Search Results
Source: Journal of Computational and Graphical Statistics
Resulting in 2 citations.
| 1. |
Westling, T. McCormick, T.H. |
|
Beyond Prediction: A Framework for Inference with Variational Approximations in Mixture Models Journal of Computational and Graphical Statistics published online (1 May 2019): DOI: 10.1080/10618600.2019.1609977. Also: https://www.tandfonline.com/doi/full/10.1080/10618600.2019.1609977 Cohort(s): NLSY97 Publisher: American Statistical Association Keyword(s): Drug Use; Modeling, MIxture Models/Finite Mixture Models Variational inference is a popular method for estimating model parameters and conditional distributions in hierarchical and mixed models, which arise frequently in many settings in the health, social, and biological sciences. Variational inference in a frequentist context works by approximating intractable conditional distributions with a tractable family and optimizing the resulting lower bound on the log-likelihood. The variational objective function is typically less computationally intensive to optimize than the true likelihood, enabling scientists to fit rich models even with extremely large datasets. Despite widespread use, little is known about the general theoretical properties of estimators arising from variational approximations to the log-likelihood, which hinders their use in inferential statistics. In this paper we connect such estimators to profile M-estimation, which enables us to provide regularity conditions for consistency and asymptotic normality of variational estimators. Our theory also motivates three methodological improvements to variational inference: estimation of the asymptotic model-robust covariance matrix, a one-step correction that improves estimator efficiency, and an empirical assessment of consistency. We evaluate the proposed results using simulation studies and data on marijuana use from the National Longitudinal Study of Youth |
|
|
Bibliography Citation
Westling, T. and T.H. McCormick. "Beyond Prediction: A Framework for Inference with Variational Approximations in Mixture Models." Journal of Computational and Graphical Statistics published online (1 May 2019): DOI: 10.1080/10618600.2019.1609977.
|
| 2. |
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] Copyright of Journal of Computational & Graphical Statistics is the property of American Statistical Association 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
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.
|