A Monte Carlo evaluation of three methods to detect local dependence in binary data latent class models
type: Item
instance of: scholarly article
title: A Monte Carlo evaluation of three methods to detect local dependence in binary data latent class models
zbMATH Open document ID: 1273.62142
author: Daniel L. Oberski
author: Geert H. Van Kollenburg
author: Jeroen K. Vermunt
published in: Advances in Data Analysis and Classification. ADAC
publication date: 2013-09-24T00:00:00Z
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 62-07
Mathematics Subject Classification ID: 65C05
Mathematics Subject Classification ID: 62F40
Mathematics Subject Classification ID: 62H12
Mathematics Subject Classification ID: 62H15
zbMATH DE Number: 6210923
zbMATH Keywords: conditional dependence
zbMATH Keywords: latent variable models
zbMATH Keywords: score tests
zbMATH Keywords: Lagrange multiplier tests
zbMATH Keywords: modification index
zbMATH Keywords: bivariate residuals
describes a project that uses: Latent GOLD
describes a project that uses: R
MaRDI profile type: Publication
full work available at URL: s11634-013-0146-2
OpenAlex ID: W1991428170
Wikidata QID: Q57569402
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cites work: Statistical tests of conditional independence between responses and/or response times on test items
DBLP publication ID: journals/adac/OberskiKV13
DOI: 10.1007/S11634-013-0146-2
description: scientific article; zbMATH DE number 6210923