In economics and social science, network data are regularly observed, a thorough understanding of the community structure facilitates comprehension economic patterns activities. Consider an undirected with n nodes K communities. We model using Degree-Corrected Mixed-Membership (DCMM) model, where for each node i=1,2,…,n, there exists membership vector πi=(πi(1),πi(2),…,πi(K))′, πi(k) is weight ...

Intensity in homogeneities cause considerable difficulties in the quantitative analysis of Magnetic Resonance (MR) images. Thus intensity in homogeneities estimation is a necessary step before quantitative analysis of MR data can be undertaken. This paper proposes a new energy minimization framework for simultaneous estimation of the intensity in homogeneities and segmentation. The intensity in...

In this paper, a maximum likelihood estimation and a minimum entropy estimation for the expected value and variance of normal fuzzy variable are discussed within the framework of credibility theory. As an application, a credibilistic portfolio selection model is proposed, which is an improvement over the traditional models as it only needs the predicted values on the security returns instead of...

Let θ and μ denote the location and the size of the mode of a probability density. We study the joint convergence rates of semirecursive kernel estimators of θ and μ. We show how the estimation of the size of the mode allows measuring the relevance of the estimation of its location. We also enlighten that, beyond their computational advantage on nonrecursive estimators, the semirecursive estima...

There are mainly two approaches to the state estimation or the system identification problems, where the system contains additive unknown values. One is the well-known stochastic approach, and the other is the setmembership approach, where the unknown values are treated as unknown deterministic values in bounded sets. In this paper, we consider the state estimation of linear systems when the in...

This paper takes a Bayesian-decision theoretic approach to transfer function estimation, nominal model estimation, and quantification of the resulting model error. Consistency of the nonparametric estimate of the transfer function is proved together with a rate of convergence. The required quantities can be computed routinely using reversible jump Markov chain Monte Carlo methods. The proposed ...

Uncertain set theory is a generalization of uncertainty theory that has become a new branch of mathematics for modeling human belief degrees. Uncertain set is a fundamental concept to describe unsharp concepts in uncertain set theory. The moments are important characteristics of an uncertain set. This paper studies the moments and central moments of uncertain set and gives some formulas to calc...