Results 11  20
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44
Hypothesis testing and model selection via posterior simulation
 In: Gilks, W.R., Richardson, S., SpiegelHalter, D.J. (Eds.), Markov Chain Monte Carlo in Practice. Chapman
, 1996
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The TETRAD Project: Constraint Based Aids to Causal Model Specification
 MULTIVARIATE BEHAVIORAL RESEARCH
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Model Selection for Generalized Linear Models via GLIB, with Application to Epidemiology
, 1993
"... Epidemiological studies for assessing risk factors often use logistic regression, loglinear models, or other generalized linear models. They involve many decisions, including the choice and coding of risk factors and control variables. It is common practice to select independent variables using a s ..."
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Cited by 15 (5 self)
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Epidemiological studies for assessing risk factors often use logistic regression, loglinear models, or other generalized linear models. They involve many decisions, including the choice and coding of risk factors and control variables. It is common practice to select independent variables using a series of significance tests and to choose the way variables are coded somewhat arbitrarily. The overall properties of such a procedure are not well understood, and conditioning on a single model ignores model uncertainty, leading to underestimation of uncertainty about quantities of interest (QUOIs). We describe a Bayesian modeling strategy that formalizes the model selection process and propagates model uncertainty through to inference about QUOIs. Each possible combination of modeling decisions defines a different model, and the models are compared using Bayes factors. Inference about a QUOI is based on an average of its posterior distributions under the individual models, weighted by thei...
Bayesian model averaging in EEG/MEG imaging
 Neuroimage
, 2004
"... In this paper, the Bayesian Theory is used to formulate the Inverse Problem (IP) of the EEG/MEG. This formulation offers a comparison framework for the wide range of inverse methods available and allows us to address the problem of model uncertainty that arises when dealing with different solutions ..."
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Cited by 13 (1 self)
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In this paper, the Bayesian Theory is used to formulate the Inverse Problem (IP) of the EEG/MEG. This formulation offers a comparison framework for the wide range of inverse methods available and allows us to address the problem of model uncertainty that arises when dealing with different solutions for a single data. In this case, each model is defined by the set of assumptions of the inverse method used, as well as by the functional dependence between the data and the Primary Current Density (PCD) inside the brain. The key point is that the Bayesian Theory not only provides for posterior estimates of the parameters of interest (the PCD) for a given model, but also gives the possibility of finding posterior expected utilities unconditional on the models assumed. In the present work, this is achieved by considering a third level of inference that has been systematically omitted by previous Bayesian formulations of the IP. This level is known as Bayesian model averaging (BMA). The new approach is illustrated in the case of considering different anatomical constraints for solving the IP of the EEG in the frequency domain. This methodology allows us to address two of the main problems that affect linear inverse solutions (LIS): (a) the existence of ghost sources and (b) the tendency to underestimate deep activity. Both simulated and real experimental data are used to demonstrate the capabilities of the BMA approach, and some of the results are compared with the solutions obtained using the popular lowresolution electromagnetic tomography (LORETA) and its anatomically constraint version (cLORETA).
Heuristic Greedy Search Algorithms for Latent Variable Models
, 1997
"... this paper we will describe how to extend search algorithms developed for nonlatent variable DAG models to the case of DAG models with latent variables. We will introduce two generalizations of DAGs, called mixed ancestor graphs (or MAGs) and partial ancestor graphs (or PAGs), and briefly describe ..."
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Cited by 9 (1 self)
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this paper we will describe how to extend search algorithms developed for nonlatent variable DAG models to the case of DAG models with latent variables. We will introduce two generalizations of DAGs, called mixed ancestor graphs (or MAGs) and partial ancestor graphs (or PAGs), and briefly describe how they can be used to search for latent variable DAG models, to classify, and to predict the effects of interventions in causal systems.
Factorial composition of selfrated schizotypal traits among young males undergoing military training
 Schizophrenia Bulletin
, 2004
"... The aim of this study within the Athens Study of Psychosis Proneness and Incidence of Schizophrenia (ASPIS) was the examination of the latent structure of schizotypal dimensions among a large population of young male conscripts in the Greek Air Force during their first week of military training. Con ..."
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Cited by 7 (0 self)
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The aim of this study within the Athens Study of Psychosis Proneness and Incidence of Schizophrenia (ASPIS) was the examination of the latent structure of schizotypal dimensions among a large population of young male conscripts in the Greek Air Force during their first week of military training. Confirmatory factor analysis (CFA) was conducted on 1,355 reliable responders to the selfrated Schizotypal Personality Questionnaire (SPQ), which covers all nine aspects of DSMIIIR schizotypal personality disorder (SPD). A fourfactor model (cognitive/perceptual, paranoid, negative, and disorganization schizotypal dimensions) provided a better fit to the data than did other competing models (one, two, three, four, and fivefactor models). This result is in agreement with recent findings supporting the notion of a multidimensional construct of the schizotypy and related schizophrenia phenotype.
Understanding the relationship between communication and political knowledge: A modelcomparison approach using panel data
 Political Communication
, 2005
"... The purpose of this study was to examine more closely the assumptions of causality in research on communication and political knowledge. Although most communication theory suggests that communication causes learning, some have argued for the reverse causal direction or reciprocal causality. Others h ..."
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Cited by 6 (2 self)
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The purpose of this study was to examine more closely the assumptions of causality in research on communication and political knowledge. Although most communication theory suggests that communication causes learning, some have argued for the reverse causal direction or reciprocal causality. Others have confounded these concepts—in conjunction with political interest—in measures of political “sophistication” or “expertise. ” We collected panel data (N = 1,109) on a national sample in June and November 2000. We employed a model comparison approach to identify the best fitting model among alternatives that included models of unidirectional and reciprocal causality in both lagged and synchronous models, controlling for prior political interest and various demographic factors. The data are most consistent with a model of causality that is unidirectional running from Time 2 measures of news use and political discussion to Time 2 political knowledge.
Bayes Factors and BIC: Comment on Weakliem
, 1998
"... Weakliem agrees that Bayes factors are useful for model selection and hypothesis testing. He reminds us that the simple and convenient BIC approximation corresponds most closely to one particular prior on the parameter space, the unit information prior, and points out that researchers may have diffe ..."
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Cited by 6 (0 self)
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Weakliem agrees that Bayes factors are useful for model selection and hypothesis testing. He reminds us that the simple and convenient BIC approximation corresponds most closely to one particular prior on the parameter space, the unit information prior, and points out that researchers may have different prior information or opinions. Clearly a prior that represents the available information should be used, although the unit information prior often seems reasonable in the absence of strong prior information. It seems that, among the Bayes factors likely to be used in practice, BIC is conservative in the sense of tending to provide less evidence for additional parameters or "effects". Thus if a Bayes factor based on additional prior information favors an effect, but BIC does not, the prior information is playing a crucial role and this should be made clear when the research is reported. BIC may well have a role as a baseline reference analysis to be provided in routine reporting of research results, perhaps along with Bayes factors based on other priors. In Weakliem's 2 x 2 table examples, BIC and Bayes factors based on Weakliem's preferred priors lead to similar substantive conclusions, but both differ from those based on P values. When there is additional prior information, the technology now exists to express it as
Marital Conflict and Conduct Problems in Children of Twins
"... The ChildrenofTwins design was used to test whether associations between marital conflict frequency and conduct problems can be replicated within the children of discordant twin pairs. A sample of 2,051 children (age 14 – 39 years) of 1,045 twins was used to estimate the genetic and environmental ..."
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Cited by 4 (2 self)
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The ChildrenofTwins design was used to test whether associations between marital conflict frequency and conduct problems can be replicated within the children of discordant twin pairs. A sample of 2,051 children (age 14 – 39 years) of 1,045 twins was used to estimate the genetic and environmental influences on marital conflict and determine whether genetic or environmental selection processes underlie the observed association between marital conflict and conduct problems. Results indicate that genetic and nonshared environmental factors influence the risk of marital conflict. Furthermore, genetic influences mediated the association between marital conflict frequency and conduct problems. These results highlight the need for quasiexperimental designs in investigations of intergenerational associations. Marital Conflict and Conduct Problems in Children of Twins Marital conflict is a robust predictor of children’s psychological adjustment, particularly symptoms of conduct disorder and other forms of externalizing
Identification and likelihood inference for recursive linear models with correlated errors
, 2007
"... In recursive linear models, the multivariate normal joint distribution of all variables exhibits a dependence structure induced by recursive systems of linear structural equations. Such models appear in particular in seemingly unrelated regressions, structural equation modelling, simultaneous equati ..."
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Cited by 3 (1 self)
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In recursive linear models, the multivariate normal joint distribution of all variables exhibits a dependence structure induced by recursive systems of linear structural equations. Such models appear in particular in seemingly unrelated regressions, structural equation modelling, simultaneous equation systems, and in Gaussian graphical modelling. We show that recursive linear models that are ‘bowfree’ are wellbehaved statistical models, namely, they are everywhere identifiable and form curved exponential families. Here, ‘bowfree ’ refers to models satisfying the condition that if a variable x occurs in the structural equation for y, then the errors for x and y are uncorrelated. For the computation of maximum likelihood estimates in ‘bowfree ’ recursive linear models we introduce the Residual Iterative Conditional Fitting (RICF) algorithm. Compared to existing algorithms RICF is easily implemented requiring only least squares computations, has clear convergence properties, and finds parameter estimates in closed form whenever possible. 1