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Loglinear Models, Logit Loglinear Models (445 Patients)
2016Multinomial regression is adequate for identifying the main predictors of outcome categories, like levels of injury or quality of life (QOL). An alternative approach is logit loglinear modeling. It does not use continuous predictors on a case by case basis, but rather the weighted means of subgroups formed with the help of predictors. This approach may
Ton J. Cleophas, Aeilko H. Zwinderman
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Loglinear Models, Hierarchical Loglinear Models (445 Patients)
2016The Pearson chi-square test is traditionally used for analyzing two dimensional contingency tables, otherwise called crosstabs or interaction matrices. They can answer questions like: is the risk of falling out of bed different between the departments of surgery and internal medicine (Chap. 35).
Ton J. Cleophas, Aeilko H. Zwinderman
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Australian Journal of Education, 1989
This didactic paper is intended as a guide to the use of microcomputer statistical analysis packages for researchers who have selected loglinear analysis as appropriate for their problem. It is assumed that readers will be familiar with the statistical theory of loglinear analysis as treated in Kennedy (1988) and Busk and Marascuilo (1989).
Mark Wilson, Stephen Moore
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This didactic paper is intended as a guide to the use of microcomputer statistical analysis packages for researchers who have selected loglinear analysis as appropriate for their problem. It is assumed that readers will be familiar with the statistical theory of loglinear analysis as treated in Kennedy (1988) and Busk and Marascuilo (1989).
Mark Wilson, Stephen Moore
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Application of loglinear models to informetric phenomena
Information Processing & Management, 1992Abstract Informetrics deals with the search for regularities in data associated with the production and use of recorded information. Most of the methods used in the past implicitly assume that the variables of importance are quantitative in form. Yet much relevant data is categorical.
Abraham Bookstein +3 more
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Multigraph representations of hierarchical loglinear models
Journal of Statistical Planning and Inference, 1996zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Khamis, Harry J., McKee, Terry A.
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Optimal prediction in loglinear models
Journal of Econometrics, 2001zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Visualizing parameters from loglinear models
Computational Statistics, 2004An interactive graphical display for the parameters of loglinear models for categorical data is described. It is demonstrated how this display can be used for the analysis of the dependence structure of data, especially, for the identification of non-hierarchical models. The Berkeley admission and Knowledge of cancer data sets are used in the examples.
Pedro M. Valero-Mora +2 more
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Hierarchische loglineare Modelle
2017Im Kapitel 2.2.2. haben wir uns mit der Darstellung von Datensatzen befasst, die qualitative Merkmale enthalten. Wir haben gelernt, die Haufigkeitsverteilung von mehreren qualitativen Merkmalen in einer Kontingenztabelle zusammenzustellen. Wir wollen uns nun mit Modellen beschaftigen, die die Abhangigkeitsstruktur zwischen den Merkmalen beschreiben ...
Torben Kuhlenkasper, Andreas Handl
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Exporters’ Exposures to Currencies: Beyond the Loglinear Model
Review of Finance, 2015Abstract We extend the constant-elasticity regression that is the default choice when equities’ exposure to currencies is estimated. In a proper real-option-style model for the exporters’ equity exposure to the foreign exchange rate, we argue, the convexity of the relationship implies that the elasticity should depend on the exchange ...
Boudt, K.M.R., Liu, F., Sercu, P.
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2009
Loglinear models provide the most flexible tools for analyzing relationships among categorical variables in complex tables. It will be shown in this chapter how to apply these models in the context of marginal modeling. First, in Section 2.1, the basics of ordinary loglinear modeling will be explained.
Wicher Bergsma +2 more
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Loglinear models provide the most flexible tools for analyzing relationships among categorical variables in complex tables. It will be shown in this chapter how to apply these models in the context of marginal modeling. First, in Section 2.1, the basics of ordinary loglinear modeling will be explained.
Wicher Bergsma +2 more
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