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Alcohol and tobacco consumption are closely correlated and published results on their association with breast cancer have not always allowed adequately for confounding between these exposures. Over 80% of the relevant information worldwide on alcohol and tobacco consumption and breast cancer were collated, checked and analysed centrally. Analyses included(More)
We derive the probability density of the ratio of components of the bivariate normal distribution with arbitrary parameters. The density is a product of two factors, the first is a Cauchy density, the second a very complicated function. We show that the distribution under study does not possess an expected value or other moments of higher order. Our(More)
To evaluate the role of pregnancy in the pathogenesis and clinical course of Hodgkin's disease (HD), we studied a series of 192 female patients aged 17-50 years at the time of diagnosis, and 496 healthy controls matched by residence and year of birth. Cases showed a marginally significant excess for the father having a high level of education, and more(More)
In the last two decades, principal component analysis (PCA) was extended to interval-valued data; several adaptations of the classical approach are known from the literature. Our approach is based on the symbolic covariance matrix Cov for the interval-valued variables proposed by Billard (2008). Its crucial advantage, when compared to other approaches, is(More)
The study analyzes the components of advertising spending for a group of European countries with stable total advertising spending over the period 1994-2007. Three components of advertising spending were considered: Electronic, Print, and Online. Our main objective was to study how the components were restructured within the period under study and to find(More)
We introduced a new bivariate distribution for a random vector Z = [X, Y]<sup>T</sup>, where X and Y are positive continuous variables. The distribution is a generalization of the Weibull distribution, it has 7 parameters: r and s are power parameters, m and n are moment parameters, a and b are scaling parameters, and p is the linking parameter. The ML(More)
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