Understanding the Deviance Information Criterion for SEM: Cautions in Prior Specification

Logit, probit and complementary log-log models are the most widely used models when binary dependent variables are available. Conventionally, these models have been frequentists. This paper aims to demonstrate how such models can be implemented relatively quickly and easily from a Bayesian framework using Gibbs sampling Markov chain Monte Carlo simulation methods in WinBUGS. We focus on the modeling and prediction of Down syndrome (DS) and Mental retardation (MR) data from an observational study at Kuwait Medical Genetic Center over a 30-year time period between 1979 and 2009. Modeling algorithms were used in two distinct ways; firstly, using three different methods at the disease level, including logistic, probit and cloglog models, and, secondly, using bivariate logistic regression to study the association between the two diseases in question. The models are compared in terms of their predictive ability via R2, adjusted R2, root mean square error (RMSE) and Bayesian Deviance Information Criterion (DIC). In the univariate analysis, the logistic model performed best, with R2 (0.1145), adjusted R2 (0.114), RMSE (0.3074) and DIC (7435.98) for DS, and R2 (0.0626), adjusted R2 (0.0621), RMSE (0.4676) and DIC (23120) for MR. In the bivariate case, results revealed that 7 and 8 out of the 10 selected covariates were significantly associated with DS and MR respectively, whilst none were associated with the interaction between the two outcomes. Bayesian methods are more flexible in handling complex non-standard models as well as they allow model fit and complexity to be assessed straightforwardly for non-nested hierarchical models.

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PSXV-9 Transgenerational epigenetic variance for production and reproduction traits in maternal-line pigs

Journal of Animal Science ◽

10.1093/jas/skab235.472 ◽

2021 ◽

Vol 99 (Supplement_3) ◽

pp. 258-259

Author(s):

Jason R Graham ◽

Jay S Johnson ◽

Andre C Araujo ◽

Jeremy T Howard ◽

Luiz F Brito

Keyword(s):

Additive Genetic Variance ◽

Phenotypic Expression ◽

Deviance Information Criterion ◽

Information Criterion ◽

Pedigree Information ◽

Relationship Matrix ◽

Maternal Line ◽

Genetic Heritability ◽

Reproduction Traits ◽

Search Approach

Abstract Modeling epigenetic factors impacting phenotypic expression of economically important traits has become a hot-topic in the field of animal breeding due to the variability in genetic expression caused by environmental stressors (e.g., heat stress). This variability may be due, in part, to in-utero epigenomic remodeling, which has been reported to be passed from parent to offspring. We aimed to estimate transgenerational epigenetic variance for various production and reproduction traits measured in a maternal-line pig population, using a Bayesian approach. The phenotypes for production [n = 10,862; i.e., weaning weight (WW), birth weight (BW) and ultrasound-backfat thickness (BF)] and reproduction [n = 5,235, i.e., number of piglets born alive (NBA) and total number of piglets born (TB)] traits from a purebred Landrace population were provided by Smithfield Premium Genetics (NC, USA). The pedigree information traced back to 10 generations. Single-trait genetic analyses were performed using mixed models that included additive genetic, common environmental, and epigenetic random effects. The Gibbs sampler algorithm based on Markov chain Monte Carlo was used to estimate the variance components. The epigenetic relationship matrix was constructed using a recursive parameter (λ) related to the transmissibility coefficient of epigenetic markers. A grid search approach was used to define the optimal λ value (λ values ranged from 0.1 to 0.5, with an interval of 0.1). The optimal λ value was determined based on the deviance information criterion, and it was used to estimate the additive and epigenetic variances. For instance, based on preliminary results, the optimal λ value estimated for TB was 0.3 with an additive genetic variance of 0.94 (0.19 PSD) and epigenetic variance of 0.67 (0.18 PSD). The additive genetic heritability was 0.076 (0.015 PSD) and the estimated epigenetic heritability was 0.053 (0.015 PSD). This preliminary result suggests that epigenetics contribute to the non-Mendelian variability in pigs.

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Utilização da modelagem inteiramente bayesiana na detecção de padrões de variação de risco relativo de mortalidade infantil no Rio Grande do Sul, Brasil

Cadernos de Saúde Pública ◽

10.1590/s0102-311x2009000700008 ◽

2009 ◽

Vol 25 (7) ◽

pp. 1501-1510 ◽

Cited By ~ 6

Author(s):

Sérgio Kakuta Kato ◽

Diego de Matos Vieira ◽

Jandyra Maria Guimarães Fachel

Keyword(s):

Deviance Information Criterion ◽

Rio Grande ◽

Information Criterion ◽

Rio Grande Do Sul ◽

Standardised Mortality Ratio ◽

Mortality Ratio

Neste artigo são analisados os fatores possivelmente associados à mortalidade infantil nos 496 municípios do Rio Grande do Sul, Brasil, com base em dados acumuladas entre os anos de 2001 a 2004, obtidos pela análise de regressão utilizando modelagem inteiramente bayesiana como alternativa para superar a autocorrelação espacial e a instabilidade dos estimadores clássicos, como a taxa bruta e a SMR (Standardised Mortality Ratio). Foram comparadas diferentes especificações de componente espacial e covariáveis, provenientes dos blocos do Índice de Desenvolvimento Sócio-econômico da Fundação de Economia e Estatística (IDESE/FEE-2003). Verificou-se que o modelo que utiliza a estrutura espacial além da covariável educação apresenta melhor desempenho, quando comparado pelo critério DIC (Deviance Information Criterion). Comparando as estimativas das SMR com os riscos relativos obtidos pela modelagem inteiramente bayesiana, foi possível observar um ganho substancial na interpretação e na detecção de padrões de variação do risco de mortalidade infantil nos municípios do Rio Grande do Sul ao utilizar essa modelagem. A região da Serra Gaúcha destacou-se com baixo risco relativo e estimativas muito homogêneas.

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Understanding historical summer flounder (Paralichthys dentatus) abundance patterns through the incorporation of oceanography-dependent vital rates in Bayesian hierarchical models

Canadian Journal of Fisheries and Aquatic Sciences ◽

10.1139/cjfas-2018-0092 ◽

2019 ◽

Vol 76 (8) ◽

pp. 1275-1294 ◽

Cited By ~ 3

Author(s):

Cecilia A. O’Leary ◽

Timothy J. Miller ◽

James T. Thorson ◽

Janet A. Nye

Keyword(s):

Population Dynamics ◽

Gulf Stream ◽

Stock Assessment ◽

Deviance Information Criterion ◽

Information Criterion ◽

Natural Mortality ◽

Vital Rates ◽

Summer Flounder ◽

Paralichthys Dentatus ◽

Abundance Patterns

Climate can impact fish population dynamics through changes in productivity and shifts in distribution, and both responses have been observed for many fish species. However, few studies have incorporated climate into population dynamics or stock assessment models. This study aimed to uncover how past variations in population vital rates and fishing pressure account for observed abundance variation in summer flounder (Paralichthys dentatus). The influences of the Gulf Stream Index, an index of climate variability in the Northwest Atlantic, on abundance were explored through natural mortality and stock–recruitment relationships in age-structured hierarchical Bayesian models. Posterior predictive loss and deviance information criterion indicated that out of tested models, the best estimates of summer flounder abundances resulted from the climate-dependent natural mortality model that included log-quadratic responses to the Gulf Stream Index. This climate-linked population model demonstrates the role of climate responses in observed abundance patterns and emphasizes the complexities of environmental effects on populations beyond simple correlations. This approach highlights the importance of modeling the combined effect of fishing and climate simultaneously to understand population dynamics.

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Deviance information criterion (DIC) in Bayesian multiple QTL mapping

Computational Statistics & Data Analysis ◽

10.1016/j.csda.2008.01.016 ◽

2009 ◽

Vol 53 (5) ◽

pp. 1850-1860 ◽

Cited By ~ 10

Author(s):

Daniel Shriner ◽

Nengjun Yi

Keyword(s):

Qtl Mapping ◽

Deviance Information Criterion ◽

Information Criterion ◽

Multiple Qtl ◽

Multiple Qtl Mapping

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Research on RMB Exchange Rate Volatility Risk Based on MSGARCH-VaR Model

Discrete Dynamics in Nature and Society ◽

10.1155/2020/8719574 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Xiaofei Wu ◽

Shuzhen Zhu ◽

Junjie Zhou

Keyword(s):

Exchange Rate ◽

Value At Risk ◽

Financial Risk ◽

Deviance Information Criterion ◽

Exchange Rate Volatility ◽

Information Criterion ◽

Model Parameters ◽

Volatility Risk ◽

Rmb Exchange Rate ◽

Trade Friction

This paper captures the RMB exchange rate volatility using the Markov-switching GARCH (MSGARCH) models and traditional single-regime GARCH models. Through the Markov Chain Monte Carlo (MCMC) method, the model parameters are estimated to study the volatility dynamics of the RMB exchange rate. Furthermore, we compare the MSGARCH models to the single-regime GARCH specifications in terms of Value-at-Risk (VaR) prediction accuracy. According to the Deviance information criterion method, the research shows that MSGARCH models outperform the single-regime specifications in capturing the complexity of RMB exchange rate volatility. After the RMB exchange rate reform in 2015, the volatility is more asymmetric and persistent, and the probability of being in the turbulent volatility regime is significantly increased. The continuous escalation of Sino-US trade friction has increased the VaR of RMB exchange rate log-returns. From the evaluation results of the actual over expected exceedance ratio (AE), the conditional coverage (CC) test, and the dynamic quantile (DQ) test, we find strong evidence that two-regime MSGARCH models could forecast VaR more accurately, which provides practical value for China’s foreign exchange management authorities to manage the financial risk.

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Investigating agreement between different data sources using Bayesian state-space models: an application to estimating NE Atlantic mackerel catch and stock abundance

ICES Journal of Marine Science ◽

10.1093/icesjms/fsq013 ◽

2010 ◽

Vol 67 (6) ◽

pp. 1138-1153 ◽

Cited By ~ 14

Author(s):

E. John Simmonds ◽

Enrique Portilla ◽

Dankert Skagen ◽

Doug Beare ◽

Dave G. Reid

Keyword(s):

State Space ◽

Deviance Information Criterion ◽

Information Criterion ◽

State Space Models ◽

Data Sources ◽

Ne Atlantic ◽

Atlantic Mackerel ◽

Spawning Stock ◽

Over Time ◽

Stock Abundance

Abstract Simmonds, E. J., Portilla, E., Skagen, D., Beare, D., and Reid, D. G. 2010. Investigating agreement between different data sources using Bayesian state-space models: an application to estimating NE Atlantic mackerel catch and stock abundance. – ICES Journal of Marine Science, 67: 1138–1153. Bayesian Markov chain Monte Carlo methods are ideally suited to analyses of situations where there are a variety of data sources, particularly where the uncertainties differ markedly among the data and the estimated parameters can be correlated. The example of Northeast Atlantic (NEA) mackerel is used to evaluate the agreement between available data from egg surveys, tagging, and catch-at-age using multiple models within the Bayesian framework WINBUGS. The errors in each source of information are dealt with independently, and there is extensive exploration of potential sources of uncertainty in both the data and the model. Model options include variation by age and over time of both selectivity in the fishery and natural mortality, varying the precision and calculation method for spawning-stock biomass derived from an egg survey, and the extent of missing catches varying over time. The models are compared through deviance information criterion and Bayesian posterior predictive p-values. To reconcile mortality estimated from the different datasets the landings and discards reported would have to have been between 1.7 and 3.6 times higher than the recorded catches.

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