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Copula models are becoming increasingly popular for modelling dependencies between random variables. The range of their recent applications includes such fields as analysis of extremes in financial assets and returns; failure of paired organs in health science; reliability studies; and human mortality in insurance.This paper gives a brief overview of the principles of construction of such copula models as Gaussian, Student, and Archimedean. The latter includes Frank, Clayton, and stable (Gumbel-Hougaard) families. The emphasis is on application of copula models to joint last survivor analysis.The main example discussed in this paper deals with the mortality of spouses, known to be associated through such factors as common disaster, common lifestyle, or the broken-heart syndrome. These factors suggest modelling dependence of spouses' lives on both calendar date scale and age-at-death scale. This dependence structure suggests a different treatment than that for problems of survival analysis such as paired organ failure or twins' mortality.Construction of a conditional Bayesian copula model is further generalized in view of the relationship between the joint first life and last surviror probabilities. A numerical example is considered, involving the implementation of Markov chain Monte Carlo algorithms using WinBUGs. Maximum likelihood estimation for this model using (5) is carried out in Reference [3], whereFrank's copula and Gompertz marginals are also considered. Bayesian estimation with exponential and normal priors for Weibull distribution parameters, beta prior for the association, and the copula functions of three forms: stable, Frank's and Clayton's, is performed in Reference [18].This approach is effective. However, it sometimes proves to be insufficient depending on the type of association between the paired lives. Apparently, the source of this insufficiency is the problem of dimensionality. We use a bivariate survival function Sðx 1 ; x 2 Þ to model the behaviour of trivariate joint first life and last survivor functions. Why are these functions trivariate?

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Purpose -The purpose of this paper is to conduct comparative research of small innovative entrepreneurship under different types of institutional environment in Russia and the USA. Design/methodology/approach -A survey was administered among small innovative firms in the State of Minnesota (USA) and Novosibirsk Oblast (Russia). Mann-Whitney test for median differences adjusted for multiple comparisons using Benjamini-Hochberg procedure is used to establish statistically significant dissimilarities between Siberian and Minnesotan populations. Findings -The results indicate that there are significant differences in the challenges faced by the Russian and American firms. The most important among them are the lack of legal structure for innovation and availability of qualified staff in Russia.Research limitations/implications -The study is limited to two regions with comparable economic and geographic environments. Practical implications -It is indicated in the results that significant changes in institutional business environment are necessary for the future development of innovative entrepreneurship in Russia. Originality/value -This study is the first of its kind to compare the challenges facing small innovative entrepreneurship in Russia and the USA.