This paper considers logistic regression analysis with linked data. It is shown that, in logistic regression analysis with linked data, a finite mixture of Bernoulli distributions can be used for modeling the response variables. We proposed an iterative maximum likelihood estimator for the regression coefficients that takes the matching probabilities into account. Next, the Bayesian counterpart...

This work considers variational Bayesian inference as an inexpensive and scalable alternative to a fully approach in the context of sparsity-promoting priors. In particular, priors considered arise from scale mixtures Normal distributions with generalized inverse Gaussian mixing distribution. includes LASSO introduced [65]. It also family which more strongly promote sparsity. For linear models ...

We propose a vector-valued regression problem whose solution is equivalent to the reproducing kernel Hilbert space (RKHS) embedding of the Bayesian posterior distribution. This equivalence provides a new understanding of kernel Bayesian inference. Moreover, the optimization problem induces a new regularization for the posterior embedding estimator, which is faster and has comparable performance...

Introduction: In addition to localizing task-related cortical and subcortical activity, FMRI data can be used to model various forms of interactions among active regions of interest (ROIs) in the brain. Granger causality (GC) is a connectivity modeling approach with the following premises: (a) a region causally connected to another would leave in the latter a time-lagged version of its own sign...

With the development of information technology, E-business becomes the core competition of small and medium enterprises in recent years. In this paper, we make an empirical test to analyze how the application of E-business will effect on the business performance. The result shows that the E-business application index increased 1 percentage can drive sales volume increased by 0.22 percentages, s...