For an interest parameterψ(θ) the Bayesian method eliminates the nuisance parameterλ(θ) by integrating with respect to a conditional prior for λ given ψ. This conditional prior may be difficult to specify in both the subjective and objective Bayesian contexts. We propose the use of results from likelihood theory that give highly accurate third order determinations of various marginal distributi...

Philosophers investigating the interpretation and use of conditional sentences have long been intrigued by the intuitive correspondence between the probability of a conditional ‘if A, then C’ and the conditional probability of C, given A. Attempts to account for this intuition within a general probabilistic theory of belief, meaning and use have been plagued by a danger of trivialization, which...

We deene new procedures for estimating generalized additive nonparametric regression models that are more eecient than the Linton and HHrdle (1996) integration-based method and achieve certain oracle bounds. We consider criterion functions based on the Linear Exponential Family, which includes many important special cases. We also consider the extension to multiple parameter models like the Gam...

In this work, an algorithm to compute the first excursion probability of a dynamic response for uncertain linear systems subjected to general stochastic Gaussian excitation, is presented. The excitation is, besides of being Gaussian, described quite generally by a finite number of deterministic functions of a Karhunen-Loève expansion [1, 2] which allows to represent any type of second moment ch...

conditional  methods  of adjustment  are often used to quantify  the effect  of the exposure on the outcome.  as  a  result,  the  stratums-specific  risk  ratio  estimates  are  reported  in  the  presence  of interaction   between   exposure  and  confounder(s)   in  the  literature,  even  if  the  target  of  the intervention on the exposure is the total population and the interaction itsel...

The integration of uncertain information from different time sources is a crucial issue in various applications. In this paper, we propose an integration method of multiple Temporal Qualitative Probabilistic Networks (TQPNs) in time series environments. First, we present the method for learning TQPN from time series data. The TQPN’s structure is constructed using Dynamic Bayesian Networks learn...

We analyse the determinants of stock market integration among EU member states for the period 1999–2007. First, we apply bivariate DCC-MGARCH models to extract dynamic conditional correlations between European stock markets, which are then explained by interest rate spreads, exchange rate risk, market capitalisation, and business cycle synchronisation in a pooled OLS model. By grouping the coun...

Tremendous worldwide genetic resources of mice represent a unique resource for the biomedical community, including inbred mice, spontaneous and induced mutants, and a large resource of conditional gene targeting ready ES-cell lines. Recently, international efforts are ongoing toward the completion of functional annotation the mouse whole genome, which furnishing systems view of mammalian biolog...