A note on identifiability and maximum likelihood estimation for a heterogeneous capture-recapture model

Pezzott, George Lucas Moraes; Salasar, Luis Ernesto Bueno; Leite, José Galvâo; Louzada-Neto, Francisco

doi:10.1080/03610926.2019.1615628

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…We will first show that if 2J ≤ K, then Q J is identifiable. The proof of this direction is similar in spirit to the proofs of Theorem 2 in Holzmann et al (2006) and Theorem 1 in Pezzott et al (2019), which were both concerned with characterizing the identifiability of the M h analogue of Q J . Assume 2J ≤ K, and let Q, R ∈ Q J such that m Q = Am R for some A > 0, so that we have the following system of equations:…”

Section: The Answermentioning

confidence: 93%

On the Identifiability of Latent Class Models for Multiple-Systems Estimation

Aleshin-Guendel

2020

Preprint

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Latent class models have recently become popular for multiple-systems estimation in human rights applications. However, it is currently unknown when a given family of latent class models is identifiable in this context. We provide necessary and sufficient conditions on the number of latent classes needed for a family of latent class models to be identifiable. Along the way we provide a mechanism for verifying identifiability in a class of multiple-systems estimation models that allow for individual heterogeneity.

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Section: The Answermentioning

confidence: 93%

On the Identifiability of Latent Class Models for Multiple-Systems Estimation

Aleshin-Guendel

2020

Preprint

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“…e identifiability property has been explored by a variety of authors [12,[16][17][18][19][20][21][22][23][24][25][26][27].…”

Section: Identifiability Of the Finite Mixture Of Ttigl Distributionsmentioning

confidence: 99%

Estimation Using Suggested EM Algorithm Based on Progressively Type-II Censored Samples from a Finite Mixture of Truncated Type-I Generalized Logistic Distributions with an Application

Ateya

Kilai

Aldallal

2022

Mathematical Problems in Engineering

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In this paper, the identifiability property has been studied for a suggested truncated type-I generalized logistic mixture model which is denoted by TTIGL . A suggested form of the EM algorithm has been applied on type-II progressive censored samples to obtain the maximum likelihood estimates MLE ′ s of the parameters, survival function SF , and hazard rate function HRF of the studied mixture model. Monte Carlo simulation algorithm has been applied to study the behavior of the mean squares errors MSE ′ s of the estimates. Also, a comparative study is conducted between the suggested EM algorithm and the ordinary algorithm of maximizing the likelihood function, which depends on the differentiation of the log likelihood function. The results of this paper have been applied on a real dataset as an application.

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