巴斯卡分布中未知参数的估计

在巴斯卡分布的参数r,p都未知的情况下,讨论了未知参数的极大似然估计。结果表明,r的极大似然估计可转化成一个超越方程的求解,采用该超越方程的解作为r的估计量有强相合性,导出了在大样本下r和p的置信区间。并用实例验证了估计方法的合理性。     

农民工对中国经济的贡献测算

中国的农民工阶层是改革开放以后才出现的。根据公开的统计数据,近年来,农民工流动的规模每年已经超过1亿人。农民工占城市第二、第三产业劳动力的比重很多地方已经超过50%。可以说,农民工已经成为城市第二、第三产业劳动力大军的重要组成部分,农民工对中国国民经济的贡献功不可没。文章利用农业部农村固定观察点的数据,测算了农民工对全国第二、第三产业产值增长的贡献。研究结果表明,1995年至2005年,如果假设农民工劳动生产率与城市产业工人一样,则农民工对中国第二、第三产业产值贡献占到这些产业产值的14.77%~24.35%,平均每年为19.98%;如果假设农民工劳动生产率低于城市产业工人,则农民工对中国第二、第三产业产值贡献占到这些产业产值的1.67%~4.71%,平均每年为3.26%,而且贡献份额有逐年增长的趋势。     

相关跳频通信系统安全性分析

分析了相关跳频通信系统中频率序列的检测以及纠错译码,并从第三方接收的角度探讨了相关跳频信号的接收、译码以及转移函数反推的复杂度。分析结果表明:相关跳频通信并不是一种抗截获的无线通信方式,其通信安全是建立在转移函数机制上的。转移函数反推的复杂度非常高,给相关跳频通信体制提供了很好的安全性。     

基于独立分量分析的降噪技术

介绍了新兴的独立分量分析技术的基本概念和原理,以及具有代表性的算法,即FastICA算法、EASI算法、非线性PCA算法和基于自然梯度的最大似然估计算法。通过降噪仿真实验,并采用均方误差作为降噪的性能指数,对这些算法与传统的自适应信号处理算法进行比较。所得实验结果表明,独立分量分析算法在降噪上的效果优于自适应信号处理算法。因此在降噪上具有较大的应用价值。     

THE AUTOREGRESSIVE MOVING AVERAGE MODEL FOR SPATIAL ANALYSIS

A two dimensional autoregressive moving average spatial model is used to analyse spatial interaction. Maximum likelihood estimates of the unknown parameters are derived as the solution of a system of nonlinear equations, and are shown to be best asymptotic normal. One important computational procedure is discussed. The argument is extended to the general regression model with autoregressive moving average residuals. Explicit computational formulae are given.     

Singly and Doubly Censored Current Status Data: Estimation, Asymptotics and Regression

In biostatistical applications interest often focuses on the estimation of the distribution of time between two consecutive events. If the initial event time is observed and the subsequent event time is only known to be larger or smaller than an observed point in time, then the data is described by the well-understood singly censored current status model, also known as interval censored data, case I. Jewell et al. (1994) extended this current status model by allowing the initial time to be unobserved, with its distribution over an observed interval [A, B] known; the data is referred to as doubly censored current status data. This model has applications in AIDS partner studies. If the initial time is known to be uniformly distribute d, the model reduces to a submodel of the current status model with the same asymptotic information bounds as in the current status model, but the distribution of interest is essentially the derivative of the distribution of interest in the current status model. As a consequence the non-parametric maximum likelihood estimator is inconsistent. Moreover, this submodel contains only smooth heavy tailed distributions for which no moments exist. In this paper, we discuss the connection between the singly censored current status model and the doubly censored current status model (for the uniform initial time) in detail and explain the difficulties in estimation which arise in the doubly censored case. We propose a regularized MLE corresponding with the current status model. We prove rate results, efficiency of smooth functionals of the regularized MLE, and present a generally applicable efficient method for estimation of regression parameters, which does not rely on the existence of moments. We also discuss extending these ideas to a non-uniform distribution for the initial time.     

Mixture models in survival analysis: Are they worth the risk?

There has been a recurring interest in models for survival data which hypothesize subpopulations of individuals highly susceptible to some type of adverse event. Other individuals are assumed to be at much less risk. Most commonly, in clinical trials, these models attempt to estimate the fraction of patients cured of disease. The use of such models is examined, and the likelihood function is advocated as an informative inference tool.     

The asymptotic properties of the maximum-relevance weighted likelihood estimators

We define the maximum-relevance weighted likelihood estimator (MREWLE) using the relevance-weighted likelihood function introduced by Hu and Zidek (1995). Furthermore, we establish the consistency of the MREWLE under a wide range of conditions. Our results generalize those of Wald (1948) to both nonidentically distributed random variables and unequally weighted likelihoods (when dealing with independent data sets of varying relevance to the inferential problem of interest). Asymptotic normality is also proven. Applying these results to generalized smoothing model is discussed.     

On the Empirical Likelihood Ratio for Smooth Functions of M-functionals

It is known that the empirical likelihood ratio can be used to construct confidence regions for smooth functions of the mean, Fréchet differentiable statistical functionals and for a class of M-functionals. In this paper, we argue that this use can be extended to the class of functionals which are smooth functions of M-functionals. In particular, we find the conditions under which the empirical log-likelihood ratio for this kind of functionals admits a χ² approxima tion. Furthermore, we investigate, by simulation methods, the related approximation error in some contexts of practical interest.     

Asymmetric Multivariate Stochastic Volatility

This paper proposes and analyses two types of asymmetric multivariate stochastic volatility (SV) models, namely, (i) the SV with leverage (SV-L) model, which is based on the negative correlation between the innovations in the returns and volatility, and (ii) the SV with leverage and size effect (SV-LSE) model, which is based on the signs and magnitude of the returns. The paper derives the state space form for the logarithm of the squared returns, which follow the multivariate SV-L model, and develops estimation methods for the multivariate SV-L and SV-LSE models based on the Monte Carlo likelihood (MCL) approach. The empirical results show that the multivariate SV-LSE model fits the bivariate and trivariate returns of the S&P 500, the Nikkei 225, and the Hang Seng indexes with respect to AIC and BIC more accurately than does the multivariate SV-L model. Moreover, the empirical results suggest that the univariate models should be rejected in favor of their bivariate and trivariate counterparts.