BACKGROUND Multiple imputation is a recommended method to handle missing data. For significance testing after multiple imputation, Rubin's Rules (RR) are easily applied to pool parameter estimates. In a logistic regression model, to consider whether a categorical covariate with more than two levels significantly contributes to the model, different methods are available. For example pooling chi-...

Correspondence analysis on generalised aggregated lexical tables (CA-GALT) is a method that generalizes classical CA-ALT to the case of several quantitative, categorical and mixed variables. It aims to establish a typology of the external variables and a typology of the events from their mutual relationships. In order to do so, the influence of external variables on the lexical choices is untan...

each observation with p traits measured in each environment is normally distributed. For r environments, the Classification methods are used in genetic resource conservation vector of each observation has dimensions 1 rp, where and plant breeding. The two-stage Ward-Modified Location Model (Ward-MLM) clustering strategy defines initial groups by the Ward rp trait–environment combinations (varia...

The factor mixture model (FMM) uses a hybrid of both categorical and continuous latent variables. The FMM is a good model for the underlying structure of psychopathology because the use of both categorical and continuous latent variables allows the structure to be simultaneously categorical and dimensional. While the conceptualization of the FMM has been explained in the literature, the use of ...

While many of the measurement approaches in health inequality measurement assume the existence of a ratio-scale variable, most of the health information available in population surveys is given in the form of categorical variables. Therefore, the well-known inequality indices may not always be readily applicable to measure health inequality as it may result in the arbitrariness of the health co...

Humans can perceive a variety of visual properties of objects besides their category. In this paper, we explorewhether convolutional neural networks (CNNs) can also learn object-related variables. The models are trained for object position, size and pose, respectively, from synthetic images and tested on unseen held-out objects. First, we show that some object properties come "for free" from le...

There are many applications dealing with incomplete data sets that take different approaches to making imputations for missing values. Most tackle the problem for numerical input variables in the data set. However, when there are two types of input variables, numerical and categorical, the state of the art has provided no clear solutions. This paper presents a proposal for handling incomplete n...

For categorical data, the relationship between visual displays of the data and models is not very well explored, even though for categorical data and loglinear models strong relationships do exist. Starting from assessing odds ratios visually, interaction effects of variables can be examined using mosaic plots. Cumulative link models provide a way to describe trends between ordinal variables. I...

In this document we describe the Mixture EFA model estimated in Mplus. Four types of dependent variables are possible in this model: normally distributed, ordered categorical with logit or probit link, Poisson distributed with the exponential link function, and censored variables. Inflation is not available for the Censored and Poisson variables. Suppose that we estimate a K class model with M ...

Historically, the focus of behavior genetic research was to obtain estimates of the sources of familial resemblance for a single phenotype. Current research strategies have moved beyond heritability estimates to the search for physiological and behavioral mechanisms by which genetic risk is translated into individual differences in behavior and disease liability. Such research questions often r...