How to include censored data in a statistical analysis is a recurrent issue in statistics. In multivariate extremes, the dependence structure of large observations can be characterized in terms of a non parametric angular measure, while marginal excesses above asymptotically large thresholds have a parametric distribution. In this work, a flexible semi-parametric Dirichlet mixture model for ang...

background: growth failure in children less than five years old can lead to the serious complications such as increased mortality, learning difficulties or physical disability. the aim of this study was to investigate the non-organic factors affecting the growth trend in less than two years children living in zanjan, iran.   methods: this longitudinal study was conducted on a sample of 3566 chi...

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multi...

where u ∼ N(0, G) and 2 ∼ N(0, R) with u and 2 independent. This model can be interpreted as Y |β, u ∼ N(Xβ+Zu,R), u ∼ N(0, G), yielding (on integrating out u) the marginal model Y |β ∼ N(Xβ,ZGZ + R). Let V = ZGZ + R. Then the maximum penalized likelihood estimates of β and u given G and R are given by θ̂ = [ β̂ û ] = (CTR−1C + B)−1CTR−1y (A.2) where C = [X Z] and B is the block diagonal matrix w...

Longitudinal studies, where data are repeatedly collected on one subject over a period, are common in medical research. When effect of a time-varying exposure on an outcome of interest is measured at different time points, standard statistical methods fail to give robust estimate in the presence of time-dependent confounders. There is alternative method avoid, that is, inverse probability weigh...

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According to the authors, time-modified confounding occurs when the causal relation between a time-fixed or time-varying confounder and the treatment or outcome changes over time. A key difference between previously described time-varying confounding and the proposed time-modified confounding is that, in the former, the values of the confounding variable change over time while, in the latter, t...

In studies of complex health conditions, mixtures of discrete outcomes (event time, count, binary, ordered categorical) are commonly collected. For example, studies of skin tumorigenesis record latency time prior to the first tumor, increases in the number of tumors at each week, and the occurrence of internal tumors at the time of death. Motivated by this application, we propose a general unde...

Several Monte–Carlo methods have been proposed for computing marginal likelihoods in Bayesian analyses. Some of these involve sampling from a sequence of intermediate distributions between the prior and posterior. A difficulty arises if the support in the posterior distribution is a proper subset of that in the prior distribution. This can happen in problems involving latent variables whose sup...