Motivated by a space-time study on forest health with damage state of trees as the response, we propose a general class of structured additive regression models for categorical responses, allowing for a flexible semiparametric predictor. Nonlinear effects of continuous covariates, time trends, and interactions between continuous covariates are modeled by penalized splines. Spatial effects can b...

Two analytical procedures for identifying young children as categorizers, the Monte Carlo Simulation and the Probability Estimate Model, were compared. Using a sequential touching method, children aged 12, 18, 24, and 30 months were given seven object sets representing different levels of categorical classification. From their touching performance, the probability that children were categorizin...

Interest in modeling the structure of latent variables is gaining momentum, and many simulation studies suggest that taxometric analysis can validly assess the relative fit of categorical and dimensional models. The generation and parallel analysis of categorical and dimensional comparison data sets reduces the subjectivity required to interpret results by providing an objective Comparison Curv...

This paper demonstrates that a low-level, linear description of the response properties of auditory neurons can exhibit some of the high-level properties of the categorical perception of human speech. In particular, it is shown that the non-linearities observed in the human perception of speech sounds which span a categorical boundaries can be understood as arising rather naturally from a low-l...

Classification problems involving a categorical class label Y and a functional predictor X(t) are becoming increasingly common. Since X(t) is infinite dimensional, some form of dimension reduction is essential in these problems. Conventional dimension reduction techniques for functional data usually suffer from one or both of the following problems. First, they do not take the categorical respo...

This paper is concerned with the problem of determining the e!ect of a categorical treatment variable on a response given that the treatment is non-randomly assigned and the response (on any given subject) is observed for one setting of the treatment. We consider classes of models that are designed for such problems. These models are subjected to a fully Bayesian analysis based on Markov chain ...

Statistics ANOVA and related Multiple imputation Basic statistics Multivariate analysis of variance and Binary outcomes related techniques Categorical outcomes Nonlinear regression Censored and truncated regression models Nonparametric statistics Cluster analysis Ordinal outcomes Correspondence analysis Other statistics Count outcomes Pharmacokinetic statistics Discriminant analysis Power and s...

The paper considers the problem of modeling association among multiple exposures in a matched case-control study where some of the exposures may be partially missing. The exposure variables could all be categorical or continuous or could be a mixed set of some categorical and some continuous variables. Association models using completely observed covariates are proposed for each of the three sc...

In many application areas, data are collected on a categorical response and high-dimensional categorical predictors, with the goals being to build a parsimonious model for classification while doing inferences on the important predictors. In settings such as genomics, there can be complex interactions among the predictors. By using a carefully-structured Tucker factorization, we define a model ...

Missing values are a common phenomenon in all areas of applied research. While various imputation methods are available for metrically scaled variables, methods for categorical data are scarce. An imputation method that has been shown to work well for high dimensional metrically scaled variables is the imputation by nearest neighbor methods. In this paper, we extend the weighted nearest neighbo...