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Applied Latent Class Analysis by Allan L. McCutcheon, Jacques A. Hagenaars

Applied Latent Class Analysis Allan L. McCutcheon, Jacques A. Hagenaars ebook
Format: pdf
Publisher: Cambridge University Press
ISBN: 0521594510, 9780521594516
Page: 478

Friday, November 2, in Cleveland Hall 418. Candidate at North Carolina State University, at 3:00 p.m. Optimum decision points for In applying this new agent system to diagnosis of acute myocardial infarction (AMI) we demonstrated that at an optimum clustering distance the number of classes is minimized with efficient training on the neural network. To determine the underlying causes that were more likely to lead to PMV, we applied LCA to group separate co-morbidity diagnoses into no more than 10 clusters of in-patients who had undergone PMV. The knowledge from data/database (KDD) framework for preparing data and finding patterns in large amounts of data served as the process framework in which a latent class analysis (LCA) was applied to IA user data. Abstract: When data are believed to have hidden clusters, it is common practice to identify the clusters and then conduct analysis in each cluster separately. Three data sets have been extensively validated prior to neural network analysis using receiver-operator curve (ROC analysis), Latent Class Analysis, and a multinomial regression approach. Latent class analysis can be applied to determine the sensitivity and specificity of a new test when no standard exists. Latent class analysis (LCA) was used to group all GOLDN individuals according to the diameter of their fasting (8-hour fast) VLDL, LDL and HDL particles, determined by nuclear resonance spectroscopy. Please join us for the applied mathematics talk "Penalization Techniques for Latent Class Regression," presented by Anthony Franklin, a Ph.D. The researchers then applied latent class growth analysis to determine the smoking trajectory for the students, measuring how smoking behaviors changed over time.