Estimation of Dirichlet distribution parameters with bias-reducing adjusted score functions

03/03/2021
by   Vincenzo Gioia, et al.
0

The Dirichlet distribution, also known as multivariate beta, is the most used to analyse frequencies or proportions data. Maximum likelihood is widespread for estimation of Dirichlet's parameters. However, for small sample sizes, the maximum likelihood estimator may shows a significant bias. In this paper, Dirchlet's parameters estimation is obtained through modified score functions aiming at mean and median bias reduction of the maximum likelihood estimator, respectively. A simulation study and an application compare the adjusted score approaches with maximum likelihood.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

04/18/2020

Efficient implementation of median bias reduction

In numerous regular statistical models, median bias reduction (Kenne Pag...
06/12/2020

Fast Maximum Likelihood Estimation and Supervised Classification for the Beta-Liouville Multinomial

The multinomial and related distributions have long been used to model c...
05/06/2019

Maximum likelihood (ML) estimators for scaled mutation parameters with a strand symmetric mutation model in equilibrium

With the multiallelic parent-independent mutation-drift model, the equil...
11/05/2020

Accurate inference in negative binomial regression

Negative binomial regression is commonly employed to analyze overdispers...
02/14/2020

Upper and Lower Class Functions for Maximum Likelihood Estimator for Single server Queues

Upper and lower class functions for the maximum likelihood estimator of ...
10/08/2018

Tilting maximum Lq-Likelihood estimation for extreme values drawing on block maxima

One of the most common anticipated difficulties in applying mainstream m...
04/22/2019

A Maximum Entropy Procedure to Solve Likelihood Equations

In this article we provide initial findings regarding the problem of sol...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.