Statistical procedures for the equality of two and k univariate distributions based on samples of dependent observations are proposed in this work. The test statistics are L2 distances of standard empirical and characteristic function processes. The p-values of the tests are obtained from a...

Studies of ocular disease and analyses of time to disease onset are complicated by the correlation expected between the two eyes from a single patient. We overcome these statistical modeling challenges through a nonparametric Bayesian frailty model. While this model suggests itself as a natural...

There is strong empirical evidence that dependence in multivariate financial time series varies over time. To model this effect, a time varying copula class is developed, which is called the stochastic copula autoregressive (SCAR) model. Dependence at time t is modeled by a real-valued latent...

In this paper, we generalize the single-index models to the scenarios with random effects. The introduction of the random effects raises interesting inferential challenges. Instead of treating the variance matrix as the tuning parameters in the nonparametric model of Gu and Ma (2005), we propose...

Suppose that one can observe bivariate random variables (L,X) only when L≤X holds. Such data are called left-truncated data and found in many fields, such as experimental education and epidemiology. Recently, a method of fitting a parametric model on (L,X) has been considered, which can easily...

Multiple testing analysis and clustering methodologies are usually applied in microarray data analysis. A combination of both methods to deal with multiple comparisons among groups obtained from microarray expressions of genes is proposed. Assuming normal data, a statistic which depends on...

A standard approach to analyzing n binary matched pairs usually represented in n 2×2 tables is to apply a subject-specific model; for the simplest situation it is the so-called Rasch model. An alternative population-averaged approach is to apply a marginal model to the single 2×2 table formed...

Covariate misclassification is a common problem in epidemiology, genetics, and other biomedical areas. Because this form of misclassification is known to bias estimators, accounting for it at the design stage is of high importance. In this paper, we extend on previous work applied to response...

In diagnostic methods evaluation, analysts commonly focus on the relative size of the treatment difference (ratio of marginal probabilities) between a new and an existing procedures. To assess non-inferiority (a new procedure is, to a pre-specified amount, no worse than an existing procedure)...

Inference in state-space models usually relies on recursive forms for filtering and smoothing of the state vectors regarding the temporal structure of the observations, an assumption that is, from our view point, unnecessary if the dataset is fixed, that is, completely available before analysis....