1,870 research outputs found
On large-sample estimation and testing via quadratic inference functions for correlated data
Hansen (1982) proposed a class of "generalized method of moments" (GMMs) for
estimating a vector of regression parameters from a set of score functions.
Hansen established that, under certain regularity conditions, the estimator
based on the GMMs is consistent, asymptotically normal and asymptotically
efficient. In the generalized estimating equation framework, extending the
principle of the GMMs to implicitly estimate the underlying correlation
structure leads to a "quadratic inference function" (QIF) for the analysis of
correlated data. The main objectives of this research are to (1) formulate an
appropriate estimated covariance matrix for the set of extended score functions
defining the inference functions; (2) develop a unified large-sample
theoretical framework for the QIF; (3) derive a generalization of the QIF test
statistic for a general linear hypothesis problem involving correlated data
while establishing the asymptotic distribution of the test statistic under the
null and local alternative hypotheses; (4) propose an iteratively reweighted
generalized least squares algorithm for inference in the QIF framework; and (5)
investigate the effect of basis matrices, defining the set of extended score
functions, on the size and power of the QIF test through Monte Carlo simulated
experiments.Comment: 32 pages, 2 figure
Designs for generalized linear models with random block effects via information matrix approximations
The selection of optimal designs for generalized linear mixed models is complicated by the fact that the Fisher information matrix, on which most optimality criteria depend, is computationally expensive to evaluate. Our focus is on the design of experiments for likelihood estimation of parameters in the conditional model. We provide two novel approximations that substantially reduce the computational cost of evaluating the information matrix by complete enumeration of response outcomes, or Monte Carlo approximations thereof: (i) an asymptotic approximation which is accurate when there is strong dependence between observations in the same block; (ii) an approximation via Kriging interpolators. For logistic random intercept models, we show how interpolation can be especially effective for finding pseudo-Bayesian designs that incorporate uncertainty in the values of the model parameters. The new results are used to provide the first evaluation of the efficiency, for estimating conditional models, of optimal designs from closed-form approximations to the information matrix derived from marginal models. It is found that correcting for the marginal attenuation of parameters in binary-response models yields much improved designs, typically with very high efficiencies. However, in some experiments exhibiting strong dependence, designs for marginal models may still be inefficient for conditional modelling. Our asymptotic results provide some theoretical insights into why such inefficiencies occur
Modeling Binary Time Series Using Gaussian Processes with Application to Predicting Sleep States
Motivated by the problem of predicting sleep states, we develop a mixed
effects model for binary time series with a stochastic component represented by
a Gaussian process. The fixed component captures the effects of covariates on
the binary-valued response. The Gaussian process captures the residual
variations in the binary response that are not explained by covariates and past
realizations. We develop a frequentist modeling framework that provides
efficient inference and more accurate predictions. Results demonstrate the
advantages of improved prediction rates over existing approaches such as
logistic regression, generalized additive mixed model, models for ordinal data,
gradient boosting, decision tree and random forest. Using our proposed model,
we show that previous sleep state and heart rates are significant predictors
for future sleep states. Simulation studies also show that our proposed method
is promising and robust. To handle computational complexity, we utilize Laplace
approximation, golden section search and successive parabolic interpolation.
With this paper, we also submit an R-package (HIBITS) that implements the
proposed procedure.Comment: Journal of Classification (2018
Robust Inference for Generalized Linear Mixed Models: An Approach Based on Score Sign Flipping
Despite the versatility of generalized linear mixed models in handling
complex experimental designs, they often suffer from misspecification and
convergence problems. This makes inference on the values of coefficients
problematic. To address these challenges, we propose a robust extension of the
score-based statistical test using sign-flipping transformations. Our approach
efficiently handles within-variance structure and heteroscedasticity, ensuring
accurate regression coefficient testing. The approach is illustrated by
analyzing the reduction of health issues over time for newly adopted children.
The model is characterized by a binomial response with unbalanced frequencies
and several categorical and continuous predictors. The proposed approach
efficiently deals with critical problems related to longitudinal nonlinear
models, surpassing common statistical approaches such as generalized estimating
equations and generalized linear mixed models
A review of R-packages for random-intercept probit regression in small clusters
Generalized Linear Mixed Models (GLMMs) are widely used to model clustered categorical outcomes. To tackle the intractable integration over the random effects distributions, several approximation approaches have been developed for likelihood-based inference. As these seldom yield satisfactory results when analyzing binary outcomes from small clusters, estimation within the Structural Equation Modeling (SEM) framework is proposed as an alternative. We compare the performance of R-packages for random-intercept probit regression relying on: the Laplace approximation, adaptive Gaussian quadrature (AGQ), Penalized Quasi-Likelihood (PQL), an MCMC-implementation, and integrated nested Laplace approximation within the GLMM-framework, and a robust diagonally weighted least squares estimation within the SEM-framework. In terms of bias for the fixed and random effect estimators, SEM usually performs best for cluster size two, while AGQ prevails in terms of precision (mainly because of SEM's robust standard errors). As the cluster size increases, however, AGQ becomes the best choice for both bias and precision
Bayesian Generalized Linear Mixed Effects Models Using Normal-Independent Distributions: Formulation and Applications
A standard assumption is that the random effects of Generalized Linear Mixed Effects Models (GLMMs) follow the normal distribution. However, this assumption has been found to be quite unrealistic and sometimes too restrictive as revealed in many real-life situations. A common case of departures from normality includes the presence of outliers leading to heavy-tailed distributed random effects. This work, therefore, aims to develop a robust GLMM framework by replacing the normality assumption on the random effects by the distributions belonging to the Normal-Independent (NI) class. The resulting models are called the Normal-Independent GLMM (NI-GLMM). The four special cases of the NI class considered in these modelsâ formulations include the normal, Student-t, Slash and contaminated normal distributions. A full Bayesian technique was adopted for estimation and inference. A real-life data set on cotton bolls was used to demonstrate the performance of the proposed NI-GLMM methodology
- âŚ