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630,143
Technology Transfer: Implementing SmallSample Inference
"... in treatment and control groups. If death rates vary among the different groups, would indicate possible systematic effect of different treatments. ffl Treat jth pair R 1 ; R 2 as binomial with probabilities ß 1 = e j +/ j 1 + e j +/ j ; ß 2 = e j 1 + e j ; and denominators m 1 , m 2 ..."
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in treatment and control groups. If death rates vary among the different groups, would indicate possible systematic effect of different treatments. ffl Treat jth pair R 1 ; R 2 as binomial with probabilities ß 1 = e j +/ j 1 + e j +/ j ; ß 2 = e j 1 + e j ; and denominators m 1 , m 2 . ffl Test / 1 = \Delta \Delta \Delta = / n = /, regardless of j . Ulcer data ? deaths r1 m1 r2 m2 0 10 2 14 1 6 2 6 1 12 2 8 6 38 5 32 1 11 2 10 2 15 3 13 2 18 2 13 1 68 1 74 3 40 2 20 1 18 1 21 0 10 1 15 2 14 3 22 1 25 4 32 1 38 0 37 3 19 3 16 1 18 0 18 0 16 1 16 1
Small Sample Inference for Generalization Error in Classification Using the CUD Bound
"... Confidence measures for the generalization error are crucial when small training samples are used to construct classifiers. A common approach is to estimate the generalization error by resampling and then assume the resampled estimator follows a known distribution to form a confidence set [Kohavi 19 ..."
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Cited by 3 (3 self)
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Confidence measures for the generalization error are crucial when small training samples are used to construct classifiers. A common approach is to estimate the generalization error by resampling and then assume the resampled estimator follows a known distribution to form a confidence set [Kohavi
ABSTRACT OF THE DISSERTATION Small Sample Inference for Collections of Bernoulli Trials
, 2010
"... This dissertation discusses two applied problems solved by saddlepoint approximation methods. The first part of this thesis concerns continuity corrected saddlepoint approximations for testing and confidence intervals for the difference of two independent binomial proportions. We propose two new co ..."
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corrections 1/2LCM(m,n) with the least common multiple (LCM) of the two binomial distributions sample sizes m and n considered by Xu and Kolassa[31], and R/2LCM(m,n), an adjusted version of 1/2LCM(m,n) with R by Xu and Kolassa[32], a heuristic factor calculator from the standard error of the marginal null
Improved Small Sample Inference for Efficient Method of Moments and Indirect Inference Estimators”, manuscript
, 2008
"... The efficient method of moments (EMM) and indirect inference (II) are two widely used simulationbased techniques for estimating structural models that have intractable likelihood functions. The poor performance in finite samples of traditional coefficient and overidentification tests based on the E ..."
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Cited by 1 (1 self)
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The efficient method of moments (EMM) and indirect inference (II) are two widely used simulationbased techniques for estimating structural models that have intractable likelihood functions. The poor performance in finite samples of traditional coefficient and overidentification tests based
Estimation and Inference in Econometrics
, 1993
"... The astonishing increase in computer performance over the past two decades has made it possible for economists to base many statistical inferences on simulated, or bootstrap, distributions rather than on distributions obtained from asymptotic theory. In this paper, I review some of the basic ideas o ..."
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Cited by 1151 (3 self)
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The astonishing increase in computer performance over the past two decades has made it possible for economists to base many statistical inferences on simulated, or bootstrap, distributions rather than on distributions obtained from asymptotic theory. In this paper, I review some of the basic ideas
Feeling and thinking: Preferences need no inferences
 American Psychologist
, 1980
"... ABSTRACT: Affect is considered by most contemporary theories to be postcognitive, that is, to occur only after considerable cognitive operations have been accomplished. Yet a number of experimental results on preferences, attitudes, impression formation, and de_ cision making, as well as some cli ..."
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Cited by 533 (2 self)
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ABSTRACT: Affect is considered by most contemporary theories to be postcognitive, that is, to occur only after considerable cognitive operations have been accomplished. Yet a number of experimental results on preferences, attitudes, impression formation, and de_ cision making, as well as some clinical phenomena, suggest that affective judgments may be fairly independent of, and precede in time, the sorts of perceptual and cognitive operations commonly assumed to be the basis of these affective judgments. Affective reactions to stimuli are often the very first reactions of the organism, and for lower organisms they are the dominant reactions. Affective reactions can occur without extensive perceptual and cognitive encoding, are made with greater confidence than cognitive judg
Smallsample corrections for score tests in Birnbaum–Saunders regressions
, 905
"... In this paper we deal with the issue of performing accurate smallsample inference in the Birnbaum– Saunders regression model, which can be useful for modeling lifetime or reliability data. We derive a Bartletttype correction for the score test and numerically compare the corrected test with the us ..."
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Cited by 1 (1 self)
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In this paper we deal with the issue of performing accurate smallsample inference in the Birnbaum– Saunders regression model, which can be useful for modeling lifetime or reliability data. We derive a Bartletttype correction for the score test and numerically compare the corrected test
Loopy Belief Propagation for Approximate Inference: An Empirical Study
 In Proceedings of Uncertainty in AI
, 1999
"... Recently, researchers have demonstrated that "loopy belief propagation"  the use of Pearl's polytree algorithm in a Bayesian network with loops  can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannonlimit performa ..."
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Cited by 680 (18 self)
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inference scheme in a more general setting? We compare the marginals computed using loopy propagation to the exact ones in four Bayesian network architectures, including two realworld networks: ALARM and QMR. We find that the loopy beliefs often converge and when they do, they give a good
Graphical models, exponential families, and variational inference
, 2008
"... The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building largescale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fiel ..."
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Cited by 800 (26 self)
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all be understood in terms of exact or approximate forms of these variational representations. The variational approach provides a complementary alternative to Markov chain Monte Carlo as a general source of approximation methods for inference in largescale statistical models.
Results 1  10
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