MOSFET中1/f噪声的Hooge参数α_H

本文报道 P 沟道 MOSFET 器件中 1/f 噪声 Hooge 参数测量的部分实验结果.由加利福尼亚州的 SILICONIX 公司提供的实验样品是采用同样工艺作在同一块基片之上的.实验表明,在由非谐和状态到谐和状态过渡时,α_H 与沟道长度的平方成正比.类似的结果未曾见报道.     

Estimation of parameters in survey sampling: Optimality

Some recent works on estimation in survey sampling are analyzed and extended from the perspective of the theory of optimal estimating functions.     

Inference about permutation parameter in large samples

The estimation problem of a permutation parameter on the basis of a random sample of increasing size is considered. A necessary and sufficient condition for the existence of an estimator, asymptotically fully efficient for two different distributions families, is derived. We also study the application of this result to cyclic groups of order two and three.     

关于普赖斯曲线方程中参数估计方法的改进

罗式胜在“关于普赖斯曲线方程中参数的讨论”一文中提出了估计普赖斯曲线方程中参数的两个方法,但具体计算中存在缺乏量化标准的缺陷。利用概率统计中的2 σ准则给出一个确定参数的改进方法,引入去除异常数据的量化标准,即使其更具有合理性和可操作性。     

Some characterizations of distributions of the exponential-type

We consider a particular subclass of the two-parameter exponential family with natural parameters γ₁, γ₂ and characterize those distributions of the family having a ratio of the mean value and the variance that is a linear function of γ₁ by the form of the moment generating function. As special cases we find the normal and the gamma distributions.     

Test for Parameter Change in Linear Processes Based on Whittle's Estimator

In this article, we develop a cusum test for testing for parameter changes in linear processes based on Whittle's estimator. It is shown that under regularity conditions, the test statistic converges to the sup of a Brownian bridge. The result is particularly useful in handling the change point test in stationary ARMA processes. A simulation result is provided for illustration.     

Invariance Theorems for Fisher Information

In multivariate and multi-parameter contexts, new expressions for Fisher Information are derived using the copula representation of the joint distribution of random variables. Invariance of Fisher Information to margins of the joint distribution is then demonstrated.     

Dynamic Copula-Based Markov Time Series

This article examines a test procedure for checking the constancy of serial dependence via copulas for Markov time series data. It also provides a copula-based modeling approach for the dynamic serial dependence. Various parametric families of copulas offering different dependent structures are investigated. A score test is proposed for checking the constancy of a copula parameter. The score test is constructed and its asymptotic null distribution established under a two-stage estimation procedure. The test does not require specification of the probability distribution for the copula parameter. To capture the dynamics of dependence structure over time, autoregressive moving average and exponential type models are proposed. Illustrations are given based on simulated data and historic coffee prices data.     

Robust Confidence Intervals for the Bernoulli Parameter

Despite the simplicity of the Bernoulli process, developing good confidence interval procedures for its parameter—the probability of success p—is deceptively difficult. The binary data yield a discrete number of successes from a discrete number of trials, n. This discreteness results in actual coverage probabilities that oscillate with the n for fixed values of p (and with p for fixed n). Moreover, this oscillation necessitates a large sample size to guarantee a good coverage probability when p is close to 0 or 1.

It is well known that the Wilson procedure is superior to many existing procedures because it is less sensitive to p than any other procedures, therefore it is less costly. The procedures proposed in this article work as well as the Wilson procedure when 0.1 ≤p ≤ 0.9, and are even less sensitive (i.e., more robust) than the Wilson procedure when p is close to 0 or 1. Specifically, when the nominal coverage probability is 0.95, the Wilson procedure requires a sample size 1, 021 to guarantee that the coverage probabilities stay above 0.92 for any 0.001 ≤ min {p, 1 ?p} <0.01. By contrast, our procedures guarantee the same coverage probabilities but only need a sample size 177 without increasing either the expected interval width or the standard deviation of the interval width.     

On Unimodal Regression in the Exponential Family

In this article, we discuss statistical methods for curve-estimation under the assumption of unimodality for variables with distributions belonging to the two-parameter exponential family with known or constant dispersion parameter. An important special case is a one-parameter distribution. We suggest a nonparametric method based on monotonicity properties. The method is applied to Swedish data on laboratory verified diagnoses of influenza and data on inflation from an episode of hyperinflation in Bulgaria.