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Source: Computational Statistics & Data Analysis

Resulting in 5 citations.

1.	Cai, Jing-Heng Song, Xin-Yuan Lam, Kwok-Hap Ip, Edward Hak-Sing
1.	A Mixture of Generalized Latent Variable Models for Mixed Mode and Heterogeneous Data Computational Statistics and Data Analysis 55,11 (November 2011): 2889-2907. Also: http://www.sciencedirect.com/science/article/pii/S0167947311001770 Cohort(s): Children of the NLSY79, NLSY79 Publisher: Elsevier Keyword(s): Alcohol Use; Bayesian; Behavior Problems Index (BPI); Child Self-Administered Supplement (CSAS); Modeling; Modeling, Growth Curve/Latent Trajectory Analysis; Modeling, Mixed Effects; Monte Carlo; Peabody Individual Achievement Test (PIAT- Math); Peabody Individual Achievement Test (PIAT- Reading) In the behavioral, biomedical, and social-psychological sciences, mixed data types such as continuous, ordinal, count, and nominal are common. Subpopulations also often exist and contribute to heterogeneity in the data. In this paper, we propose a mixture of generalized latent variable models (GLVMs) to handle mixed types of heterogeneous data. Different link functions are specified to model data of multiple types. A Bayesian approach, together with the Markov chain Monte Carlo (MCMC) method, is used to conduct the analysis. A modified DIC is used for model selection of mixture components in the GLVMs. A simulation study shows that our proposed methodology performs satisfactorily. An application of mixture GLVM to a data set from the National Longitudinal Surveys of Youth (NLSY) is presented. [Copyright Elsevier] Copyright of Computational Statistics & Data Analysis is the property of Elsevier Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.
	Bibliography Citation Cai, Jing-Heng, Xin-Yuan Song, Kwok-Hap Lam and Edward Hak-Sing Ip. "A Mixture of Generalized Latent Variable Models for Mixed Mode and Heterogeneous Data." Computational Statistics and Data Analysis 55,11 (November 2011): 2889-2907.

2.	Chang, Hsiu-Ching Chung, Hwan
2.	Dealing with Multiple Local Modalities in Latent Class Profile Analysis Computational Statistics and Data Analysis 68 (December 2013): 296-310. Also: http://www.sciencedirect.com/science/article/pii/S0167947313002612 Cohort(s): NLSY97 Publisher: Elsevier Keyword(s): Alcohol Use; Modeling, Latent Class Analysis/Latent Transition Analysis; Monte Carlo Parameters for latent class profile analysis (LCPA) are easily estimated by maximum likelihood via the EM algorithm or Bayesian method via Markov chain Monte Carlo. However, the local maximum problem is a long-standing issue in any hill-climbing optimization technique for the LCPA model. To deal with multiple local modalities, two probabilistic optimization techniques using the deterministic annealing framework are proposed. The deterministic annealing approaches are implemented with an efficient recursive formula in the step for the parameter update. The proposed methods are applied to the data from the National Longitudinal Survey of Youth 1997 (NLSY97), a survey that explores the transition from school to work and from adolescence to adulthood in the United States.
	Bibliography Citation Chang, Hsiu-Ching and Hwan Chung. "Dealing with Multiple Local Modalities in Latent Class Profile Analysis." Computational Statistics and Data Analysis 68 (December 2013): 296-310.

3.	Hoshino, Takahiro
3.	A Bayesian Propensity Score Adjustment for Latent Variable Modeling and MCMC Algorithm Computational Statistics and Data Analysis 52,3 (January 2008): 1413-1429. Also: http://www.sciencedirect.com/science/article/pii/S0167947307001429 Cohort(s): NLSY79 Publisher: Elsevier Keyword(s): Bayesian; Cigarette Use (see Smoking); Markov chain / Markov model; Modeling, Growth Curve/Latent Trajectory Analysis; Monte Carlo; Pregnancy and Pregnancy Outcomes; Propensity Scores; Smoking (see Cigarette Use); Statistical Analysis; Variables, Independent - Covariate The estimation of the differences among groups in observational studies is frequently inaccurate owing to a bias caused by differences in the distributions of covariates. In order to estimate the average treatment effects when the treatment variable is binary, Rosenbaum and Rubin [1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41–55] proposed an adjustment method for pre-treatment variables using propensity scores. Imbens [2000. The role of the propensity score in estimating dose-response functions. Biometrika 87, 706–710] extended the propensity score methodology for estimation of average treatment effects with multivalued treatments. However, these studies focused only on estimating the marginal mean structure. In many substantive sciences such as the biological and social sciences, a general estimation method is required to deal with more complex analyses other than regression, such as testing group differences on latent variables. For latent variable models, the EM algorithm or the traditional Monte Carlo methods are necessary. However, in propensity score adjustment, these methods cannot be used because the full distribution is not specified. In this paper, we propose a quasi-Bayesian estimation method for general parametric models that integrate out the distributions of covariates using propensity scores. Although the proposed Bayes estimates are shown to be consistent, they can be calculated by existing Markov chain Monte Carlo methods such as Gibbs sampler. The proposed method is useful to estimate parameters in latent variable models, while the previous methods were unable to provide valid estimates for complex models such as latent variable models. We also illustrated the procedure using the data obtained from the US National Longitudinal Survey of Children and Youth (NLSY1979–2002) for estimating the effect of maternal smoking during pregnancy on the development ... [Copyright 2008 Elsevier] Copyright of Computational Statistics & Data Analysis is the property of Elsevier Science Publishers B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts)
	Bibliography Citation Hoshino, Takahiro. "A Bayesian Propensity Score Adjustment for Latent Variable Modeling and MCMC Algorithm ." Computational Statistics and Data Analysis 52,3 (January 2008): 1413-1429.

4.	Lu, Zhenqiu Laura Zhang, Zhiyong
4.	Robust Growth Mixture Models with Non-ignorable Missingness: Models, Estimation, Selection, and Application Computational Statistics and Data Analysis 71 (March 2014): 220-240. Also: http://www.sciencedirect.com/science/article/pii/S0167947313002818 Cohort(s): NLSY97 Publisher: Elsevier Keyword(s): Bayesian; Missing Data/Imputation; Modeling; Peabody Individual Achievement Test (PIAT- Math); Statistical Analysis Challenges in the analyses of growth mixture models include missing data, outliers, estimation, and model selection. Four non-ignorable missingness models to recover the information due to missing data, and three robust models to reduce the effect of non-normality are proposed. A full Bayesian method is implemented by means of data augmentation algorithm and Gibbs sampling procedure. Model selection criteria are also proposed in the Bayesian context. Simulation studies are then conducted to evaluate the performances of the models, the Bayesian estimation method, and selection criteria under different situations. The application of the models is demonstrated through the analysis of education data on children’s mathematical ability development. The models can be widely applied to longitudinal analyses in medical, psychological, educational, and social research.
	Bibliography Citation Lu, Zhenqiu Laura and Zhiyong Zhang. "Robust Growth Mixture Models with Non-ignorable Missingness: Models, Estimation, Selection, and Application." Computational Statistics and Data Analysis 71 (March 2014): 220-240.

5.	Rashid, S. Mitra, R. Steele, R. J.
5.	Using Mixtures of t Densities to Make Inferences in the Presence of Missing Data with a Small Number of Multiply Imputed Data Sets Computational Statistics and Data Analysis 92 (December 2015): 84-96. Also: http://www.sciencedirect.com/science/article/pii/S016794731500136X Cohort(s): Children of the NLSY79, NLSY79 Publisher: Elsevier Keyword(s): Breastfeeding; Family Income; Missing Data/Imputation; Peabody Individual Achievement Test (PIAT- Math); Statistical Analysis Strategies for making inference in the presence of missing data after conducting a Multiple Imputation (MI) procedure are considered. An approach which approximates the posterior distribution for parameters using a mixture of t-distributions is proposed. Simulated experiments show this approach improves inferences in some aspects, making them more stable over repeated analysis and creating narrower bounds for certain common statistics of interest. Extensions to the existing literature have been executed that provide further stability to inferences and also a strong potential to identify ways to make the analysis procedure more flexible. The competing methods have been first compared using simulated data sets and then a real data set concerning analysis of the effect of breastfeeding duration on children's cognitive ability. R code to implement the methods used is available as online supplementary material.
	Bibliography Citation Rashid, S., R. Mitra and R. J. Steele. "Using Mixtures of t Densities to Make Inferences in the Presence of Missing Data with a Small Number of Multiply Imputed Data Sets." Computational Statistics and Data Analysis 92 (December 2015): 84-96.

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