Results 121 to 130 of about 1,086 (165)

Recognizing Submodels of a Loglinear Model

1992
Submodels of a loglinear probabilistic model are characterized by means of a graph theoretic property suitable for an efficient computation.
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On hierarchical loglinear models in capture–recapture studies

Computational Statistics & Data Analysis, 2009
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Na You, Chang Xuan Mao
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Exporters’ Exposures to Currencies: Beyond the Loglinear Model

Review of Finance, 2015
Abstract 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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Using Loglinear Models to Compress Datacubes

2000
A 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, 1984
Loglinear 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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The Association Graph and the Multigraph for Loglinear Models

2011
The 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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Loglinear Multivariate and Mixture Rasch Models

2007
In 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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An Axiomatization of Loglinear Models with an Application to the Model-Search Problem

1996
A 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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Loglinear models

1997
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Lognormal Loglinear Model (Basics)

2016
In the lognormal loglinear model the distribution of some random vector is specified by assuming that its logarithm, i.e. the random vector consisting of the logarithms of the coordinates of the given random vector, has a normal distribution and fulfills the conditions of a linear model.
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