This paper describes the properties of a two-stage estimator of the dependence parameter in the Clayton-Oakes multivariate failure time model. The parameter is estimated from a likelihood function in which the marginal hazard functions are replaced by estimates. The method extends the approach of Shih and Louis (1995) and Genest, Ghoudi and Rivest (1995) to allow the marginal hazard for failure...

We evaluate the effects of data dimension on the asymptotic normality of the empirical likelihood ratio for high-dimensional data under a general multivariate model. Data dimension and dependence among components of the multivariate random vector affect the empirical likelihood directly through the trace and the eigenvalues of the covariance matrix. The growth rates to infinity we obtain for th...

The most frequent measure of phylogenetic uncertainty for splits is bootstrap support. Although large bootstrap support intuitively suggests that a split in a tree is well supported, it has not been clear how large bootstrap support needs to be to conclude that there is significant evidence that a hypothesized split is present. Indeed, recent work has shown that bootstrap support is not first-o...

This article deals with the problem of testing the hypothesis that q p-variate normal distributions are independent and that their covariance matrices are equal. The exact null distribution of the likelihood ratio statistic when q = 2 is obtained using inverse Mellin transform and the definition of Meijer’s G-function. Results for p = 2, 3, 4 and 5 are given in computable series forms.

Often of primary interest in the analysis of multivariate data are the copula parameters describing the dependence among the variables, rather than the univariate marginal distributions. Since the ranks of a multivariate dataset are invariant to changes in the univariate marginal distributions, rank-based estimators are natural candidates for semiparametric copula estimation. Asymptotic informa...

A decision theoretic foundation of the maximum likelihood estimation method is provided. In regular parametric models, the maximum likelihood estimator θ̂ is shown to be finite sample efficient for the parameter θ, with respect to the mean squared error of the scores and within a large class C of estimates; C includes for some of these models the unbiased, as well as the equivariant estimators o...

The joint probability density function, evaluated at the observed data, is commonly used as the likelihood function to compute maximum likelihood estimates. For some models, however, there exist paths in the parameter space along which this densityapproximation likelihood goes to infinity and maximum likelihood estimation breaks down. In applications, all observed data are discrete due to the r...

It has been known since Phillips and Hansen (1990) that cointegrated systems can be consistently estimated using stochastic trend instruments that are independent of the system variables. A similar phenomenon occurs with deterministically trending instruments. The present work shows that such ‘‘irrelevant’’ deterministic trend instruments may be systematically used to produce asymptotically eff...

In modern medicine, sophisticated laboratory tests and imaging studies are often emphasized at the expense of history and physical examination, rather than complementing clinical assessment. Ancillary testing often fails to advance the diagnostic process, and increases patient risk and the expense of medical care. The relative value of clinical evaluation and technological methods is rarely con...