Carlo Simulation Studies (carlo + simulation_studies)

Distribution by Scientific Domains
Mathematics and Statistics60%

Kinds of Carlo Simulation Studies

  • monte carlo simulation studies
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    Selected Abstracts

    Predictive distributions in risk analysis and estimation for the triangular distribution

    ENVIRONMETRICS, Issue 7 2001
    Yongsung Joo
    Abstract Many Monte Carlo simulation studies have been done in the field of risk analysis. This article demonstrates the importance of using predictive distributions (the estimated distributions of the explanatory variable accounting for uncertainty in point estimation of parameters) in the simulations. We explore different types of predictive distributions for the normal distribution, the lognormal distribution and the triangular distribution. The triangular distribution poses particular problems, and we found that estimation using quantile least squares was preferable to maximum likelihood. Copyright © 2001 John Wiley & Sons, Ltd. [source]

    Identifying the time of polynomial drift in the mean of autocorrelated processes

    QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 5 2010
    Marcus B. Perry
    Abstract Control charts are used to detect changes in a process. Once a change is detected, knowledge of the change point would simplify the search for and identification of the special ause. Consequently, having an estimate of the process change point following a control chart signal would be useful to process engineers. This paper addresses change point estimation for covariance-stationary autocorrelated processes where the mean drifts deterministically with time. For example, the mean of a chemical process might drift linearly over time as a result of a constant pressure leak. The goal of this paper is to derive and evaluate an MLE for the time of polynomial drift in the mean of autocorrelated processes. It is assumed that the behavior in the process mean over time is adequately modeled by the kth-order polynomial trend model. Further, it is assumed that the autocorrelation structure is adequately modeled by the general (stationary and invertible) mixed autoregressive-moving-average model. The estimator is intended to be applied to data obtained following a genuine control chart signal in efforts to help pinpoint the root cause of process change. Application of the estimator is demonstrated using a simulated data set. The performance of the estimator is evaluated through Monte Carlo simulation studies for the k=1 case and across several processes yielding various levels of positive autocorrelation. Results suggest that the proposed estimator provides process engineers with an accurate and useful estimate for the last sample obtained from the unchanged process. Copyright © 2009 John Wiley & Sons, Ltd. [source]

    Testing Hardy-Weinberg Equilibrium using Family Data from Complex Surveys

    ANNALS OF HUMAN GENETICS, Issue 4 2009
    Dewei She
    Summary Genetic data collected during the second phase of the Third National Health and Nutrition Examination Survey (NHANES III) enable us to investigate the association of a wide variety of health factors with regard to genetic variation. The classic question when looking into the genetic variations in a population is whether the population is in the state of Hardy-Weinberg Equilibrium (HWE). Our objective was to develop test procedures using family data from complex surveys such as NHANES III. We developed six Pearson ,2 based tests for a diallelic locus of autosomal genes. The finite sample properties of the proposed test procedures were evaluated via Monte Carlo simulation studies and the Rao-Scott first order corrected test was recommended. Test procedures were applied to three loci from NHANES III genetic databases, i.e., ADRB2, TGFB1, and VDR. HWE was shown to hold at 0.05 level for all three loci when only families with genotypic information available for two parents and for one or more children were used in the analysis. [source]

    Incorporating Correlation for Multivariate Failure Time Data When Cluster Size Is Large

    BIOMETRICS, Issue 2 2010
    L. Xue
    Summary We propose a new estimation method for multivariate failure time data using the quadratic inference function (QIF) approach. The proposed method efficiently incorporates within-cluster correlations. Therefore, it is more efficient than those that ignore within-cluster correlation. Furthermore, the proposed method is easy to implement. Unlike the weighted estimating equations in Cai and Prentice (1995,,Biometrika,82, 151,164), it is not necessary to explicitly estimate the correlation parameters. This simplification is particularly useful in analyzing data with large cluster size where it is difficult to estimate intracluster correlation. Under certain regularity conditions, we show the consistency and asymptotic normality of the proposed QIF estimators. A chi-squared test is also developed for hypothesis testing. We conduct extensive Monte Carlo simulation studies to assess the finite sample performance of the proposed methods. We also illustrate the proposed methods by analyzing primary biliary cirrhosis (PBC) data. [source]

    Functional Mapping of Quantitative Trait Loci Underlying Growth Trajectories Using a Transform-Both-Sides Logistic Model

    BIOMETRICS, Issue 3 2004
    Rongling Wu
    Summary The incorporation of developmental control mechanisms of growth has proven to be a powerful tool in mapping quantitative trait loci (QTL) underlying growth trajectories. A theoretical framework for implementing a QTL mapping strategy with growth laws has been established. This framework can be generalized to an arbitrary number of time points, where growth is measured, and becomes computationally more tractable, when the assumption of variance stationarity is made. In practice, however, this assumption is likely to be violated for age-specific growth traits due to a scale effect. In this article, we present a new statistical model for mapping growth QTL, which also addresses the problem of variance stationarity, by using a transform-both-sides (TBS) model advocated by Carroll and Ruppert (1984, Journal of the American Statistical Association79, 321,328). The TBS-based model for mapping growth QTL cannot only maintain the original biological properties of a growth model, but also can increase the accuracy and precision of parameter estimation and the power to detect a QTL responsible for growth differentiation. Using the TBS-based model, we successfully map a QTL governing growth trajectories to a linkage group in an example of forest trees. The statistical and biological properties of the estimates of this growth QTL position and effect are investigated using Monte Carlo simulation studies. The implications of our model for understanding the genetic architecture of growth are discussed. [source]