Sliced Inverse Regression (SIR) is an effective method for dimension reduction in high-dimensional regression problems. The original method, however, requires the inversion of the predictors covariance matrix. In case of collinearity between these predictors or small sample sizes compared to the dimension, the inversion is not possible and a regularization technique has to be used. Our approach...

A two-factor experiment with interaction between factors wherein observations follow an Inverse Gaussian model is considered. Analysis of the experiment is approached via an empirical Bayes procedure. The conjugate family of prior distributions is considered. Bayes and empirical Bayes estimators are derived. Application of the procedure is illustrated on a data set, which has previously been an...

In order to have more flexibility, several double distributions been studied in the literature. this paper, we propose a inverse Gaussian distribution using random sign mixture transform and study its associated inferences. The maximum likelihood estimation is performed estimate parameters. Extensive simulation studies are carried out examine performance of estimators corresponding confidence i...

Viewing the Matsumoto–Yor property as a bivariate property with respect to the simple tree with two vertices and one edge, we extend it to a p-variate property with respect to any tree with p vertices. The converse of the Matsumoto–Yor property, which characterizes the product of a gamma and a generalized inverse Gaussian distribution, is extended to characterize the product of a gamma and p 1 ...

In many linear inverse problems the unknown function f (or its discrete approximation θp×1), which needs to be reconstructed, is subject to the nonnegative constraint(s); we call these problems the nonnegative linear inverse problems (NNLIPs). This paper considers NNLIPs. However, the error distribution is not confined to the traditional Gaussian or Poisson distributions. We adopt the exponenti...

In Diffusion-based Molecular Communications, the channel between Transmitter Nano-machine (TN) and Receiver Nano-machine (RN) can be modeled by Additive Inverse Gaussian Channel, that is the first hitting time of messenger molecule released from TN and captured by RN follows Inverse Gaussian distribution. In this channel, a quantity-based modulation embedding message on the different quantity l...

In this paper, an adaptive inverse Gaussian stochastic process is developed to characterize the degradation process of condition monitored components. The knowledge of the degradation process is updated through the parameter of the process when new observations are available. The updating is performed through a general Bayesian filtering process within a state space model setting. The proposed ...

Gaussian graphical models are of great interest in statistical learning. Because the conditional independencies between different nodes correspond to zero entries in the inverse covariance matrix of the Gaussian distribution, one can learn the structure of the graph by estimating a sparse inverse covariance matrix from sample data, by solving a convex maximum likelihood problem with an l1-regul...