喷涂工艺薄膜厚度模型的构建与应用

为研究12寸晶圆切割过程中喷涂工艺设备参数对保护性薄膜厚度的影响,提高设备调整效率和工艺质量,采用均匀设计的实验方法,以薄膜在测试量块上的厚度为目标,对影响厚度的主要因素:涂料压力、微调阀和雾化压力进行研究。对实验数据采用SPSS进行非线性逐步回归得到了薄膜厚度的数学模型,揭示了各因素的交互关系,为测机调整提供数据依据,节省大量反复测机时间,在保证工艺质量的同时提高了喷涂设备的生产效率。该模型已成功运用于某芯片封装测试厂的喷涂设备,并取得了显著效果。     

Fitting Second-Order Finite Mixture Models to Data with Many Censored Values Using Maximum Likelihood Estimation

Finite mixture models, that is, weighted averages of parametric distributions, provide a powerful way to extend parametric families of distributions to fit data sets not adequately fit by a single parametric distribution. First-order finite mixture models have been widely used in the physical, chemical, biological, and social sciences for over 100 years. Using maximum likelihood estimation, we demonstrate how a first-order finite mixture model can represent the large variability in data collected by the U.S. Environmental Protection Agency for the concentration of Radon 222 in drinking water supplied from ground water, even when 28% of the data fall at or below the minimum reporting level. Extending the use of maximum likelihood, we also illustrate how a second-order finite mixture model can separate and represent both the variability and the uncertainty in the data set.     

A recursive algorithm on estimating the parameters in multilevel models subject to the measurement errors on the covariates

Multilevel models are popular models for analysing data inheriting a hierarchical structure. They are used in diverse fields including social, medical, economical and biological sciences. These models encounter some problems in estimating the parameters, if there are measurement errors in either explanatory or response variables. A common approach to tackle this obstacle is to consider the pseudo variables and follow some simulation methods to estimate the parameters. We propose a new algorithm constituting the iterative and simulation extrapolation steps in turn. To evaluate the proposed algorithm, various simulation studies are also conducted. Moreover, we investigate the implementation of our method on a real data set concerning the cost and expenditure of the households in Tehran city in the year 2007.     

Rank estimation of regression coefficients using iterated reweighted least squares

This paper is concerned with the rank estimator for the parameter vector β in a linear model which is obtained by the minimization of a rank dispersion function. The rank estimator has many advantages over the regular least squares estimator, but the inaccessibility of software to carry out its computation has limited its use. An iterated reweighted least squares algorithm is presented for the computation of the rank estimator. The method is simple in concept and can be carried out readily with a wide variety of statistical software. Details of the method are discussed along with some results on its asymptotic distribution and numerical stability. Some examples are presented to show advantages of the rank method.     

Testing for serial correlation in the presence of dynamic heteroscedasticity

Standard serial correlation tests are derived assuming that the disturbances are homoscedastic, but this study shows that asympotic critical values are not accurate when this assumption is violated. Asymptotic critical values for the ARCH(2)-corrected LM, BP and BL tests are valid only when the underlying ARCH process is strictly stationary, whereas Wooldridge's robust LM test has good properties overall. These tests exhibit similar bahaviour even when the underlying process is GARCH (1,1). When the regressors include lagged dependent variables, the rejection frequencies under both the null and alternative hypotheses depend on the coefficientsof the lagged dependent variables and the other model parameters. They appear to be robust across various disturbance distributions under the null hypothesis.     

Estimating the parameters of mixed linear models with modal estimators

This paper presents a procedure to estimate the variance components and fixed effects of mixed linear models. The mode of the joint posterior distribution of all the parameters is obtained by an iterative technique.

The proposed method is illustrated with one-way and two-fold nested random models. Two numerical examples demonstrate the iterative solution.     

Good ridge estimators based on prior information

Ridge regression is re-examined and ridge estimators based on prior information are introduced. A necessary and sufficient condition is given for such ridge estimators to yield estimators of every nonnull linear combination of the regression coefficients with smaller mean square error than that of the Gauss-Markov best linear unbiased estimator.     

A Survey of Microcomputer Survival Analysis Software: The Need for an Integrated Framework

This paper surveys commercially available MS-DOS and Microsoft Windows based microcomputer software for survival analysis, especially for Cox proportional hazards regression and parametric survival models. Emphasis is given to functionality, documentation, generality, and flexibility of software. A discussion of the need for software integration is given, which leads to the conclusion that survival analysis software not closely tied to a well-designed package will not meet an analyst's general needs. Some standalone programs are good tools for teaching the theory of some survival analysis procedures, but they may not teach the student good data analysis techniques such as critically examining regression assumptions. We contrast typical software with a general, integrated, modeling framework that is available with S-PLUS.     

Empirical Likelihood for Threshold Autoregressive Models

This article develops empirical likelihood for threshold autoregressive models. We propose general estimating equations based on moment constraint. Under some suitable conditions, we show the empirical likelihood estimators for parameter are asymptotically normally distributed, and the proposed log empirical likelihood ratio statistic asymptotically follows a standard chi-squared distribution.     

Modelling lifetimes by quantile functions using Parzen's score function

The present paper introduces methods of constructing quantile functions as models of lifetimes with monotone and nonmonotone hazard functions. This is accomplished on the basis of the relationships the hazard quantile function has with the score function introduced by Parzen in connection with the tail heaviness of probability distributions. Three models illustrated here contain several existing models as particular cases. The appropriateness of the models in real situations is also demonstrated.