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Model selection and accounting for model uncertainty in graphical models using Occam's window
, 1993
"... We consider the problem of model selection and accounting for model uncertainty in highdimensional contingency tables, motivated by expert system applications. The approach most used currently is a stepwise strategy guided by tests based on approximate asymptotic Pvalues leading to the selection o ..."
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Cited by 364 (48 self)
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We consider the problem of model selection and accounting for model uncertainty in highdimensional contingency tables, motivated by expert system applications. The approach most used currently is a stepwise strategy guided by tests based on approximate asymptotic Pvalues leading to the selection of a single model; inference is then conditional on the selected model. The sampling properties of such a strategy are complex, and the failure to take account of model uncertainty leads to underestimation of uncertainty about quantities of interest. In principle, a panacea is provided by the standard Bayesian formalism which averages the posterior distributions of the quantity of interest under each of the models, weighted by their posterior model probabilities. Furthermore, this approach is optimal in the sense of maximising predictive ability. However, this has not been used in practice because computing the posterior model probabilities is hard and the number of models is very large (often greater than 1011). We argue that the standard Bayesian formalism is unsatisfactory and we propose an alternative Bayesian approach that, we contend, takes full account of the true model uncertainty byaveraging overamuch smaller set of models. An efficient search algorithm is developed for nding these models. We consider two classes of graphical models that arise in expert systems: the recursive causal models and the decomposable
Algebraic Algorithms for Sampling from Conditional Distributions
 Annals of Statistics
, 1995
"... We construct Markov chain algorithms for sampling from discrete exponential families conditional on a sufficient statistic. Examples include generating tables with fixed row and column sums and higher dimensional analogs. The algorithms involve finding bases for associated polynomial ideals and so a ..."
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Cited by 264 (20 self)
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We construct Markov chain algorithms for sampling from discrete exponential families conditional on a sufficient statistic. Examples include generating tables with fixed row and column sums and higher dimensional analogs. The algorithms involve finding bases for associated polynomial ideals and so an excursion into computational algebraic geometry.
Preliminaries to a Theory of Speech Disfluencies
, 1994
"... This thesis examines disfluencies (e.g., "um", repeated words, and a variety of forms of selfrepair) in the spontaneous speech of adult normal speakers of American English. Despite their prevalence, disfluencies have traditionally been viewed as irregular events and have received little a ..."
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Cited by 174 (7 self)
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This thesis examines disfluencies (e.g., "um", repeated words, and a variety of forms of selfrepair) in the spontaneous speech of adult normal speakers of American English. Despite their prevalence, disfluencies have traditionally been viewed as irregular events and have received little attention. The goal of the thesis is to provide evidence that, on the contrary, disfluencies show remarkably regular trends in a number of dimensions. These regularities have consequences for models of human language production; they can also be exploited to improve performance in speech applications. The method includes analysis of over 5000 handannotated disfluencies from a database (250,000 words) containing three different styles of spontaneous speech: taskoriented humancomputer dialog, taskoriented humanhuman dialog, and humanhuman conversation on a prescribed topic. The approach is theoryneutral and strongly datadriven. The annotations correspond to observable characteristics ("features") ...
poLCA: Polytomous Variable Latent Class Analysis. R package version 1.4
, 2013
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Modeling affective processes in dyadic relations via dynamic factor analysis
 Emotion
, 2003
"... An intraindividual variability design, including application of dynamic factor models, was used to examine the affective processes of a husband–wife dyad over 182 consecutive days. Structural equation analyses indicated differences in the affective structure between the husband and the wife, and th ..."
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Cited by 15 (2 self)
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An intraindividual variability design, including application of dynamic factor models, was used to examine the affective processes of a husband–wife dyad over 182 consecutive days. Structural equation analyses indicated differences in the affective structure between the husband and the wife, and these differences were characterized in terms of their factorial configuration and temporal organization. Examination of the dyad’s affective dynamics revealed unidirectional (i.e., from the husband to the wife) interpersonal influences with a defined structure over time. The study of intraindividual variability is well recognized as a crucial premise to understanding individual processes. On the strength of this principle, psychologists have devoted substantial effort to examining and comprehending the essence unique to the person (e.g., Allport, 1937). Evidence of the pursuit of this aim are the methods developed to capture an individual’s fluctuations over time (Cattell, 1952, 1961; Cattell, Cattell, & Rhymer, 1947; Zevon & Tellegen, 1982). Illustrative is Ptechnique factor analysis, in which a person is measured at multiple occasions on many variables to generate an individual multivariate time series. This approach, originally intended to identify individual traits (Cattell et al., 1947; Nessel
Split models for contingency tables
, 2003
"... A framework for loglinear models with context specific independence structures, i.e. conditional independencies holding only for specific values of the conditioning variables is introduced. This framework is constituted by the class of split models. Also a software package named YGGDRASIL which is ..."
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Cited by 12 (1 self)
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A framework for loglinear models with context specific independence structures, i.e. conditional independencies holding only for specific values of the conditioning variables is introduced. This framework is constituted by the class of split models. Also a software package named YGGDRASIL which is designed for statistical inference in split models is presented. Split models are an extension of graphical models for contingency tables. The treatment of split models includes estimation, representation and a Markov property for reading off independencies holding in a specific context. Two examples, including an illustration of the use of YGGDRASIL are
Three Centuries of Categorical Data Analysis: Loglinear Models and Maximum Likelihood Estimation
"... The common view of the history of contingency tables is that it begins in 1900 with the work of Pearson and Yule, but it extends back at least into the 19th century. Moreover it remains an active area of research today. In this paper we give an overview of this history focussing on the development o ..."
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Cited by 12 (2 self)
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The common view of the history of contingency tables is that it begins in 1900 with the work of Pearson and Yule, but it extends back at least into the 19th century. Moreover it remains an active area of research today. In this paper we give an overview of this history focussing on the development of loglinear models and their estimation via the method of maximum likelihood. S. N. Roy played a crucial role in this development with two papers coauthored with his students S. K. Mitra and Marvin Kastenbaum, at roughly the midpoint temporally in this development. Then we describe a problem that eluded Roy and his students, that of the implications of sampling zeros for the existence of maximum likelihood estimates for loglinear models. Understanding the problem of nonexistence is crucial to the analysis of large sparse contingency tables. We introduce some relevant results from the application of algebraic geometry to the study of this statistical problem. 1
poLCA: An R package for polytomous variable latent class analysis
 Journal of Statistical Software
"... poLCA is a software package for the estimation of latent class and latent class regression models for polytomous outcome variables, implemented in the R statistical computing environment. Both models can be called using a single simple command line. The basic latent class model is a finite mixture ..."
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Cited by 11 (0 self)
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poLCA is a software package for the estimation of latent class and latent class regression models for polytomous outcome variables, implemented in the R statistical computing environment. Both models can be called using a single simple command line. The basic latent class model is a finite mixture model in which the component distributions are assumed to be multiway crossclassification tables with all variables mutually independent. The latent class regression model further enables the researcher to estimate the effects of covariates on predicting latent class membership. poLCA uses expectationmaximization and NewtonRaphson algorithms to find maximum likelihood estimates of the model parameters.
Sequences of regressions and their independences
, 2012
"... Ordered sequences of univariate or multivariate regressions provide statistical modelsfor analysingdata fromrandomized, possiblysequential interventions, from cohort or multiwave panel studies, but also from crosssectional or retrospective studies. Conditional independences are captured by what we ..."
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Cited by 9 (2 self)
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Ordered sequences of univariate or multivariate regressions provide statistical modelsfor analysingdata fromrandomized, possiblysequential interventions, from cohort or multiwave panel studies, but also from crosssectional or retrospective studies. Conditional independences are captured by what we name regression graphs, provided the generated distribution shares some properties with a joint Gaussian distribution. Regression graphs extend purely directed, acyclic graphs by two types of undirected graph, one type for components of joint responses and the other for components of the context vector variable. We review the special features and the history of regression graphs, prove criteria for Markov equivalence anddiscussthenotion of simpler statistical covering models. Knowledgeof Markov equivalence provides alternative interpretations of a given sequence of regressions, is essential for machine learning strategies and permits to use the simple graphical criteria of regression graphs on graphs for which the corresponding criteria are in general more complex. Under the known conditions that a Markov equivalent directed acyclic graph exists for any given regression graph, we give a polynomial time algorithm to find one such graph.