'Objective Bayesian analysis for the Student-t regression model'

Fonseca, Thaís C. O.; Ferreira, Marco Antonio Rosa; Migon, Hélio S.

doi:10.1093/biomet/asu001

Cited by 13 publications

(15 citation statements)

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“…2014). A specific Bayesian approach to the use of the Student’s -distribution has been developed (Geweke 1993; Fonseca, Ferreira, and Migon 2008). This strategy has been used to account for heterogeneity in growth models (Zhang et al.…”

Section: Modeling Strategies and Simulation Analysismentioning

confidence: 99%

Exploring the Dynamics of Latent Variable Models

2019

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Researchers face a tradeoff when applying latent variable models to time-series, cross-sectional data. Static models minimize bias but assume data are temporally independent, resulting in a loss of efficiency. Dynamic models explicitly model temporal data structures, but smooth estimates of the latent trait across time, resulting in bias when the latent trait changes rapidly. We address this tradeoff by investigating a new approach for modeling and evaluating latent variable estimates: a robust dynamic model. The robust model is capable of minimizing bias and accommodating volatile changes in the latent trait. Simulations demonstrate that the robust model outperforms other models when the underlying latent trait is subject to rapid change, and is equivalent to the dynamic model in the absence of volatility. We reproduce latent estimates from studies of judicial ideology and democracy. For judicial ideology, the robust model uncovers shocks in judicial voting patterns that were not previously identified in the dynamic model. For democracy, the robust model provides more precise estimates of sudden institutional changes such as the imposition of martial law in the Philippines (1972–1981) and the short-lived Saur Revolution in Afghanistan (1978). Overall, the robust model is a useful alternative to the standard dynamic model for modeling latent traits that change rapidly over time.

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Section: Modeling Strategies and Simulation Analysismentioning

confidence: 99%

Exploring the Dynamics of Latent Variable Models

2019

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“…To complete the Bayesian model, the prior distributions are considered independent and the same as in the Gaussian case. The parameter has a Jeffreys prior distribution as proposed in Fonseca et al (2008) which is detailed in Appendix A1.2.…”

Section: Inferencementioning

confidence: 99%

Bayesian residual analysis for spatially correlated data

Fonseca

2019

Statistical Modelling

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This work considers residual analysis and predictive techniques for the identification of individual and multiple outliers in geostatistical data. The standardized Bayesian spatial residual is proposed and computed for three competing models: the Gaussian, Student-t and Gaussian-log-Gaussian spatial processes. In this context, the spatial models are investigated regarding their plausibility for datasets contaminated with outliers. The posterior probability of an outlying observation is computed based on the standardized residuals and different thresholds for outlier discrimination are tested. From a predictive point of view, methods such as the conditional predictive ordinate, the predictive concordance and the Savage–Dickey density ratio for hypothesis testing are investigated for identification of outliers in the spatial setting. For illustration, contaminated datasets are considered to assess the performance of the three spatial models for identification of outliers in spatial data. Furthermore, an application to wind speed modelling is presented to illustrate the usefulness of the proposed tools to detect regions with large wind speeds.

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“…8,10,19,21 The exponential prior for degrees of freedom parameter may not be an optimal choice. Other priors such as the Jeffrey’s independent prior 23 and objective prior assuming discrete parameter space proposed by Villa and Walker 24 may be preferred. This suggestion may be adopted to conduct sensitivity analysis in examining how the results are influenced by prior distributions.…”

Section: Applicationmentioning

confidence: 99%

Mixed-effects location and scale Tobit joint models for heterogeneous longitudinal data with skewness, detection limits, and measurement errors

Lü

2017

Stat Methods Med Res

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The joint modeling of mean and variance for longitudinal data is an active research area. This type of model has the advantage of accounting for heteroscedasticity commonly observed in between and within subject variations. Most of researches focus on improving the estimating efficiency but ignore many data features frequently encountered in practice. In this article, we develop a mixed-effects location scale joint model that concurrently accounts for longitudinal data with multiple features. Specifically, our joint model handles heterogeneity, skewness, limit of detection, measurement errors in covariates which are typically observed in the collection of longitudinal data from many studies. We employ a Bayesian approach for making inference on the joint model. The proposed model and method are applied to an AIDS study. Simulation studies are performed to assess the performance of the proposed method. Alternative models under different conditions are compared.

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'Objective Bayesian analysis for the Student-t regression model'

Cited by 13 publications

References 1 publication

Exploring the Dynamics of Latent Variable Models

Exploring the Dynamics of Latent Variable Models

Bayesian residual analysis for spatially correlated data

Mixed-effects location and scale Tobit joint models for heterogeneous longitudinal data with skewness, detection limits, and measurement errors

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