In many surveys, the data comprise a large number of categorical variables that suffer from item nonresponse. Standard methods for multiple imputation, like log-linear models or sequential regression imputation, can fail to capture complex dependencies and can be difficult to implement effectively in high dimensions. We present a fully Bayesian, joint modeling approach to multiple imputation fo...

Log-linear models have recently been used in acoustic modeling for speech recognition systems. This has been motivated by competitive results compared to systems based on Gaussian models, and a more direct parametrisation of the posterior model. To competitively use log-linear models for speech recognition, important methods, such as speaker adaptation, have to be reformulated in a log-linear f...

This document describes log-linear models, which are a far-reaching extension of logistic regression, and conditional random fields (CRFs), which are a special case of log-linear models. Section 1 explains what a log-linear model is, and introduces feature functions. Section 2 then presents linear-chain CRFs as an example of log-linear models, and Section 3 explains the special algorithms that ...

Log-linear analysis has become a widely used method for the analysis of multivariate frequency tables obtained by crossclassifying sets of nominal, ordinal, or discrete interval level variables. Examples of textbooks discussing categorical data analysis by means of log-linear models are [4], [2], [14], [15], [16], and [27]. We start by introducing the standard hierarchical log-linear modelling ...

In this article, we establish properties that relate quantiles of the log-skew-normal distribution to its parameters, allowing us investigate relationship between a positive skewed response variable and set explanatory variables via linear regression model. We compute maximum likelihood estimates parameters through correspondence skew-normal models. Monte Carlo simulations show satisfactory per...

Based on the experience of teaching logistic regression to non-mathematicians, a number of areas of possible confusion are identified that may arise particularly when the method is contrasted with multiple linear regression. The fact that the model is multiplicative in odds ratios means that the concept of interaction needs to be clearly defined. Confidence intervals for the estimates of the od...