4,839 research outputs found
A Review on Joint Models in Biometrical Research
In some fields of biometrical research joint modelling of longitudinal measures and event time data has become very popular. This article reviews the work in that area of recent fruitful research by classifying approaches on joint models in three categories: approaches with focus on serial trends, approaches with focus on event time data and approaches with equal focus on both outcomes. Typically longitudinal measures and event time data are modelled jointly by introducing shared random effects or by considering conditional distributions together with marginal distributions. We present the approaches in an uniform nomenclature, comment on sub-models applied to longitudinal measures and event time data outcomes individually and exemplify applications in biometrical research
Bayesian semiparametric inference for multivariate doubly-interval-censored data
Based on a data set obtained in a dental longitudinal study, conducted in
Flanders (Belgium), the joint time to caries distribution of permanent first
molars was modeled as a function of covariates. This involves an analysis of
multivariate continuous doubly-interval-censored data since: (i) the emergence
time of a tooth and the time it experiences caries were recorded yearly, and
(ii) events on teeth of the same child are dependent. To model the joint
distribution of the emergence times and the times to caries, we propose a
dependent Bayesian semiparametric model. A major feature of the proposed
approach is that survival curves can be estimated without imposing assumptions
such as proportional hazards, additive hazards, proportional odds or
accelerated failure time.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS368 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Extreme-Value Copulas
Being the limits of copulas of componentwise maxima in independent random
samples, extreme-value copulas can be considered to provide appropriate models
for the dependence structure between rare events. Extreme-value copulas not
only arise naturally in the domain of extreme-value theory, they can also be a
convenient choice to model general positive dependence structures. The aim of
this survey is to present the reader with the state-of-the-art in dependence
modeling via extreme-value copulas. Both probabilistic and statistical issues
are reviewed, in a nonparametric as well as a parametric context.Comment: 20 pages, 3 figures. Minor revision, typos corrected. To appear in F.
Durante, W. Haerdle, P. Jaworski, and T. Rychlik (editors) "Workshop on
Copula Theory and its Applications", Lecture Notes in Statistics --
Proceedings, Springer 201
Estimation of a regression spline sample selection model
It is often the case that an outcome of interest is observed for a restricted non-randomly selected sample of the population. In such a situation, standard statistical analysis yields biased results. This issue can be addressed using sample selection models which are based on the estimation of two regressions: a binary selection equation determining whether a particular statistical unit will be available in the outcome equation. Classic sample selection models assume a priori that continuous regressors have a pre-specified linear or non-linear relationship to the outcome, which can lead to erroneous conclusions. In the case of continuous response, methods in which covariate effects are modeled flexibly have been previously proposed, the most recent being based on a Bayesian Markov chain Monte Carlo approach. A frequentist counterpart which has the advantage of being computationally fast is introduced. The proposed algorithm is based on the penalized likelihood estimation framework. The construction of confidence intervals is also discussed. The empirical properties of the existing and proposed methods are studied through a simulation study. The approaches are finally illustrated by analyzing data from the RAND Health Insurance Experiment on annual health expenditures
- …