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26
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 11 (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...
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.
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 6 (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).
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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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
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 3 (1 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.
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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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
A Bayesian Approach to Financial Model Calibration, Uncertainty Measures and Optimal Hedging
"... Michaelmas 2009This thesis is dedicated to the late ..."
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Michaelmas 2009This thesis is dedicated to the late
Event History Modeling of World Fertility Survey Data
, 1993
"... Event history analysis seems ideally suited for the analysis of World Fertility Survey (WFS) data, which consists of full birth histories and related information. However, it has not been much used for this purpose, and most analyses of WFS data have consisted of tabulations of standard fertility ra ..."
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Event history analysis seems ideally suited for the analysis of World Fertility Survey (WFS) data, which consists of full birth histories and related information. However, it has not been much used for this purpose, and most analyses of WFS data have consisted of tabulations of standard fertility rates, and regressions with children ever born as the dependent variable, both of which have disadvantages. We suggest that this is because event history analysis has practical drawbacks for WFS data, even though, in principle, it provides a superior analytic framework. These are the many partial dates, the computational burden of discretetime event history analysis, the need to take account of five clocks at once (age, period, cohort, time since last event, and parity), and the difficulty of interpreting the coefficients. We propose a modeling strategy for the event history analysis of WFS data which aims to overcome these problems, and we apply it to the previously unanalyzed WFS data from...
ANNALES D’ÉCONOMIE ET DE STATISTIQUE. – N 46 — 1997 Choice Among Hypotheses using Estimation Criteria
"... ABSTRACT. – A rule for choosing among nested models is presented, taking into account that the usual model selection procedure is a sequence of tests, followed by estimation of the parameters that remain in the model. We take a decision theoretical approach and formulate the loss functions and the r ..."
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ABSTRACT. – A rule for choosing among nested models is presented, taking into account that the usual model selection procedure is a sequence of tests, followed by estimation of the parameters that remain in the model. We take a decision theoretical approach and formulate the loss functions and the rules from a Bayesian point of view. The rule resembles a NeymanPearson type test but it takes into consideration the estimation loss across different candidate models and reports an estimate together with an associated loss, taking into account the uncertainty in the selection. The method is compared with other existing procedures and illustrated by examples. Choix de modèles à partir de critères d’estimation RÉSUMÉ. – Nous développons une méthode de sélection de modèles pour des modèles emboités. L’apport de cette méthode est prendre en compte l’usage courant en sélection de modèle, qui est d’appliquer une suite de tests et d’effectuer ensuite l’estimation des paramètres restants. Nous reprenons cette démarche d’un point de vue décisionnel, en introduisant des fonctions de coût adéquates et en définissant les estimateurs de Bayes associés. La règle de décision est similaire à celle de NeymanPearson mais elle prend en compte les coûts respectifs des différents modèles et propose un estimateur en sus d’un modèle, en intégrant l’incertitude liée au choix du modèle. Nous comparons cette nouvelle méthode avec les techniques existantes et l’illustrons sur des exemples standards. * C. GOUTIS; Ch. P. ROBERT: CRESTINSEE. Constantinos Goutis died tragically in a scubadiving accident on July 21, 1996, near Seattle. He was then 33 and a visiting