Estimating the Coefficient of Cross-Validity in Multiple Regression: A Comparison of Analytical and Empirical Methods

In predictive applications of multiple regression, interest centers on the estimation of the population coefficient of cross-validation rather than the population multiple correlation. The accuracy of 3 analytical formulas for shrinkage estimation (Ezekiel, Browne, & Darlington) and 4 empirical techniques (simple cross-validation, multicross-validation, jackknife, and bootstrap) were investigated in a Monte Carlo study. Random samples of size 20 to 200 were drawn from a pseudopopulation of actual field data. Regression models were investigated with population coefficients of determination ranging from .04 to .50 and with numbers of regressors ranging from 2 to 10. For all techniques except the Browne formula and multicross-validation, substantial statistical bias was evident when the shrunken R ² values were used to estimate the coefficient of cross-validation. In addition, none of the techniques examined provided unbiased estimates with sample sizes smaller than 100, regardless of the number of regressors.     

Monte Carlo Confidence Intervals for Complex Functions of Indirect Effects

One challenge in mediation analysis is to generate a confidence interval (CI) with high coverage and power that maintains a nominal significance level for any well-defined function of indirect and direct effects in the general context of structural equation modeling (SEM). This study discusses a proposed Monte Carlo extension that finds the CIs for any well-defined function of the coefficients of SEM such as the product of k coefficients and the ratio of the contrasts of indirect effects, using the Monte Carlo method. Finally, we conduct a small-scale simulation study to compare CIs produced by the Monte Carlo, nonparametric bootstrap, and asymptotic-delta methods. Based on our simulation study, we recommend researchers use the Monte Carlo method to test a complex function of indirect effects.     

Reexamining the Impact of Nonnormality in Two-Group Comparison Procedures

The authors performed a Monte Carlo simulation to empirically investigate the robustness and power of 4 methods in testing mean differences for 2 independent groups under conditions in which 2 populations may not demonstrate the same pattern of nonnormality. The approaches considered were the t test, Wilcoxon rank-sum test, Welch-James test with trimmed means and Winsorized variances, and a nonparametric bootstrap test. Results showed that the Wilcoxon rank-sum test and Welch-James test with trimmed means and Winsorized variances were not robust in terms of type I error control when the 2 populations showed different patterns of nonnormality. The nonparametric bootstrap test provided power advantages over the t test. The authors discuss other results from the simulation study and provide recommendations.     

A Bootstrap Generalization of Modified Parallel Analysis for IRT Dimensionality Assessment

This article introduces a bootstrap generalization to the Modified Parallel Analysis (MPA) method of test dimensionality assessment using factor analysis. This methodology, based on the use of Marginal Maximum Likelihood nonlinear factor analysis, provides for the calculation of a test statistic based on a parametric bootstrap using the MPA methodology for generation of synthetic datasets. Performance of the bootstrap test was compared with the likelihood ratio difference test and the DIMTEST procedure using a Monte Carlo simulation. The bootstrap test was found to exhibit much better control of the Type I error rate than the likelihood ratio difference test, and comparable power to DIMTEST under most conditions. A major conclusion to be taken from this research is that under many real-world conditions, the bootstrap MPA test presents a useful alternative for practitioners using Marginal Maximum Likelihood factor analysis to test for multidimensional testing data.     

Application of a new resampling method to SEM: a comparison of S-SMART with the bootstrap

Among the commonly used resampling methods of dealing with small-sample problems, the bootstrap enjoys the widest applications because it often outperforms its counterparts. However, the bootstrap still has limitations when its operations are contemplated. Therefore, the purpose of this study is to examine an alternative, new resampling method (called S-SMART) and compare the statistical performance of it with that of the bootstrap through an application of them to the most advanced modelling technique, SEM, as an example. The evaluation of the statistical performances of S-SMART and the bootstrap with respect to the standard errors of the parameter estimates was conducted through a Monte Carlo simulation study. This work, while potentially benefiting educational and behavioural research, conceivably would also provide methodological support for other research areas, such as bioinformatics, biology, geosciences, astronomy, and ecology, where large samples are hard to obtain.     

Improving the Root Mean Square Error of Approximation for Nonnormal Conditions in Structural Equation Modeling

In this study, the authors investigated incorporating adjusted model fit information into the root mean square error of approximation (RMSEA) fit index. Through Monte Carlo simulation, the usefulness of this adjusted index was evaluated for assessing model adequacy in structural equation modeling when the multivariate normality assumption underlying maximum likelihood estimation is violated. Adjustment to the RMSEA was considered in 2 forms: a rescaling adjustment via the Satorra-Bentler rescaled goodness-of-fit statistic and a bootstrap adjustment via the Bollen and Stine adjusted model p value. Both properly specified and misspecifed models were examined. The adjusted RMSEA was evaluated in terms of the average index value across study conditions and with respect to model rejection rates under tests of exact fit, close fit, and not-close fit.     

Identifying Unobserved Hazard Functions in Discrete-Time Survival Mixture Analysis

This Monte Carlo simulation adds to the growing body of enumeration index performance research in continuous response variable mixture models by addressing the issue of the performance of these indexes in discrete-time survival mixture analysis (DTSMA) models. Results showed that although all enumeration indexes performed very well in identifying a homogeneous DTSMA model (i.e., k = 1 hazard function in the sample data), the findings also showed that the enumeration indexes performed poorly in identifying the correct number of unobserved hazard functions present in a heterogeneous (i.e., k = 3) DTSMA model. More important, the performance of the enumeration indexes for the heterogeneous DTSMA models did not improve as the sample size, the effect of time-invariant covariates, or adjacent hazard function separation distance increased, which is inconsistent with some previous Monte Carlo simulation results. The limitations of this Monte Carlo simulation study and future empirical investigation possibilities are both discussed.     

蒙特卡罗方法在数学建模中的应用

蒙特卡罗方法是一个以概率模型为基础,利用计算机通过多次反复模拟实验完成问题求解的一种数值计算方法。它特别适用于用传统的解析法难以解决甚至是无法解决的问题。文章主要介绍蒙特卡罗方法及基本原理,并通过实例说明蒙特卡罗方法在数学建模中的应用。     

A Comparison of Propensity Score Weighting Methods for Evaluating the Effects of Programs With Multiple Versions

This Monte Carlo simulation study compares methods to estimate the effects of programs with multiple versions when assignment of individuals to program version is not random. These methods use generalized propensity scores, which are predicted probabilities of receiving a particular level of the treatment conditional on covariates, to remove selection bias. The results indicate that inverse probability of treatment weighting (IPTW) removes the most bias, followed by optimal full matching (OFM), and marginal mean weighting through stratification (MMWTS). The study also compared standard error estimation with Taylor series linearization, bootstrapping and the jackknife across propensity score methods. With IPTW, these standard error estimation methods performed adequately, but standard errors estimates were biased in most conditions with OFM and MMWTS.     

用计算机模拟薄膜生长过程的KMC方法

以有表面活性剂介入的Cu薄膜为例,用Kinetic Monte Carlo方法对薄膜生长的随机过程进行了计算机模拟,并对所提出的模型和Kinetic Monte Carlo算法进行了详尽描述．在实现动力学生长过程及物理模型向计算机语言及算法的转化过程中,不仅构建了模拟薄膜生长过程的主体流程图,并且将主体流程图分解成了原子吸附事件流程图、原子迁移事件过程流程图、“小事件”列表刷新流程图和事件列表刷新示意图四个部分,分别对其进行说明和阐述．可以看出在薄膜领域中,计算机模拟在理论和实验这两方面的研究中都发挥了重要作用,Kinetic Monte Carlo方法也已经成了解决薄膜生长相关问题的必要工具．     

Monte Carlo模拟在聚合反应速率常数估算中的运用

1 Introduction It is i mportant to study polymerization mechanisminpolymerization engineering. Obtaining kinetic data isthe key step for establishing polymerization mechanismand understanding elementary reactions . The kineticdata contains two groups of data ,i.e.,the rate andreaction order .The rate ,especially the rate constant ,is i mportant and to be esti mated assuming that the or-der of the reactionis known.Furthermore ,esti mationof rate constants for polymerization is an active anddif…     

A Monte Carlo Model of Light Propagation in Nontransparent Tissue

Opticalcoherencetomography (OCT)wasorigi nallydevelopedforimagingintransparenttissue[1] .Since1 995 ,OCThasbeenadvancedtoimagefromophthal mologytonontransparenttissuewithahighimagingres olution[2 ,3 ] .OCTdemonstratesitsgreatestpotentialinsituationswhereconventionalbiopsyiseitherdangerousorineffective[4] .Becauseofthecomplexityofthelightpropagationinnontransparenttissue,theapplicationofOCTinhighlyscatteringbiologicaltissuesuchasskintis suearenotoptimistic.Yadlowsky[5] explainedthatmul tip…     

Variational Monte Carlo technique

In this article, I discuss the use of the Variational Monte Carlo technique in the determination of ground state energies of quantum harmonic oscillators in one and three dimensions. In order to provide a better understanding of this technique, a pre-requisite concept of Monte Carlo integration is also discussed along with numerical examples. The technique can be easily extended to study the ground state energies of hydrogen atom, particle in a box, helium atom and other complex microscopic systems involving N particles in d-dimensions.     

Inference and Interval Estimation Methods for Indirect Effects With Latent Variable Models

Although much is known about the performance of recent methods for inference and interval estimation for indirect or mediated effects with observed variables, little is known about their performance in latent variable models. This article presents an extensive Monte Carlo study of 11 different leading or popular methods adapted to structural equation models with latent variables. Manipulated variables included sample size, number of indicators per latent variable, internal consistency per set of indicators, and 16 different path combinations between latent variables. Results indicate that some popular or previously recommended methods, such as the bias-corrected bootstrap and asymptotic standard errors had poorly calibrated Type I error and coverage rates in some conditions. Likelihood-based confidence intervals, the distribution of the product method, and the percentile bootstrap emerged as leading methods for both interval estimation and inference, whereas joint significance tests and the partial posterior method performed well for inference.     

基于故障树的飞机氧气系统可靠性仿真研究

飞机氧气系统是飞机的重要组成部分,其可靠性的高低对飞行安全至关重要,使用故障树方法对其进行了定性分析,在此基础上用蒙特卡洛仿真方法进行了仿真计算,得出氧气系统的可靠度。结果表明,把蒙特卡洛用于飞机氧气系统的故障树分析是切实有效的。     

Mediated Effects with the Parallel Process Latent Growth Model: An Evaluation of Methods for Testing Mediation in the Presence of Nonnormal Data

This Monte Carlo simulation study investigated the impact of nonnormality on estimating and testing mediated effects with the parallel process latent growth model and 3 popular methods for testing the mediated effect (i.e., Sobel’s test, the asymmetric confidence limits, and the bias-corrected bootstrap). It was found that nonnormality had little effect on the estimates of the mediated effect, standard errors, empirical Type I error, and power rates in most conditions. In terms of empirical Type I error and power rates, the bias-corrected bootstrap performed best. Sobel’s test produced very conservative Type I error rates when the estimated mediated effect and standard error had a relationship, but when the relationship was weak or did not exist, the Type I error was closer to the nominal .05 value.     

架空直立式码头桩基可靠度分析

以三峡库区某架空直立式码头为例,采用基于有限元软件ANSYS的Monte Carlo法,对架空直立式码头桩基进行了灵敏度分析、散点图分析,并讨论了桩基在各种工况组合作用下的可靠指标.结果表明船舶撞击力对码头桩基的可靠度计算起着决定性作用,码头桩基可靠度指标基本满足规范要求,可靠指标计算方法和结果可为类似码头桩径优化设计提供参考.     

Unequal Covariate Group Means and the Analysis of Covariance

The study, using a Monte Carlo technique, was designed to investigate the effect of the differences in covariate means among the treatment groups on the significance level and the power of the F-test of the analysis of covariance. The results show that the covariate group means differences have little effect on the significance level if the covariate is highly correlated with the criterion variable. However, if the correlation is .4 or less, larger sample sizes are required. The effect on the power is more sensitive for smaller experiments. The larger the differences among covariate group means, the lower the actual power becomes compared to the approximate theoretical power.     

蒙特卡罗方法及其在辐射剂量计算中的应用

概述了蒙特卡罗方法的产生与发展，阐述了蒙特卡罗方法的基本特点，最后就蒙特卡罗方法在辐射剂量计算上的应用进行了讨论。