A black-box optimization problem is considered, in which the function to be optimized can only be expressed in terms of a complicated stochastic algorithm that takes a long time to evaluate. The value returned is required to be sufficiently near to a target value, and uses data that has a...

In this paper we introduce a stochastic network formation model where agents choose both actions and links. Neighbors in the network benefit from each other's action levels through local complementarities and there exists a global interaction effect reflecting a strategic substitutability in...

Present paper proposes an autoregressive time series model to study the behaviour of merger and acquire concept which is equally important as other available theories like structural break, detrending etc. The main motivation behind newly proposed merged autoregressive (M-AR) model is to study...

Sequential Monte Carlo (SMC) methods are widely used for non-linear filtering purposes. However, the SMC scope encompasses wider applications such as estimating static model parameters so much that it is becoming a serious alternative to Markov-Chain Monte-Carlo (MCMC) methods. Not only do SMC...

A flexible predictive density combination model is introduced for large financial data sets which allows for dynamic weight learning and model set incompleteness. Dimension reduction procedures allocate the large sets of predictive densities and combination weights to relatively small sets....