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Nonparametric RandomEffects Model
"... Randomeffects modeling is one of the several alternative approaches to deal with dependent observations such as that which occur in repeated measures or multilevel data structures. Nonparametric randomeffects models differ from standard (parametric) randomeffects models in that no assumptions ..."
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Randomeffects modeling is one of the several alternative approaches to deal with dependent observations such as that which occur in repeated measures or multilevel data structures. Nonparametric randomeffects models differ from standard (parametric) randomeffects models in that no assump
Randomeffect models for guessing
"... The guessing parameter γ in the 3PL model (Birnbaum, 1968) can be interpreted in several ways. One of the interpretations is that people guess when they fail (knowing that they fail), and that γ is the probability this guess is correct. Commonly, γ is considered an item parameter. We will present mo ..."
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models with a guessing parameter that varies random over persons, with a normal distribution for the logit of γ. The interpretation of this random gamma could be that people differ in how much they resist attractive wrong responses, or how easily they can eliminate some of the alternatives as being wrong
Random Effects Models for Network Data
, 2003
"... One impediment to the statistical analysis of network data has been the difficulty in modeling the dependence among the observations. In the very simple case of binary (01) network data, some researchers have parameterized network dependence in terms of exponential family representations. Accurate ..."
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Cited by 19 (2 self)
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parameter estimation for such models is quite difficult, and the most commonly used models often display a significant lack of fit. Additionally, such models are generally limited to binary data. In contrast, random effects models have been a widely successful tool in capturing statistical dependence for a
Transactions Likelihood for randomeffect models
"... For inferences from randomeffect models Lee and Nelder (1996) proposed to use hierarchical likelihood (hlikelihood). It allows inference from models that may include both fixed and random parameters. Because of the presence of unobserved random variables hlikelihood is not a likelihood in the Fis ..."
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For inferences from randomeffect models Lee and Nelder (1996) proposed to use hierarchical likelihood (hlikelihood). It allows inference from models that may include both fixed and random parameters. Because of the presence of unobserved random variables hlikelihood is not a likelihood
N: Metaanalysis in clinical trials
 Controlled Clinical Trials
, 1986
"... ABSTRACT: This paper examines eight published reviews each reporting results from several related trials. Each review pools the results from the relevant trials in order to evaluate the efficacy of a certain treatment for a specified medical condition. These reviews lack consistent assessment of hom ..."
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Cited by 1303 (0 self)
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of homogeneity of treatment effect before pooling. We discuss a random effects approach to combining evidence from a series of experiments comparing two treatments. This approach incorporates the heterogeneity of effects in the analysis of the overall treatment efficacy. The model can be extended to include
Metabolic stability and epigenesis in randomly connected nets
 Journal of Theoretical Biology
, 1969
"... “The world is either the effect of cause or chance. If the latter, it is a world for all that, that is to say, it is a regular and beautiful structure.” Marcus Aurelius Protoorganisms probably were randomly aggregated nets of chemical reactions. The hypothesis that contemporary organisms are also r ..."
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Cited by 657 (5 self)
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“The world is either the effect of cause or chance. If the latter, it is a world for all that, that is to say, it is a regular and beautiful structure.” Marcus Aurelius Protoorganisms probably were randomly aggregated nets of chemical reactions. The hypothesis that contemporary organisms are also
A Random Effects Model of the Emergence of Charter Schools in North Carolina
"... random effects model of the emergence of charter schools in North Carolina. Education Policy ..."
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random effects model of the emergence of charter schools in North Carolina. Education Policy
Segmentation of brain MR images through a hidden Markov random field model and the expectationmaximization algorithm
 IEEE TRANSACTIONS ON MEDICAL. IMAGING
, 2001
"... The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogrambased model, the FM has an intrinsic limi ..."
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Cited by 639 (15 self)
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limitation—no spatial information is taken into account. This causes the FM model to work only on welldefined images with low levels of noise; unfortunately, this is often not the the case due to artifacts such as partial volume effect and bias field distortion. Under these conditions, FM model
Markov Random Field Models in Computer Vision
, 1994
"... . A variety of computer vision problems can be optimally posed as Bayesian labeling in which the solution of a problem is defined as the maximum a posteriori (MAP) probability estimate of the true labeling. The posterior probability is usually derived from a prior model and a likelihood model. The l ..."
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Cited by 516 (18 self)
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. The latter relates to how data is observed and is problem domain dependent. The former depends on how various prior constraints are expressed. Markov Random Field Models (MRF) theory is a tool to encode contextual constraints into the prior probability. This paper presents a unified approach for MRF modeling
Results 1  10
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