A Robust High-Dimensional Estimation of Multinomial Mixture Models

DOI

10.2991/jsta.d.210126.001How to use a DOI?

Keywords

EM algorithm; Data corruption; High-dimensional; Multinomial logistic mixture models; Robustness

Abstract

In this paper, we are concerned with a robustifying high-dimensional (RHD) structured estimation in finite mixture of multinomial models. This method has been used in many applications that often involve outliers and data corruption. Thus, we introduce a class of the multinomial logistic mixture models for dependent variables having two or more discrete categorical levels. Through the optimization with the expectation maximization (EM) algorithm, we study two distinct ways to overcome sparsity in finite mixture of the multinomial logistic model; i.e., in the parameter space, or in the output space. It is shown that the new method is consistent for RHD structured estimation. Finally, we will implement the proposed method on real data.

Copyright

© 2021 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Azam Sabbaghi
AU  - Farzad Eskandari
AU  - Hamid Reza Navabpoor
PY  - 2021
DA  - 2021/02/08
TI  - A Robust High-Dimensional Estimation of Multinomial Mixture Models
JO  - Journal of Statistical Theory and Applications
SP  - 21
EP  - 32
VL  - 20
IS  - 1
SN  - 2214-1766
UR  - https://doi.org/10.2991/jsta.d.210126.001
DO  - 10.2991/jsta.d.210126.001
ID  - Sabbaghi2021
ER  -