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Ke-hai Yuan - One of the best experts on this subject based on the ideXlab platform.

  • Examining Missing Data Mechanisms via homogeneity of parameters, homogeneity of distributions, and multivariate normality
    Wiley Interdisciplinary Reviews: Computational Statistics, 2013
    Co-Authors: Mortaza Jamshidian, Ke-hai Yuan
    Abstract:

    This paper reviews various methods of identifying Missing Data Mechanisms. The three well-known Mechanisms of Missing completely at random (MCAR), Missing at random (MAR), and Missing not at random (MNAR) are considered. A number of tests deem rejection of homogeneity of means and/or covariances (HMC) among observed Data patterns as a means to reject MCAR. Utility of these tests as well as their shortcomings are discussed. In particular, examples of MAR and MNAR Data with homogeneous means and covariances between their observed Data patterns are provided for which tests of HMC fail to reject MCAR. More generally, tests of homogeneity of parameter estimates between various subsets of Data are reviewed and their utility as tests of MCAR and MAR (in special cases) is pointed out. Since many tests of MCAR assume multinormality, methods to assess this assumption in the context of incomplete Data are reviewed. Tests of homogeneity of distributions among observed Data patterns for MCAR are also considered. A new nonparametric test of this type is proposed on the basis of pairwise comparison of marginal distributions. Finally, methods of examining Missing Data Mechanism based on sensitivity analysis including methods that model Missing Data Mechanism based on logistic, probit, and latent variable regression models, as well as methods that do not require modeling of Missing Data Mechanism are reviewed. The paper concludes with some practical comments about the validity and utility of tests of Missing Data Mechanism. © 2013 Wiley Periodicals, Inc. How to cite this article:

  • Data driven sensitivity analysis to detect Missing Data Mechanism with applications to structural equation modelling
    Journal of Statistical Computation and Simulation, 2013
    Co-Authors: Mortaza Jamshidian, Ke-hai Yuan
    Abstract:

    Missing Data are a common problem in almost all areas of empirical research. Ignoring the Missing Data Mechanism, especially when Data are Missing not at random (MNAR), can result in biased and/or inefficient inference. Because MNAR Mechanism is not verifiable based on the observed Data, sensitivity analysis is often used to assess it. Current sensitivity analysis methods primarily assume a model for the response Mechanism in conjunction with a measurement model and examine sensitivity to Missing Data Mechanism via the parameters of the response model. Recently, Jamshidian and Mata (Post-modelling sensitivity analysis to detect the effect of Missing Data Mechanism, Multivariate Behav. Res. 43 (2008), pp. 432–452) introduced a new method of sensitivity analysis that does not require the difficult task of modelling the Missing Data Mechanism. In this method, a single measurement model is fitted to all of the Data and to a sub-sample of the Data. Discrepancy in the parameter estimates obtained from the the t...

  • sem with Missing Data and unknown population distributions using two stage ml theory and its application
    Multivariate Behavioral Research, 2008
    Co-Authors: Ke-hai Yuan, Laura Lu
    Abstract:

    This article provides the theory and application of the 2-stage maximum likelihood (ML) procedure for structural equation modeling (SEM) with Missing Data. The validity of this procedure does not require the assumption of a normally distributed population. When the population is normally distributed and all Missing Data are Missing at random (MAR), the direct ML procedure is nearly optimal for SEM with Missing Data. When Missing Data Mechanisms are unknown, including auxiliary variables in the analysis will make the Missing Data Mechanism more likely to be MAR. It is much easier to include auxiliary variables in the 2-stage ML than in the direct ML. Based on most recent developments for Missing Data with an unknown population distribution, the article first provides the least technical material on why the normal distribution-based ML generates consistent parameter estimates when the Missing Data Mechanism is MAR. The article also provides sufficient conditions for the 2-stage ML to be a valid statistical ...

  • SEM with Missing Data and Unknown Population Distributions Using Two-Stage ML: Theory and Its Application.
    Multivariate behavioral research, 2008
    Co-Authors: Ke-hai Yuan
    Abstract:

    This article provides the theory and application of the 2-stage maximum likelihood (ML) procedure for structural equation modeling (SEM) with Missing Data. The validity of this procedure does not require the assumption of a normally distributed population. When the population is normally distributed and all Missing Data are Missing at random (MAR), the direct ML procedure is nearly optimal for SEM with Missing Data. When Missing Data Mechanisms are unknown, including auxiliary variables in the analysis will make the Missing Data Mechanism more likely to be MAR. It is much easier to include auxiliary variables in the 2-stage ML than in the direct ML. Based on most recent developments for Missing Data with an unknown population distribution, the article first provides the least technical material on why the normal distribution-based ML generates consistent parameter estimates when the Missing Data Mechanism is MAR. The article also provides sufficient conditions for the 2-stage ML to be a valid statistical procedure in the general case. For the application of the 2-stage ML, an SAS IML program is given to perform the first-stage analysis and EQS codes are provided to perform the second-stage analysis. An example with open- and closed-book examination Data is used to illustrate the application of the provided programs. One aim is for quantitative graduate students/applied psychometricians to understand the technical details for Missing Data analysis. Another aim is for applied researchers to use the method properly.

Laura Lu - One of the best experts on this subject based on the ideXlab platform.

  • sem with Missing Data and unknown population distributions using two stage ml theory and its application
    Multivariate Behavioral Research, 2008
    Co-Authors: Ke-hai Yuan, Laura Lu
    Abstract:

    This article provides the theory and application of the 2-stage maximum likelihood (ML) procedure for structural equation modeling (SEM) with Missing Data. The validity of this procedure does not require the assumption of a normally distributed population. When the population is normally distributed and all Missing Data are Missing at random (MAR), the direct ML procedure is nearly optimal for SEM with Missing Data. When Missing Data Mechanisms are unknown, including auxiliary variables in the analysis will make the Missing Data Mechanism more likely to be MAR. It is much easier to include auxiliary variables in the 2-stage ML than in the direct ML. Based on most recent developments for Missing Data with an unknown population distribution, the article first provides the least technical material on why the normal distribution-based ML generates consistent parameter estimates when the Missing Data Mechanism is MAR. The article also provides sufficient conditions for the 2-stage ML to be a valid statistical ...

Donald Hedeker - One of the best experts on this subject based on the ideXlab platform.

  • Binary variable multiple-model multiple imputation to address Missing Data Mechanism uncertainty: Application to a smoking cessation trial
    Statistics in Medicine, 2014
    Co-Authors: Juned Siddique, Ofer Harel, Catherine M. Crespi, Donald Hedeker
    Abstract:

    The true Missing Data Mechanism is never known in practice. We present a method for generating multiple imputations for binary variables, which formally incorporates Missing Data Mechanism uncertainty. Imputations are generated from a distribution of imputation models rather than a single model, with the distribution reflecting subjective notions of Missing Data Mechanism uncertainty. Parameter estimates and standard errors are obtained using rules for nested multiple imputation. Using simulation, we investigate the impact of Missing Data Mechanism uncertainty on post-imputation inferences and show that incorporating this uncertainty can increase the coverage of parameter estimates. We apply our method to a longitudinal smoking cessation trial where nonignorably Missing Data were a concern. Our method provides a simple approach for formalizing subjective notions regarding nonresponse and can be implemented using existing imputation software. Copyright © 2014 John Wiley & Sons, Ltd.

  • Binary variable multiple-model multiple imputation to address Missing Data Mechanism uncertainty: Application to a smoking cessation trial
    Statistics in medicine, 2014
    Co-Authors: Juned Siddique, Ofer Harel, Catherine M. Crespi, Donald Hedeker
    Abstract:

    The true Missing Data Mechanism is never known in practice. We present a method for generating multiple imputations for binary variables, which formally incorporates Missing Data Mechanism uncertainty. Imputations are generated from a distribution of imputation models rather than a single model, with the distribution reflecting subjective notions of Missing Data Mechanism uncertainty. Parameter estimates and standard errors are obtained using rules for nested multiple imputation. Using simulation, we investigate the impact of Missing Data Mechanism uncertainty on post-imputation inferences and show that incorporating this uncertainty can increase the coverage of parameter estimates. We apply our method to a longitudinal smoking cessation trial where nonignorably Missing Data were a concern. Our method provides a simple approach for formalizing subjective notions regarding nonresponse and can be implemented using existing imputation software.

Cornelis A.w. Glas - One of the best experts on this subject based on the ideXlab platform.

  • Modelling non‐ignorable Missing‐Data Mechanisms with item response theory models
    British Journal of Mathematical and Statistical Psychology, 2005
    Co-Authors: Rebecca Holman, Cornelis A.w. Glas
    Abstract:

    A model-based procedure for assessing the extent to which Missing Data can be ignored and handling non-ignorable Missing Data is presented. The procedure is based on item response theory modelling. As an example, the approach is worked out in detail in conjunction with item response Data modelled using the partial credit and generalized partial credit models. Simulation studies are carried out to assess the extent to which the bias caused by ignoring the Missing-Data Mechanism can be reduced. Finally, the feasibility of the procedure is demonstrated using Data from a study to calibrate a medical disability scale.

  • Modelling non-ignorable Missing-Data Mechanisms with item response theory models.
    The British journal of mathematical and statistical psychology, 2005
    Co-Authors: Rebecca Holman, Cornelis A.w. Glas
    Abstract:

    A model-based procedure for assessing the extent to which Missing Data can be ignored and handling non-ignorable Missing Data is presented. The procedure is based on item response theory modelling. As an example, the approach is worked out in detail in conjunction with item response Data modelled using the partial credit and generalized partial credit models. Simulation studies are carried out to assess the extent to which the bias caused by ignoring the Missing-Data Mechanism can be reduced. Finally, the feasibility of the procedure is demonstrated using Data from a study to calibrate a medical disability scale.

S.r. De Morais - One of the best experts on this subject based on the ideXlab platform.

  • A conservative feature subset selection algorithm with Missing Data
    Neurocomputing, 2009
    Co-Authors: Alexandre Aussem, S.r. De Morais
    Abstract:

    This paper introduces a novel conservative feature subset selection method with incomplete Data sets. The method is conservative in the sense that it selects the minimal subset of features that renders the rest of the features independent of the target (the class variable) without making any assumption about the Missing Data Mechanism. This is achieved in the context of determining the Markov blanket of the target that reflects the worst-case assumption about the Missing Data Mechanism, including the case when Data are not Missing at random. An application of the method on synthetic and real-world incomplete Data is carried out to illustrate its practical relevance. The method is compared against state-of-the-art approaches such as the expectation-maximization (EM) algorithm and the available case technique.

  • ICDM - A Conservative Feature Subset Selection Algorithm with Missing Data
    2008 Eighth IEEE International Conference on Data Mining, 2008
    Co-Authors: Alexandre Aussem, S.r. De Morais
    Abstract:

    This paper introduces a novel conservative feature subset selection method with incomplete Data sets. The method is conservative in the sense that it selects the minimal subset of features that renders the rest of the features independent of the target (the class variable) without making any assumption about the Missing Data Mechanism. This is achieved in the context of determining the Markov blanket of the target that reflects the worst-case assumption about the Missing Data Mechanism, including the case when Data is not Missing at random. An application of the method on synthetic incomplete Data is carried out to illustrate its practical relevance. The method is compared against state-of-the-art approaches such as the expectation maximization (EM) algorithm and the available case technique.