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Maximum Likelihood Estimation of Hierarchical Linear Models from Incomplete Data: Random Coefficients, Statistical Interactions, and Measurement Error

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Bibliographic Details
Title: Maximum Likelihood Estimation of Hierarchical Linear Models from Incomplete Data: Random Coefficients, Statistical Interactions, and Measurement Error
Language: English
Authors: Shin, Yongyun, Raudenbush, Stephen W.
Source: Grantee Submission. 2023.
Peer Reviewed: Y
Page Count: 44
Publication Date: 2023
Sponsoring Agency: Institute of Education Sciences (ED)
Contract Number: R305D210022
Document Type: Reports - Research
Descriptors: Maximum Likelihood Statistics, Hierarchical Linear Modeling, Error of Measurement, Statistical Distributions, Inferences, Predictor Variables, Computation, Context Effect, Probability, Family Income, Prediction, Equations (Mathematics), Simulation, Statistical Analysis, Interaction
DOI: 10.1080/10618600.2023.2234414
Abstract: We consider two-level models where a continuous response R and continuous covariates C are assumed missing at random. Inferences based on maximum likelihood or Bayes are routinely made by estimating their joint normal distribution from observed data R[subscript obs] and C[subscript obs]. However, if the model for R given C includes random coefficients, interactions, or polynomial terms, their joint distribution will be nonstandard. We propose a family of unique factorizations involving selected "provisionally known random effects'' u such that h(R[subscript obs], C[subscript obs] |u) is normally distributed and u is a low-dimensional normal random vector; we approximate h(R[subscript obs], C[subscript obs]) =[integral]h(R[subscript obs], C[subscript obs]¦u)g(u)du via adaptive Gauss-Hermite quadrature. For polynomial models, the approximation is exact but, in any case, can be made as accurate as required given sufficient computation time. The model incorporates random effects as explanatory variables, reducing bias due to measurement error. By construction, our factorizations solve problems of compatibility among fully conditional distributions that have arisen in Bayesian imputation based on the Gibbs Sampler. We spell out general rules for selecting u, and show that our factorizations can support fully compatible Bayesian methods of imputation using the Gibbs Sampler. [This paper will be published in "Journal of Computational and Graphical Statistics."]
Abstractor: As Provided
IES Funded: Yes
Entry Date: 2023
Accession Number: ED629560
Database: ERIC
Description
Abstract:We consider two-level models where a continuous response R and continuous covariates C are assumed missing at random. Inferences based on maximum likelihood or Bayes are routinely made by estimating their joint normal distribution from observed data R[subscript obs] and C[subscript obs]. However, if the model for R given C includes random coefficients, interactions, or polynomial terms, their joint distribution will be nonstandard. We propose a family of unique factorizations involving selected "provisionally known random effects'' u such that h(R[subscript obs], C[subscript obs] |u) is normally distributed and u is a low-dimensional normal random vector; we approximate h(R[subscript obs], C[subscript obs]) =[integral]h(R[subscript obs], C[subscript obs]¦u)g(u)du via adaptive Gauss-Hermite quadrature. For polynomial models, the approximation is exact but, in any case, can be made as accurate as required given sufficient computation time. The model incorporates random effects as explanatory variables, reducing bias due to measurement error. By construction, our factorizations solve problems of compatibility among fully conditional distributions that have arisen in Bayesian imputation based on the Gibbs Sampler. We spell out general rules for selecting u, and show that our factorizations can support fully compatible Bayesian methods of imputation using the Gibbs Sampler. [This paper will be published in "Journal of Computational and Graphical Statistics."]
DOI:10.1080/10618600.2023.2234414