This paper presents a speech-model using the Linear Predictive (LP) residual signal and Maximum Likelihood Estimator (MLE). With this model an accuracy of the reverberation time estimation can be improved. During past decade, the reverberation time estimation was performed using only maximum likelihood detector, which resulted in excess time of estimation. For the purpose of estimating room aco...

We study and compare three estimators of a discrete monotone distribution: (a) the (raw) empirical estimator; (b) the "method of rearrangements" estimator; and (c) the maximum likelihood estimator. We show that the maximum likelihood estimator strictly dominates both the rearrangement and empirical estimators in cases when the distribution has intervals of constancy. For example, when the distr...

When dealing with a regular (fixed-support) one-parameter distribution, the corresponding maximum-likelihood estimator (MLE) is, to good approximation, normally distributed. But, when support boundaries are functions of parameter, finding approximation for sampling distribution MLE (needed construct an accurate confidence interval parameter’s true value) may get very challenging. We demonstrate...

This module introduces the maximum likelihood estimator. We show how the MLE implements the likelihood principle. Methods for computing th MLE are covered. Properties of the MLE are discussed including asymptotic e ciency and invariance under reparameterization. The maximum likelihood estimator (MLE) is an alternative to the minimum variance unbiased estimator (MVUE). For many estimation proble...

Determinantal point processes (DPPs) have wide-ranging applications in machine learning, where they are used to enforce the notion of diversity in subset selection problems. Many estimators have been proposed, but surprisingly the basic properties of the maximum likelihood estimator (MLE) have received little attention. The difficulty is that it is a non-concave maximization problem, and such f...

Abstract: We introduce the beta model for random hypergraphs in order to represent the occurrence of multi-way interactions among agents in a social network. This model builds upon and generalizes the well-studied beta model for random graphs, which instead only considers pairwise interactions. We provide two algorithms for fitting the model parameters, IPS (iterative proportional scaling) and ...

Abstract: We introduce the beta model for random hypergraphs in order to represent the occurrence of multi-way interactions among agents in a social network. This model builds upon and generalizes the well-studied beta model for random graphs, which instead only considers pairwise interactions. We provide two algorithms for fitting the model parameters, IPS (iterative proportional scaling) and ...

In this paper, we are interested in the numerical computation of constrained maximum likelihood estimator (MLE) parameter vector a discrete statistical model used statistics applied to road safety. The is divided into two blocks: one block with interest and second secondary parameters. MLE solution system non-linear implicit equations difficult solve closed-form. To overcome difficulty, propose...

Maximum likelihood is the most widely used statistical estimation technique. Recent work by Jiao, Venkat, Han, and Weissman [1] introduced a general methodology for the construction of estimators for functionals in parametric models, and demonstrated improvements both in theory and in practice over the maximum likelihood estimator (MLE), particularly in high dimensional scenarios involving para...

Estimating the cardinality (i.e. number of distinct elements) of an arbitrary set expression defined over multiple distributed streams is one of the most fundamental queries of interest. Earlier methods based on probabilistic sketches have focused mostly on the sketching algorithms. However, the estimators do not fully utilize the information in the sketches and thus are not statistically effic...