We consider n observations from the GARCH-type model: S = σ2Z, where σ2 and Z are independent random variables. We develop a new wavelet linear estimator of the unknown density of σ2 under four different dependence structures: the strong mixing case, the β- mixing case, the pairwise positive quadrant case and the ρ-mixing case. Its asymptotic mean integrated squared error properties are ...

Most of previous works and applications Bayesian factor model have assumed the normal likelihood regardless its validity. We propose a for heavy-tailed high-dimensional data based on multivariate Student-t to obtain better covariance estimation. use multiplicative gamma process shrinkage prior number adaptation scheme proposed in Bhattacharya Dunson [Biometrika 98(2):291–306, 2011]. Since naive...

Source separation, one of the most recent domain of signal processing, consists in recovering signals mixed by an unknown transmission channel. Many approaches manage the separation using different techniques such as likelihood and information theory ([1],[6]) or high order statistics ([7]). This paper proposes a Bayesian approach to the problem of an instantaneous linear mixing, considering th...

Abstract Purpose Lake Victoria has been increasingly silting over the past decades, impacting water quality and loss of biodiversity. Sediment control strategies require information on relative absolute contributions sediment from different sources. However, to date, there is no continuous monitoring flux or in any tributaries, prohibiting an assessment scale problem. The aim this study was tra...

Basically, medical diagnosis problems are the most effective component of treatment policies. Recently, significant advances have been formed in medical diagnosis fields using data mining techniques. Data mining or Knowledge Discovery is searching large databases to discover patterns and evaluate the probability of next occurrences. In this paper, Bayesian Classifier is used as a Non-linear dat...

A generic algorithm for the extraction of probabilistic (Bayesian) information about model parameters from data is presented. The algorithm propagates an ensemble of particles in the product space of model parameters and outputs. Each particle update consists of a random jump in parameter space followed by a simulation of a model output and a Metropolis acceptance/rejection step based on a comp...

Following a Bayesian statistical inference paradigm, we provide an alternative methodology for analyzing a multivariate logistic regression. We use a multivariate normal prior in the Bayesian analysis. We present a unique Bayes estimator associated with a prior which is admissible. The Bayes estimators of the coefficients of the model are obtained via MCMC methods. The proposed procedure...