1,721,016 research outputs found
Improving the accuracy of likelihood-based inference in meta-analysis and meta-regression
Full text linkRandom-effects models are frequently used to synthesise information from different studies in meta-analysis. While likelihood-based inference is attractive both in terms of limiting properties and of implementation, its application in random-effects meta-analysis may result in misleading conclusions, especially when the number of studies is small to moderate. The current paper shows how methodology that reduces the asymptotic bias of the maximum likelihood estimator of the variance component can also substantially improve inference about the mean effect size. The results are derived for the more general framework of random-effects meta-regression, which allows the mean effect size to vary with study-specific covariates
Marginal Beta Regression for Time Series Analysis
No full textA marginal beta regression model with autoregressive and moving average errors is developed for the analysis of time series of values in the standard unit interval (0,1), such as proportions and rates. The dependence structure is conveniently related to the marginal model through a Gaussian copula specification. Likelihood inference, model validation via residual analysis, and prediction are briefly discussed. The methodology is applied to the time series of the rate of hidden unemployment in S ̃ao Paulo, Brazil
Pairwise Likelihood Inference for General State Space Models
No full textThis article concerns parameter estimation for general state space models, following a frequentist likelihood-based approach. Since exact methods for computing and maximizing the likelihood function are usually not feasible, approximate solutions, based on Monte Carlo or numerical methods, have to be considered. Here, we concentrate on a different approach based on a simple pseudolikelihood, called “pairwise likelihood.” Its merit is to reduce the computational burden so that it is possible to fit highly structured statistical models, even when the use of standard likelihood methods is not possible. We discuss pairwise likelihood inference for state space models, and we present some touchstone examples concerning autoregressive models with additive observation noise and switching regimes, the local level model and a non-Makovian generalization of the dynamic Tobit model.Composite likelihood, Efficiency, Pairwise likelihood, Pseudolikelihood, Regime switching, State space model, Tobit model,
Pairwise Likelihood Inference for Ordinal Categorical Time Series.
No full textOrdinal categorical time series may be analyzed as censored observations from a suitable latent stochastic process, which describes the underlying evolution of the system. This approach may be considered as an alternative to Markov chain models or to regression methods for categorical time series data. The problem of parameter estimation is solved through a simple pseudolikelihood, called pairwise likelihood. This inferential methodology is successfully applied to the class of autoregressive ordered probit models. Potential usefulness for inference and model selection within more general classes of models are also emphasized. Illustrations include simulation studies and two simple real data applications
A pairwise likelihood approach to generalized linear models with crossed random effects
No full textInference in generalized linear models with crossed effects is often made cumbersome by the high-dimensional intractable integrals involved in the likelihood function. We propose an inferential strategy based on the pairwise likelihood, which only requires the computation of bivariate distributions. The benefits of our approach are the simplicity of implementation and the potential to handle large data sets. The estimators based on the pairwise likelihood are generally consistent and asymptotically normally distributed. The pairwise likelihood makes it possible to improve on standard inferential procedures by means of bootstrap methods. The performance of the proposed methodology is illustrated by simulations and application to the well-known salamander mating data set
metaLik: Likelihood inference in meta-analysis and meta-regression models
No full textFirst- and higher-order likelihood inference in meta-analysis and meta-regression models
On the assessment of regulators' efficiency. An application to European telecommunications
No full textConsistent and Scalable Composite Likelihood Estimation of Probit Models with Crossed Random Effects
No full textEstimation of crossed random effects models commonly requires computational costs that grow faster than linearly in the sample size N, often as fast as Ω(N 3/2), making them unsuitable for large data sets. For non-Gaussian responses, integrating out the random effects to get a marginal likelihood brings significant challenges, especially for high dimensional integrals where the Laplace approximation might not be accurate. We develop a composite likelihood approach to probit models that replaces the crossed random effects model with some hierarchical models that require only one-dimensional integrals. We show how to consistently estimate the crossed effects model parameters from the hierarchical model fits.We find that the computation scales linearly in the sample size. We illustrate the method on about five million observations from Stitch Fix where the crossed effects formulation would require an integral of dimension larger than 700000
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