Palestras e Seminários

07/02/2018

16:00

Auditório 2 da Biblioteca Comunitária da UFSCar

Palestrante: Jorge Luis Bazán Guzmán

Responsável: Mariana Curi (Este endereço de email está sendo protegido de spambots. Você precisa do JavaScript ativado para vê-lo.)

Salvar atividade no Google Calendar Latent variable modeling - 6th Workshop on Probabilistic and Statistical Methods

Abstract: The deterministic inputs, noisy ``and'' gate (DINA) model is a popular Cognitive Diagnosis Model (CDM) in psychology and psychometrics used to identify test takers' profiles with respect to a set of latent attributes or skills. In this work we propose an estimation method for the DINA model with the No-U-Turn Sampler (NUTS) algorithm, an extension to Hamiltonian Monte Carlo (HMC) method. We conduct a simulation study in order to evaluate the parameter recovery and efficiency of this new Markov chain Monte Carlo method and to compare it with two other Bayesian methods, the Metropolis Hastings and Gibbs sampling algorithms, and with a frequentist method, using the Expectation-Maximization algorithm. The results indicated that NUTS algorithm employed in the DINA model properly recovers all parameters and is more accurate than the other known methods used in the comparison. We apply this methodology in the mental health area in order to develop a new method of classification for respondents to the Beck Depression Inventory. The implementation of this method for the DINA model applied to other psychological tests has the potential to improve the medical diagnostic process.

Obs: Palestra aberta ao público (não é necessário se inscrever no Workshop).

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