Equivalence of parametric identifiability and estimability

Given that estimators are monotone functions of observations, parametric identiflability is shown to be both necessary and sufficient for estimability.     

Nonparametric recursive estimation in stationary markov processes

This paper deals with a class of recursive kernel estimators of the transition probability density function t(y|x) of a stationary Markov process. A sufficient condition for such estimators to be weakly and strongly 2 consistent for almost all (x,y)∈R² is given. Further an L, convergence result is obtained. No continuity conditions are imposed on t(y|x).     

Use of Dummy Variables in Testing for Equality between Sets of Coefficients in Two Linear Regressions: A Note

Elementary inductive proofs are presented for the binomial approximation to the hypergeometric distribution, the density of an order statistic, and the distribution of when X ₁, ···, X _n are a sample from N (μ, 1).     

试析“一号文件”对解决我国“三农”问题的深远影响

从1982年到2005年中共中央为解决我国"三农"问题,先后发出七个"一号文件".这七个"一号文件"是新时期中国共产党对我国"三农"问题探索的最新成果,提出了新世纪解决我国"三农"问题的战略构想,对我国"三农"问题的解决产生深远的影响.     

校园一卡通平台下财务管理模式研究

校园一卡通的推广应用,可以有效地提高财务管理效率,规范财务管理制度。本文通过对校园一卡通平台的简单介绍,探讨该平台下的财务管理模式及其新特点,充分肯定了该平台对提升财务管理水平的积极作用,并对会计人员提出了新的要求。     

Robustness of measures of common cause sigma in presence of data correlation

Process monitoring in the presence of data correlation is one of the most discussed issues in statistical process control literature over the past decade. However, the attention to retrospective analysis in the presence of data correlation with various common cause sigma estimators is lacking in the literature. Maragah et al. (1992), in an early paper on the retrospective analysis in presence of data correlation, addresses only a single common cause sigma estimator. This paper studies the effect of data correlation on retrospective X-chart with various common cause sigma estimates in stable period of AR(1) Process. This study is carried out with the aim of identifying suitable standard deviation statistic/statistics which is/are robust to the data correlation. This paper also discusses the robustness of common cause sigma estimates for monitoring the data following other time series models, namely ARMA(1,1) and AR(p). Further, the bias characteristics of robust standard deviation estimates have been discussed for the above time-series models. This paper further studies the performance of retrospective X-chart on forecast residuals from various forecasting methods of AR(1) process. The above studies were carried out through simulating the stable period of AR(1), AR(2), stable and invertible period of ARMA(1,1) processes. The average number of false alarms have been considered as a measure of performance. The results of simulation studies have been discussed.     

Test sizes and the importance of finding global maxima of the likelihood function

This paper demonstrates the importance of taking care to find a global maximum when estimating nuisance parameters required for hypothesis tests. We find that sizes can be badly inflated when this is not done for a test of the general linear regression model.     

Improved estimation for Poisson INAR(1) models

We consider the first-order Poisson autoregressive model proposed by McKenzie [Some simple models for discrete variate time series. Water Resour Bull. 1985;21:645–650] and Al-Osh and Alzaid [First-order integer valued autoregressive (INAR(1)) process. J Time Ser Anal. 1987;8:261–275], which may be suitable in situations where the time series data are non-negative and integer valued. We derive the second-order bias of the squared difference estimator [Weiß. Process capability analysis for serially dependent processes of Poisson counts. J Stat Comput Simul. 2012;82:383–404] for one of the parameters and show that this bias can be used to define a bias-reduced estimator. The behaviour of a modified conditional least-squares estimator is also studied. Furthermore, we access the asymptotic properties of the estimators here discussed. We present numerical evidence, based upon Monte Carlo simulation studies, showing that the here proposed bias-adjusted estimator outperforms the other estimators in small samples. We also present an application to a real data set.