Results 11 to 20 of about 2,337,681 (280)
Meta-Analysis with Few Studies and Binary Data: A Bayesian Model Averaging Approach
Mathematics, 2020 In meta-analysis, the existence of between-sample heterogeneity introduces model uncertainty, which must be incorporated into the inference. We argue that an alternative way to measure this heterogeneity is by clustering the samples and then determining ...
Francisco-José Vázquez-Polo +2 more
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Comparison of Confirmatory Factor Analysis Estimation Methods on Binary Data
International Journal of Assessment Tools in Education, 2020 This Monte Carlo simulation study aimed to investigate confirmatory factor analysis (CFA) estimation methods under different conditions, such as sample size, distribution of indicators, test length, average factor loading, and factor structure.
Burcu Atar +2 more
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Passer Journal, 2020 The beta-binomial model that is generated by a simple mixture model has been commonly applied in the health, physical, and social sciences. In clinical and public health, overdispersion occurs due to biological variation between the subjects of interest.
Hanaw Ahmed Amin, Rando Rasul Qadir
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Log-mean linear models for binary data [PDF]
, 2012 This paper introduces a novel class of models for binary data, which we call log-mean linear models. The characterizing feature of these models is that they are specified by linear constraints on the log-mean linear parameter, defined as a log-linear ...
A. Roverato +4 more
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Revista de Matemática: Teoría y Aplicaciones, 2017 Within the context of a latent class model with manifest binary variables, we propose an alternative method that solves the problem of estimating empirical distribution with sparse contingency tables and the chi-square approximation for goodness-of-fit ...
Carlomagno Araya Alpizar
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Approximate initial data for binary black holes [PDF]
, 2006 We construct approximate analytical solutions to the constraint equations of general relativity for binary black holes of arbitrary mass ratio in quasicircular orbit.
Baumgarte, Thomas W. +2 more
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A binary neural k-nearest neighbour technique [PDF]
, 2005 K-Nearest Neighbour (k-NN) is a widely used technique for classifying and clustering data. K-NN is effective but is often criticised for its polynomial run-time growth as k-NN calculates the distance to every other record in the data set for each record ...
Austin, J., Hodge, V.J.
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Stats, 2020 In sequential tests, typically a (pairwise) multiple comparison procedure (MCP) is performed after an omnibus test (an overall equality test). In general, when an omnibus test (e.g., overall equality of multiple proportions test) is rejected, then we ...
Dewi Rahardja
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Data-Influence Analytics in Predictive Models Applied to Asthma Disease
Mathematics, 2020 Asthma is one of the most common chronic diseases around the world and represents a serious problem in human health. Predictive models have become important in medical sciences because they provide valuable information for data-driven decision-making. In
Alejandra Tapia +3 more
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Comparing initial-data sets for binary black holes [PDF]
, 2002 We compare the results of constructing binary black hole initial data with three different decompositions of the constraint equations of general relativity. For each decomposition we compute the initial data using a superposition of two Kerr-Schild black
A. Garat +37 more
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