A method for fully automating the measurement of various neurological structures in MRI is presented. This technique uses an atlas-based trained maximum likelihood classifier. The classifier requires a map of prior probabilities, which is obtained by registering a large number of previously classified data sets to the atlas and calculating the resulting probability that each represented tissue ...

This paper considers settings where populations of units may experience recurrent events, termed failures for convenience, and where the units are subject to varying levels of usage. We provide joint models for the recurrent events and usage processes, which facilitate analysis of their relationship as well as prediction of failures. Data on usage are often incomplete and we show how to impleme...

seemed to have in the past towards the logit transformation have now been withdrawn but the use of logits is not described in this book. The later chapters of the book provide both a framework of theory based on maximum likelihood estimation and a discussion of more specialized practical problems such as the study of the joint action of drugs. These later sections are not easy to read but are w...

Network meta-analysis is used to simultaneously compare multiple treatments in a single analysis. However, network meta-analyses may exhibit inconsistency, where direct and different forms of indirect evidence are not in agreement with each other, even after allowing for between-study heterogeneity. Models for network meta-analysis with random inconsistency effects have the dual aim of allowing...

Models with random effects/latent variables are widely used for capturing unobserved heterogeneity in multilevel/hierarchical data and account for associations in multivariate data. The estimation of those models becomes cumbersome as the number of latent variables increases due to high-dimensional integrations involved. Composite likelihood is a pseudo-likelihood that combines lower-order marg...

The maximum likelihood estimation (MLE) method, typically used for polytomous logistic regression, is prone to bias due to both misclassification in outcome and contamination in the design matrix. Hence, robust estimators are needed. In this study, we propose such a method for nominal response data with continuous covariates. A generalized method of weighted moments (GMWM) approach is developed...

The introduced three parameter (position μ, scale Σ and shape γ) multivariate generalized Normal distribution (γ-GND) is based on a strong theoretical background and emerged from Logarithmic Sobolev Inequalities. It includes a number of well known distributions such as the multivariate Uniform, Normal, Laplace and the degenerated Dirac distributions. In this paper, the cumulative distribution, ...

In this paper we propose a new approach, called a fuzzy class model for Poisson regression, in the analysis of heterogeneous count data. On the basis of fuzzy set concept and fuzzy classification maximum likelihood (FCML) procedures we create an FCML algorithm for fuzzy class Poisson regression models. Traditionally, the EM algorithm had been used for latent class regression models. Thus, the a...

In this paper, the performance of a new fuzzy classifier, here called fuzzy β-NN, has been analyzed. The classifier classifies data according to the fuzzy membership values of the reference set inside the prespecified radius β. Members of the reference set outside the radius β have no influence on classification decision. The successful classification by the classifier depends on the parameters...