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Author: Zhang, Qiang
Resulting in 4 citations.
1. Ip, Edward Hak-Sing
Jones, Alison Snow
Zhang, Qiang
Rijmen, Frank
Mixed-effects Hidden Markov Model
Working Paper, Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest University School of Medicine, September 21, 2007.
Also: http://www.phs.wfubmc.edu/public/downloads/MHMM_Ip.pdf
Cohort(s): Children of the NLSY79
Publisher: Wake Forest University School of Medicine
Keyword(s): Behavior Problems Index (BPI); Home Observation for Measurement of Environment (HOME); Markov chain / Markov model; Modeling, Mixed Effects; Peabody Individual Achievement Test (PIAT- Math); Peabody Individual Achievement Test (PIAT- Reading)

Permission to reprint the abstract has not been received from the publisher.

In this paper, we develop a method - the Mixed-effects Hidden Markov Model (MHMM) - for analyzing multiple outcomes in a longitudinal context and for examining the covariates impact on HMM parameters. MHMM embeds a Generalized Linear Mixed Model (GLMM) into a HMM structure, and treat any one of the three sets of HMM parameters, i.e. prior probabilities, transition probabilities and conditional probabilities, as predicted variables. We present the overall likelihood function and its simplified forms, and estimate the parameters through an EM algorithm. The convergence of the algorithm and the model identifiability is also briefly discussed.MHMM is applied to a sample of young children drawn from the National Longitudinal Survey of Youth (NLSY).
Bibliography Citation
Ip, Edward Hak-Sing, Alison Snow Jones, Qiang Zhang and Frank Rijmen. "Mixed-effects Hidden Markov Model." Working Paper, Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest University School of Medicine, September 21, 2007.
2. Ip, Edward Hak-Sing
Jones, Alison Snow
Zhang, Qiang
Rijmen, Frank
Temporal Configuration Analysis of Developmental Trajectories in Young Children of Heavy Episodic Drinking Mothers
Working Paper, Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest University School of Medicine, 2006.
Also: http://www.phs.wfubmc.edu/public/downloads/NLSY_Ip.pdf
Cohort(s): Children of the NLSY79
Publisher: Wake Forest University School of Medicine
Keyword(s): Alcohol Use; Behavior Problems Index (BPI); Home Observation for Measurement of Environment (HOME); Markov chain / Markov model; Modeling, Mixed Effects; Mothers, Behavior; Mothers, Health; Peabody Individual Achievement Test (PIAT- Math); Peabody Individual Achievement Test (PIAT- Reading); Statistical Analysis

Permission to reprint the abstract has not been received from the publisher.

Most models for longitudinal analysis focus on a single outcome variable. In this paper, we develop a method for examining patterns of evolution of cognitive and behavioral profiles of children of heavy episodic drinking (HED) mothers. A developmental profile contains multiple outcome variables. Our objectives are to delineate clusters of outcome trajectories and to study the effect of maternal HED on these trajectories. Several analytical methods, including the functional principal component analysis and the K-mean algorithm, are adapted to achieve this aim. The proposed method, which we call Temporal Configuration Analysis (TCA), is applied to a sample of young children drawn from the National Longitudinal Survey of Youth (NLSY). While most of our results are consistent with previous findings, we demonstrate how the method can lead to nuanced yet important differences in developmental trajectories for children of HED mothers.
Bibliography Citation
Ip, Edward Hak-Sing, Alison Snow Jones, Qiang Zhang and Frank Rijmen. "Temporal Configuration Analysis of Developmental Trajectories in Young Children of Heavy Episodic Drinking Mothers." Working Paper, Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest University School of Medicine, 2006.
3. Zhang, Qiang
Ip, Edward Hak-Sing
Generalized Linear Model for Partially Ordered Data
Statistics in Medicine 31,1 (13 January 2012): 56-68.
Also: http://onlinelibrary.wiley.com/doi/10.1002/sim.4318/abstract
Cohort(s): NLSY97
Publisher: Wiley Online
Keyword(s): Cigarette Use (see Smoking); Modeling; Smoking (see Cigarette Use)

Permission to reprint the abstract has not been received from the publisher.

Within the rich literature on generalized linear models, substantial efforts have been devoted to models for categorical responses that are either completely ordered or completely unordered. Few studies have focused on the analysis of partially ordered outcomes, which arise in practically every area of study, including medicine, the social sciences, and education. To fill this gap, we propose a new class of generalized linear models—the partitioned conditional model—that includes models for both ordinal and unordered categorical data as special cases. We discuss the specification of the partitioned conditional model and its estimation. We use an application of the method to a sample of the National Longitudinal Study of Youth to illustrate how the new method is able to extract from partially ordered data useful information about smoking youths that is not possible using traditional methods. © 2011 John Wiley & Sons, Ltd.
Bibliography Citation
Zhang, Qiang and Edward Hak-Sing Ip. "Generalized Linear Model for Partially Ordered Data." Statistics in Medicine 31,1 (13 January 2012): 56-68.
4. Zhang, Qiang
Jones, Alison Snow
Rijmen, Frank
Ip, Edward Hak-Sing
Multivariate Discrete Hidden Markov Models for Domain-Based Measurements and Assessment of Risk Factors in Child Development
Journal of Computational and Graphical Statistics 19,3 (September 2010): 746-765.
Also: http://pubs.amstat.org/doi/abs/10.1198/jcgs.2010.09015
Cohort(s): Children of the NLSY79, NLSY79
Publisher: American Statistical Association
Keyword(s): Alcohol Use; Behavior Problems Index (BPI); Child Development; Cognitive Ability; Home Observation for Measurement of Environment (HOME); Markov chain / Markov model; Modeling, Mixed Effects; Modeling, Random Effects; Peabody Individual Achievement Test (PIAT- Math); Peabody Individual Achievement Test (PIAT- Reading)

Many studies in the social and behavioral sciences involve multivariate discrete measurements, which are often characterized by the presence of an underlying individual trait, the existence of clusters such as domains of measurements, and the availability of multiple waves of cohort data. Motivated by an application in child development, we propose a class of extended multivariate discrete hidden Markov models for analyzing domain-based measurements of cognition and behavior. A random effects model is used to capture the long-term trait. Additionally, we develop a model selection criterion based on the Bayes factor for the extended hidden Markov model. The National Longitudinal Survey of Youth (NLSY) is used to illustrate the methods. Supplementary technical details and computer codes are available online. [ABSTRACT FROM AUTHOR]

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Bibliography Citation
Zhang, Qiang, Alison Snow Jones, Frank Rijmen and Edward Hak-Sing Ip. "Multivariate Discrete Hidden Markov Models for Domain-Based Measurements and Assessment of Risk Factors in Child Development." Journal of Computational and Graphical Statistics 19,3 (September 2010): 746-765.