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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 370 (47 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
Bayesian Model Selection in Social Research (with Discussion by Andrew Gelman & Donald B. Rubin, and Robert M. Hauser, and a Rejoinder)
 SOCIOLOGICAL METHODOLOGY 1995, EDITED BY PETER V. MARSDEN, CAMBRIDGE,; MASS.: BLACKWELLS.
, 1995
"... It is argued that Pvalues and the tests based upon them give unsatisfactory results, especially in large samples. It is shown that, in regression, when there are many candidate independent variables, standard variable selection procedures can give very misleading results. Also, by selecting a singl ..."
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Cited by 585 (21 self)
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It is argued that Pvalues and the tests based upon them give unsatisfactory results, especially in large samples. It is shown that, in regression, when there are many candidate independent variables, standard variable selection procedures can give very misleading results. Also, by selecting a
Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification
 Psychological Methods
, 1998
"... This study evaluated the sensitivity of maximum likelihood (ML), generalized least squares (GLS), and asymptotic distributionfree (ADF)based fit indices to model misspecification, under conditions that varied sample size and distribution. The effect of violating assumptions of asymptotic robustn ..."
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Cited by 543 (0 self)
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This study evaluated the sensitivity of maximum likelihood (ML), generalized least squares (GLS), and asymptotic distributionfree (ADF)based fit indices to model misspecification, under conditions that varied sample size and distribution. The effect of violating assumptions of asymptotic
How much should we trust differencesindifferences estimates?
, 2003
"... Most papers that employ DifferencesinDifferences estimation (DD) use many years of data and focus on serially correlated outcomes but ignore that the resulting standard errors are inconsistent. To illustrate the severity of this issue, we randomly generate placebo laws in statelevel data on femal ..."
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Cited by 828 (1 self)
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into account the autocorrelation of the data) works well when the number of states is large enough. Two corrections based on asymptotic approximation of the variancecovariance matrix work well for moderate numbers of states and one correction that collapses the time series information into a “pre” and “post
Policy gradient methods for reinforcement learning with function approximation.
 In NIPS,
, 1999
"... Abstract Function approximation is essential to reinforcement learning, but the standard approach of approximating a value function and determining a policy from it has so far proven theoretically intractable. In this paper we explore an alternative approach in which the policy is explicitly repres ..."
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Cited by 439 (20 self)
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Abstract Function approximation is essential to reinforcement learning, but the standard approach of approximating a value function and determining a policy from it has so far proven theoretically intractable. In this paper we explore an alternative approach in which the policy is explicitly
On the distribution of the largest eigenvalue in principal components analysis
 ANN. STATIST
, 2001
"... Let x �1 � denote the square of the largest singular value of an n × p matrix X, all of whose entries are independent standard Gaussian variates. Equivalently, x �1 � is the largest principal component variance of the covariance matrix X ′ X, or the largest eigenvalue of a pvariate Wishart distribu ..."
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Cited by 422 (4 self)
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Let x �1 � denote the square of the largest singular value of an n × p matrix X, all of whose entries are independent standard Gaussian variates. Equivalently, x �1 � is the largest principal component variance of the covariance matrix X ′ X, or the largest eigenvalue of a pvariate Wishart
Combining evidence using pvalues: Application to sequence homology searches
 Bioinformatics
, 1998
"... Motivation: To illustrate an intuitive and statistically valid method for combining independent sources of evidence that yields a pvalue for the complete evidence, and to apply it to the problem of detecting simultaneous matches to multiple patterns in sequence homology searches. Results: In seque ..."
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Cited by 228 (13 self)
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Motivation: To illustrate an intuitive and statistically valid method for combining independent sources of evidence that yields a pvalue for the complete evidence, and to apply it to the problem of detecting simultaneous matches to multiple patterns in sequence homology searches. Results
The Node Distribution of the Random Waypoint Mobility Model for Wireless Ad Hoc Networks
, 2003
"... The random waypoint model is a commonly used mobility model in the simulation of ad hoc networks. It is known that the spatial distribution of network nodes moving according to this model is, in general, nonuniform. However, a closedform expression of this distribution and an indepth investigation ..."
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Cited by 377 (10 self)
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of the resulting distribution is the weighted sum of three independent components: the static, pause, and mobility component. This division enables us to understand how the models parameters influence the distribution. We derive an exact equation of the asymptotically stationary distribution for movement on a
The positive false discovery rate: A Bayesian interpretation and the qvalue
 Annals of Statistics
, 2003
"... Multiple hypothesis testing is concerned with controlling the rate of false positives when testing several hypotheses simultaneously. One multiple hypothesis testing error measure is the false discovery rate (FDR), which is loosely defined to be the expected proportion of false positives among all s ..."
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Cited by 337 (8 self)
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general conditions, even under certain forms of dependence. Also, a new quantity called the “qvalue ” is introduced and investigated, which is a natural “Bayesian posterior pvalue, ” or rather the pFDR analogue of the pvalue.
Numerical Distribution Functions of Likelihood Ratio Tests for Cointegration
 Journal of Applied Econometrics
, 1999
"... This paper employs response surface regressions based on simulation experiments to calculate asymptotic distribution functions for the Johansentype likelihood ratio tests for cointegration. These are carried out in the context of the models recently proposed by Pesaran, Shin, and Smith (1997) that ..."
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Cited by 299 (11 self)
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response surface estimation and a computer program for utilizing them. This program, which is freely available via the Internet, can be used to calculate both asymptotic critical values and P values. JEL Classification Number: C22 Keywords: cointegration tests, Johansen tests, vector autoregressions
Results 1  10
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