Results 211 to 220 of about 484,316 (264)
Some of the next articles are maybe not open access.
Strategies for the Selection of Log-Linear Models
Biometrics, 1978In a multidimensional contingency table strategies have been proposed to build log-linear models using either stepwise methods or standardized estimates of the parameters of the saturated model. Brown (1976) proposed a two-step procedure to screen effects and then test a subset of models.
Benedetti, Jacqueline K. +1 more
openaire +2 more sources
Conditional log-linear structures for log-linear modelling
Computational Statistics & Data Analysis, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +2 more sources
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
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
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
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
Log-linear modeling using conditional log-linear structures
Annals of the Institute of Statistical Mathematics, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
VELLAISAMY, P, VIJAY, V
openaire +2 more sources
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
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
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