自相关过程能力指数置信区间的构建与评价

过程能力指数是指过程处于控制状态下的实际加工能力,其计算方法通常要求过程是独立同分布的,而实际过程中数据往往表现出一定的自相关性。评价过程控制的能力指数是一个基于样本观测值的统计量,需要对其进行统计检验。当过程自相关时,还需要对其进行统计推断。本文提出了自相关下过程能力指数置信区间的构建方法,并从理论和模拟两个角度研究自相关对该置信区间的影响,给出了自相关情形下置信区间的计算步骤。模拟和实证均表明：正自相关会导致常规方法估计出的置信区间偏大且上移,负自相关会导致置信区间估计偏小且下移,本文提出的方法能很好地解决置信区间估计不准确的问题,能较准确地评价过程的实际生产能力。     

Simultaneous Parameter Estimation in Exploratory Factor Analysis: An Expository Review

The classical exploratory factor analysis (EFA) finds estimates for the factor loadings matrix and the matrix of unique factor variances which give the best fit to the sample correlation matrix with respect to some goodness-of-fit criterion. Common factor scores can be obtained as a function of these estimates and the data. Alternatively to the classical EFA, the EFA model can be fitted directly to the data which yields factor loadings and common factor scores simultaneously. Recently, new algorithms were introduced for the simultaneous least squares estimation of all EFA model unknowns. The new methods are based on the numerical procedure for singular value decomposition of matrices and work equally well when the number of variables exceeds the number of observations. This paper provides an account that is intended as an expository review of methods for simultaneous parameter estimation in EFA. The methods are illustrated on Harman's five socio-economic variables data and a high-dimensional data set from genome research.