Results 211 to 220 of about 136,511 (258)
Some of the next articles are maybe not open access.
Aggregation with Log-Linear Models
The Review of Economic Studies, 1992When economic theory suggests a log-linear specification for individual agents, e.g., CobbDouglas production, it is common to estimate the same log-linear model with aggregate data, invoking a representative agent assumption and thereby assuming away aggregation errors.
openaire +1 more source
2012
This chapter describes graphical models for multivariate discrete (categorical) data. It starts out by describing various different ways in which such data may be represented in R—for example, as contingency tables—and how to convert between these representations.
Søren Højsgaard +2 more
openaire +1 more source
This chapter describes graphical models for multivariate discrete (categorical) data. It starts out by describing various different ways in which such data may be represented in R—for example, as contingency tables—and how to convert between these representations.
Søren Højsgaard +2 more
openaire +1 more source
2005
A large amount of data collected in the social sciences are counts crossclassified into categories. These counts are non-negative integers and require special methods of analysis to model appropriately; log-linear models are one sophisticated method. The counts are modeled by the Poisson distribution, and related to the classifying variables through a ...
openaire +1 more source
A large amount of data collected in the social sciences are counts crossclassified into categories. These counts are non-negative integers and require special methods of analysis to model appropriately; log-linear models are one sophisticated method. The counts are modeled by the Poisson distribution, and related to the classifying variables through a ...
openaire +1 more source
1981
The three preceding chapters have all used models in which the response variables were probabilities (Chapters 4 and 5) or a linear combination of probabilities (Chapter 6). In this chapter we consider a model in which the response function involves the natural logarithm of the response variable.
Ron N. Forthofer, Robert G. Lehnen
openaire +1 more source
The three preceding chapters have all used models in which the response variables were probabilities (Chapters 4 and 5) or a linear combination of probabilities (Chapter 6). In this chapter we consider a model in which the response function involves the natural logarithm of the response variable.
Ron N. Forthofer, Robert G. Lehnen
openaire +1 more source
2006
Abstract Log-linear models for multidimensional tables of discrete data were first popularized by Goodman (1970) and Bishop et al. (1975). These models can be interpreted in terms of interactions between the various factors in multidimensional tables and are easily generalized to higher dimensions.
openaire +1 more source
Abstract Log-linear models for multidimensional tables of discrete data were first popularized by Goodman (1970) and Bishop et al. (1975). These models can be interpreted in terms of interactions between the various factors in multidimensional tables and are easily generalized to higher dimensions.
openaire +1 more source
Table selection and log-linear models
Journal of Chronic Diseases, 1980Abstract The use of multi-dimensional contingency tables has become commonplace in the analysis of epidemiological data. This paper examines two problems in this methodology. First, for data having many response or dependent variables, it is often unclear as to which of many possible tables should be analyzed.
D H, Freeman, J F, Jekel
openaire +2 more sources
Equivalence of Generative and Log-Linear Models
IEEE Transactions on Audio, Speech, and Language Processing, 2011Conventional speech recognition systems are based on hidden Markov models (HMMs) with Gaussian mixture models (GHMMs). Discriminative log-linear models are an alternative modeling approach and have been investigated recently in speech recognition. GHMMs are directed models with constraints, e.g., positivity of variances and normalization of conditional
Georg Heigold +4 more
openaire +1 more source
2014
The classical log-linear models are introduced for two-way and multi-way contingency tables. Estimation theory, goodness-of-fit testing, and model selection procedures are discussed. Characteristic examples are worked out in R and interpreted. Log-linear models for three-dimensional tables are illustrated through mosaic plots.
openaire +1 more source
The classical log-linear models are introduced for two-way and multi-way contingency tables. Estimation theory, goodness-of-fit testing, and model selection procedures are discussed. Characteristic examples are worked out in R and interpreted. Log-linear models for three-dimensional tables are illustrated through mosaic plots.
openaire +1 more source
Log-Linear Modelling and Spatial Analysis
Environment and Planning A: Economy and Space, 1985In the past decade the social sciences have seen an upsurge of interest in analysing multidimensional contingency tables using log-linear models. Two broad families of log-linear models may be distinguished: the family of conventional models and the family of unconventional models (that is, quasi-log-linear and hybrid models).
E Aufhauser, M M Fischer
openaire +3 more sources
Log-Linear Models for Linked Loci
Biometrics, 1984Noting the importance of measuring the pure effects of various disequilibrium parameters in a randomly mating population, the author suggests a class of log-linear models together with methods which enable one to choose the model which best fits the data.
openaire +2 more sources