带一个插值点的回归模型的参数分析

一般的回归模型中认为所有的数据点的重要程度是相同的,但有的实际问题中可能由于种种原因,其中有某个数据特别重要,针对这种情况,提出一种带一个插值点的回归模型,并得到这种回归模型三个参数的最大似然估计.     

面板数据公共变点的非参数最大似然估计

Gao Pengli;Xia Zhiming(School of Mathematics,Northwest University,Xi'an 710127,China)     

累积Logistic回归模型结构变点的序贯检验

分类时间序列在生物医学、社会学和遗传学等领域有着广泛的应用,累积Logistic回归模型是分类时间序列建模的一类重要模型.本文基于偏似然得分过程(Partial likelihood score process)提出一种变点序贯检验方法,监测累积Logistic回归模型的结构是否发生变化.原假设下推导检验统计量的极限分...     

单参数Pareto分布变点估计研究

研究单参数Pareto分布存在变点时的估计问题,分别利用极大似然估计法和贝叶斯方法对单参数Pareto分布的变点进行估计,并运用Matlab软件进行随机模拟,随机结果表明贝叶斯方法与极大似然估计相比,估计值更接近真值.     

变系数混合模型的光滑样条推断

为了拟合纵向数据和其他相关数据,本文提出了变系数混合效应模型(VCMM).该模型运用变系数线性部分来表示协变量对响应变量的影响,而用随机效应来描述纵向数据组内的相关性, 因此,该模型允许协变量和响应变量之间存在十分灵活的泛函关系.文中运用光滑样条来估计均值部分的系数函数,而用限制最大似然的方法同时估计出光滑参数和方差成分,我们还得到了所提估计的计算方法.大量的模拟研究表明对于具有各种协方差结构的变系数混合效应模型,运用本文所提出的方法都能够十分有效地估计出模型中的系数函数和方差成分.     

非对称广义自回归条件异方差的新模型

本文提出了一个新的非对称广义自回归条件异方差的新模型,证明了该模型宽平稳及其最简模型偶数价矩存在的充要条件。     

当前状态数据下单变点危险率治愈模型的拟极大似然估计

变点危险率模型已受到广泛关注.它不仅可以更加直接地显示治疗效果或医学上的突破,也可以提供这些事件发生的时间点.在这篇文章中,我们提出当前状态数据下的单边点危险率治愈模型并探讨了这个模型的估计方法.我们建立了估计的大样本理论并通过模拟评估有限样本下的估计.     

基于经验欧氏似然的均值单变点检测

均值单变点检测是研究变点问题的基础.论文根据均值单变点模型的特点,构造截断经验欧氏似然比检验函数并给出显式表达.在此基础上,得到了零假设下检验统计量的极限分布为极值分布,给出变点的诊断方法.在有变点的情况下,进一步给出变点位置的估计和其相合性的理论证明.最后通过数值模拟和尼罗河年流量的实证分析说明所提方法的有效性和实用...     

k组多元自相关数据服从同一模型的假设检验

本文考虑了ｋ个多元同类自相关线性模型的回归系数和协方差阵相等的同时检验问题，得到了似然比检验统计量，统计量的矩及渐近中心分布。     

周期相关时间序列与周期自回归模型

介绍了周期相关时间序列和周期自回归模型,并研究了周期自回归时间序列的稳定性及周期性,得到了它为周期相关时间序列的一个充要条件,推广了文献[1]的结论.     

Pitfalls of Fitting Autoregressive Models for Heavy-Tailed Time Series

We consider the analysis of time series data which require models with a heavy-tailed marginal distribution. A natural model to attempt to fit to time series data is an autoregression of order p, where p itself is often determined from the data. Several methods of parameter estimation for heavy tailed autoregressions have been considered, including Yule–Walker estimation, linear programming estimators, and periodogram based estimators. We investigate the statistical pitfalls of the first two methods when the models are mis-specified—either completely or due to the presence of outliers. We illustrate the results of our considerations on both simulated and real data sets. A warning is sounded against the assumption that autoregressions will be an applicable class of models for fitting heavy tailed data.     

A Score Type Test for General Autoregressive Models in Time Series

This paper is devoted to the goodness-of-fit test for the general autoregressive models in time series. By averaging for the weighted residuals, we construct a score type test which is asymptotically standard chi-squared under the null and has some desirable power properties under the alternatives. Specifically, the test is sensitive to alternatives and can detect the alternatives approaching, along a direction, the null at a rate that is arbitrarily close to n-1/2. Furthermore, when the alternatives are not directional, we construct asymptotically distribution-free maximin tests for a large class of alternatives. The performance of the tests is evaluated through simulation studies.     

多元时间序列模型协方差矩阵的递归算法

本文推导了多元时序模型的协方差矩阵与模型参数的关系式，并给出了计算多维时序过程自协方差矩阵的递归算法     

结构经济序列分析的状态空间方法

本文讨论结构经济时间序列用状态空间模型进行分解处理的方法．在§１中综述结构时间序列的状态空间描述．§２中着重论述了将处理不完全数据的ＥＭ－算法应用于状态空间模型参数的极大似然估计．在§３中给出采用本文所述方法对一些我国宏观经济序列的计算实例．     

增长型经济变量的趋势时间序列预测模型

针对增长型外汇储备时间序列变化复杂性的特点,可以建立确定性趋势的时间序列模型及包含单位根的随机趋势模型.实际计算显示,确定性趋势的时间序列模型具有较高的预测精度.     

ARCH模型的多变点检验

研究自回归条件异方差(ARCH)模型的多变点检验问题.提出一种拟似然比检验统计量,并在原假设下给出统计量的极限分布.在假设检验过程中得到变点个数的一致估计.数值模拟与实例分析说明了方法的合理性.     

The Likelihood of the Parameters of a Continuous Time Vector Autoregressive Model

This paper provides a method of constructing the likelihood function of the parameters of a continuous time vector autoregressive model on the basis of discrete data without requiring the restrictions extant methods impose on the data that are capable of being rejected by a statistical test. In particular, the method does not rely on a steady-state assumption that can rule out unit root processes; it allows for weak assumptions on the innovations; and it allows for a mixture of skip-sampled and temporally-aggregated data. This revised version was published online in August 2006 with corrections to the Cover Date.     

Evolving Time Series Forecasting ARMA Models

Time Series Forecasting (TSF) allows the modeling of complex systems as black-boxes, being a focus of attention in several research arenas such as Operational Research, Statistics or Computer Science. Alternative TSF approaches emerged from the Artificial Intelligence arena, where optimization algorithms inspired on natural selection processes, such as Evolutionary Algorithms (EAs), are popular. The present work reports on a two-level architecture, where a (meta-level) binary EA will search for the best ARMA model, being the parameters optimized by a (low-level) EA, which encodes real values. The handicap of this approach is compared with conventional forecasting methods, being competitive.     

Efficient Mean Estimation in Log-Normal Linear Models with First-Order Correlated Errors

In this paper, we propose a log-normal linear model whose errors are first-order correlated, and suggest a two-stage method for the efficient estimation of the conditional mean of the response variable at the original scale. We obtain two estimators which minimize the asymptotic mean squared error (MM) and the asymptotic bias (MB), respectively. Both the estimators are very easy to implement, and simulation studies show that they are perform better.