We study maximum likelihood estimation for the statistical model for both directed and undirected random graph models in which the degree sequences are minimal sufficient statistics. In the undirected case, the model is known as the beta model. We derive necessary and sufficient conditions for the existence of the MLE that are based on the polytope of degree sequences. We characterize in a comb...

It is well known that the normal mixture with unequal variance has unbounded likelihood and thus the corresponding global maximum likelihood estimator (MLE) is undefined. One of the commonly used solutions is to put a constraint on the parameter space so that the likelihood is bounded and then one can run the EM algorithm on this constrained parameter space to find the constrained global MLE. H...

DNA sequences covering 36% of the mle gene that encodes the malolactic enzyme were determined for 13 strains of lactic acid bacteria, representing Pediococcus, Leuconostoc, Lactobacillus and Oenococcus genera. The sequences were aligned with the corresponding region of mleS in Lactococcus lactis. The phylogenetic distance matrix tree of all mle sequences was compared with the 16S rRNA phylogene...

In this paper, distributed maximum likelihood estimation (MLE) with quantized data is considered under the assumption that the structure of the joint probability density function (pdf) is known, but it contains unknown deterministic parameters. The parameters may include different vector parameters corresponding to marginal pdfs and parameters that describe dependence of observations across sen...

We describe a novel algorithm called k-Maximum Likelihood Estimator (k-MLE) for learning finite statistical mixtures of exponential families relying on Hartigan’s k-means swap clustering method. To illustrate this versatile Hartigan k-MLE technique, we consider the exponential family of Wishart distributions and show how to learn their mixtures. First, given a set of symmetric positive definite...

We consider a particular maximum likelihood estimator (MLE) and a computationally-intensive Bayesian method for differentially private estimation of the linear mixed-effects model (LMM) with normal random errors. The LMM is important because it is used in small area estimation and detailed industry tabulations that present significant challenges for confidentiality protection of the underlying ...

Postprandial hypoglycemic effect of mulberry leaf (Morus alba L.) was compared in two animal models: Goto-Kakizaki (GK) rats, a spontaneous non-obese animal model for type II diabetes, and their counterpart control Wistar rats. First, the effect of a single oral administration of mulberry leaf aqueous extract (MLE) on postprandial glucose responses was determined using maltose or glucose as sub...

Abstract. In this paper we consider some four-parametric, so-called Generalized Pareto-like Frequency Distribution, which have been constructed using stochastic Birth-Death Process in order to model phenomena arising in Bioinformatics (Astola and Danielian, 2007). As examples, two ”real data” sets on the number of proteins and number of residues for analyzing such distribution are given. The co...