monte-Carlo模拟考察点估计的精度得到极大似然估计优于矩估计.     

EM算法在删失数据分布和混合分布参数估计中的应用

EM算法是一种迭代算法,主要采用后验分布的众数或极大似然估计,广泛的应用于删失数据,截尾数据,成群数据,带有讨厌参数的数据等。文章介绍EM算法,并对删失数据的对数正态分布参数估计和混合正态分布参数的极大似然估计进行了模拟,模拟结果表明对删失数据分布的参数估计和复杂的极大似然估计,EM算法是有效的,估值精度满足要求。     

马尔可夫转换模型的极大似然估计的算法

由金融和经济时间序列,文章引入了马尔可夫转换模型并详细给出其原理--隐藏马尔可夫模型,以及在条件高斯下的极大似然估计方法.通过引入新的模型--扩张隐藏马尔可夫模型,对多种状态转移的情形下的极大似然估计量的算法进行了改进.     

Global sensitivity analysis using complex linear models

A global sensitivity analysis of complex computer codes is usually performed by calculating the Sobol indices. The indices are estimated using Monte Carlo methods. The Monte Carlo simulations are time-consuming even if the computer response is replaced by a metamodel. This paper proposes a new method for calculating sensitivity indices that overcomes the Monte Carlo estimation. The method assumes a discretization of the domain of simulation and uses the expansion of the computer response on an orthogonal basis of complex functions to built a metamodel. This metamodel is then used to derive an analytical estimation of the Sobol indices. This approach is successfully tested on analytical functions and is compared with two alternative methods.     

Asymptotic normality for plug-in estimators of diversity indices on countable alphabets

The plug-in estimator is one of the most popular approaches to the estimation of diversity indices. In this paper, we study its asymptotic distribution for a large class of diversity indices on countable alphabets. In particular, we give conditions for the plug-in estimator to be asymptotically normal, and in the case of uniform distributions, where asymptotic normality fails, we give conditions for the asymptotic distribution to be chi-squared. Our results cover some of the most commonly used indices, including Simpson's index, Reńyi's entropy and Shannon's entropy.     

A Comparison of Forecasting Accuracies of Alternative Regional Production Index Methodologies

This article examines the forecasting accuracies of various methods used by Federal Reserve Banks to estimate real value added by regional manufacturing industries. Using Texas manufacturing data and weighted forecasting accuracy measures consistent with index number construction for Texas, obtained results support the use of very simple methods based on the assumption of product exhaustion, allowing for technical change. More complex methods using Cobb-Douglas production functions estimated by Bayesian techniques did not perform as well, not because of lack of conceptual sophistication or appropriate prior information but probably because of the small number of observations and collinearity of the data that are available when constructing regional production indices. These results must be qualified. The weighted forecasting accuracy measures tend to obscure the fact that no one method is uniformly superior to the other methods for all industries. Given industry weights different from those for Texas, the results presented here could be reversed. Confirmation of the conclusions drawn await the results of other regional manufacturing studies.     

要素均衡： 人类发展指数的算法改进与实证研究

 人类发展指数是一个影响很大、应用很广的衡量人类发展成就的综合指标,但其传统算法存在两个统计缺陷：其一是缺乏对各向度本身的分布敏感性;其二是没有考虑各向度之间的均衡性。本文以后者为针对,采用测度准则法,提出一种以要素均衡为导向的人类发展指数改进法。实证显示,与人类发展指数的传统及现行算法相比较,此改进既能更好地体现人类发展的要义,也可提高人类发展指数的统计鉴别力,所以可用作计算人类发展指数的一种新选择。     

Multi-factor prediction of water flooding efficiency based on a time-varying system

ABSTRACT

The neural network prediction method gets good historical matching between prediction indices and influence factor indices, while the differential simulation prediction method can reflect the changing trend of prediction indices; considering these new traits, a new multi-factor prediction method is proposed to organically combine these two prediction methods. At first, the input–output relation between water flooding efficiency in ultra-high water cut stage and their influence factors is viewed as a time varying system, then the BP neural network is introduced in parameter identification of differential simulation to obtain a new multi-factor prediction method of functional simulation based on the time varying system. This new prediction model has got good self-adaptability since its parameters change by time. Moreover, it has better results in the mid-long-term water flooding efficiency prediction because the non convergence problem appeared in the coupling process can be overcome in the training process of the neural network by variable learning rates. In the end, practical output prediction cases in two different oilfield blocks in China are given. The computational results show that the prediction results obtained using the new multi-factor prediction method are in good agreement with the reality, even much better than the results obtained by other prediction methods.     

Detection and estimation of structural change in heavy-tailed sequence

In this paper, bootstrap detection and ratio estimation are proposed to analysis mean change in heavy-tailed distribution. First, the test statistic is constructed into a ratio form on the CUSUM process. Then, the asymptotic distribution of test statistic is obtained and the consistency of the test is proved. To solve the problem that the null distribution of the test statistic contains unknown tail index, we present a bootstrap approximation method to determine the critical values of the null distribution. We also discuss how to estimate change point based on ratio method. The consistency and rate of convergence for the change-point estimator are established. Finally, the excellent performance of our method is demonstrated through simulations using artificial and real data sets. Especially the simulation results of bootstrap test are better than those of another existing method.     

Entropic representation and estimation of diversity indices

This paper serves a twofold purpose. First, a unified perspective on diversity indices is introduced based on an entropic basis. It is shown that the class of all linear combinations of the entropic basis, referred to as the class of linear diversity indices, covers a wide range of diversity indices used in the literature. Second, a class of estimators for linear diversity indices is proposed and it is shown that these estimators have rapidly decaying biases and asymptotic normality.     

基于信息可替代性的评价指标筛选研究

提出一种指标筛选方法,旨在筛选出对评价结果影响显著、反映信息重叠程度低的评价指标。以相对离散系数作为指标的信息含量,依据提出的累计信息贡献率标准删除信息含量明显偏小的指标进行指标的初筛,克服现有研究仅利用相对离散系数进行指标筛选的不足。借鉴聚类分析的思想,取初筛后保留下来的一个指标与其余各指标构成Person相关系数平方的均值,反映该指标的信息可被其余全部指标替代的程度。通过信息可替代性标准剔除信息可替代性较大的指标,保证最终被保留的指标间反映的信息重叠程度低,克服现有研究仅通过两个指标间的相关性筛选指标难以有效降低评价指标集信息重叠的不足。此外,针对剔除信息重叠的指标与剔除对评价结果影响不显著的指标何者优先为宜的问题,提出了显著再相关的指标筛选标准。最后,通过一个实例说明指标筛选方法的可行性。     

季节调整新方法及其原理研究

本文的主要工作是从频域的角度对季节调整中“季节滤子”的设计及估计问题进行研究。通过将直接信号提取（DSEF）方法引入到季节调整的应用之中,突破现有季节调整方法中仅能处理季度或月度数据的限制,且该方法下季节调整后的序列是理论季节调整后序列的“均方误差”最小估计。将DSEF方法应用于对中国季度进出口总额序列的季节调整分析中。分析结果显示,相比于X-11和SEATS方法,DSEF方法季节调整结果的离差较小且稳健性较好。     

时间序列多期累积量的预测区间及应用

一、引言在时间序列分析的许多实际应用中,考虑的预测区间通常有两种,一种是单期预测区间,另一种是多期联立预测区间。后者是一个新的研究领域。对时间序列Ｘｔ,可观察时间ｔ＝１,２,…,ｎ,建立单期预测区间是指对给定的α,找到常数Ｃ使Ｐ（｜Ｘｎ＋ｋ－Ｘｎ（ｋ...