Results 121 to 130 of about 1,123 (168)
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MODEL SELECTION CRITERIA FOR LOGLINEAR MODELS
Australian & New Zealand Journal of Statistics, 2010SummaryConsiderable work has been devoted to developing model selection criteria for normal theory regression models. Less attention has been paid to discrete data. We develop two loglinear model selection criteria for Poisson counts. These criteria are based on an estimated bias adjustment of the Akaike information criterion.
Edward J. Bedrick, Winston K. Crandall
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Recognizing Submodels of a Loglinear Model
1992Submodels of a loglinear probabilistic model are characterized by means of a graph theoretic property suitable for an efficient computation.
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Logit Loglinear and Hierarchical Loglinear Modeling for Outcome Categories (445 Patients)
2020Multinomial regression is adequate for identifying the main predictors of certain outcome categories, like different levels of injury or quality of life (QOL) (see also Chap. 28). An alternative approach is logit loglinear modeling. The latter method does not use continuous predictors on a case by case basis, but rather the weighted means of these ...
Ton J. Cleophas, Aeilko H. Zwinderman
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On hierarchical loglinear models in capture–recapture studies
Computational Statistics & Data Analysis, 2009zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Na You, Chang Xuan Mao
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Using Loglinear Models to Compress Datacubes
2000A data cube is a popular organization for summary data. A cube is simply a multidimensional structure that contains in each cell an aggregate value, i.e., the result of applying an aggregate function to an underlying relation. In practical situations, cubes can require a large amount of storage, so, compressing them is of practical importance.
Daniel Barbará, Xintao Wu
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LOGLINEAR MODELING WITH INEXPENSIVE COMPUTING EQUIPMENT
American Journal of Epidemiology, 1984Loglinear models are finding increasing application in the analysis of data from epidemiologic studies and increasing attention in statistics courses taken by epidemiologists in training. This paper describes a program for microcomputers, written in BASIC, which fits hierarchic loglinear models to categoric data organized into multiway contingency ...
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Loglinear Multivariate and Mixture Rasch Models
2007In this chapter, Rasch models (RMs) are derived from a stochastic subject model. Fixed-effects RMs are shown to be equivalent to loglinear models with raw-score variables; random-effects RMs are equivalent to loglinear models with latent class variables. Within the larger framework of loglinear models, various extensions of the RM can be formulated. We
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The Association Graph and the Multigraph for Loglinear Models
2011The Association Graph and the Multigraph for Loglinear Models will help students, particularly those studying the analysis of categorical data, to develop the ability to evaluate and unravel even the most complex loglinear models without heavy calculations or statistical software.
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An Axiomatization of Loglinear Models with an Application to the Model-Search Problem
1996A good strategy to save computational time in a model-search problem consists in endowing the search procedure with a mechanism of logical inference, which sometimes allows a loglinear model to be accepted or rejected on logical grounds, without resorting to the numeric test.
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