For a given bivariate survival function F, we study the relations between the set of the level curves of F and the Kendall distribution. Then we characterize the survival models simultaneously admitting a specified Kendall distribution and a specified set of level curves. Attention will be restricted to exchangeable survival models. 2009 Elsevier Inc. All rights reserved.

The content of human sweat is studied by high-resolution NMR, and the majority of organic components most often found in sweat of conditionally healthy people are identified. Original and simple tools are designed for sweat sampling from different areas of human body. The minimal surface area needed for sampling is in the range of 50-100 cm(2). On all the surface parts of the human body examine...

Despite all the attention paid to variational methods based on sum-product message passing (loopy belief propagation, tree-reweighted sum-product), these methods are still bound to inference on a small set of probabilistic models. Mean field approximations have been applied to a broader set of problems, but the solutions are often poor. We propose a new class of conditionally-specified variatio...

A characterization of all pairs (U,S) where is U left-continuous uninorms with neutral element e from (0,1] and S countinuous t-conorms satisfying distributivity of U over S ( socalled conditional distributivity) is given.

We derive conditional Gaussian type identities of the form E [ exp ( i ∫ T 0 utdBt ) ∣∣∣∣ ∫ T 0 |ut|dt ] = exp ( − 2 ∫ T 0 |ut|dt ) , for Brownian stochastic integrals, under conditions on the process (ut)t∈[0,T ] specified using the Malliavin calculus. This applies in particular to the quadratic Brownian integral ∫ t 0 ABsdBs under the matrix condition A †A2 = 0, using a characterization of Yo...

Motivated by the application of seismic inversion in the petroleum industry we consider a hidden Markov model with two hidden layers. The bottom layer is a Markov chain and given this the variables in the second hidden layer are assumed conditionally independent and Gaussian distributed. The observation process is assumed Gaussian with mean values that are linear functions of the second hidden ...

The recovery of gradients of sparsely observed functional data is a challenging ill-posed inverse problem. Given observations of smooth curves (e.g., growth curves) at isolated time points, the aim is to provide estimates of the underlying gradients (or growth velocities). To address this problem, we develop a Bayesian inversion approach that models the gradient in the gaps between the observat...

Multiple imputation is a practically useful approach to handling incompletely observed data in statistical analysis. Parameter estimation and inference based on imputed full data have been made easy by Rubin's rule for result combination. However, creating proper imputation that accommodates flexible models for statistical analysis in practice can be very challenging. We propose an imputation f...

MIXREGLS is a program which provides estimates for a mixed-effects location scale model assuming a (conditionally) normally-distributed dependent variable. This model can be used for analysis of data in which subjects may be measured at many observations and interest is in modeling the mean and variance structure. In terms of the variance structure, covariates can by specified to have effects o...

This paper presents a case study in longitudinal data analysis where the goal is to estimate the efficacy of a new drug for treatment of MDD. Data characteristic indicate: 1. Subjects from different treatment groups drop out differentially across time. 2. There are a high proportion of subjects who never experience any response. To overcome these challenges, we developed a logistic random-effec...