On Finite Mixture Modeling of Change-point Processes

Zhu, Xuwen; Melnykov, Yana

doi:10.1007/s00357-021-09385-6

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…Then, such data have been used for model-based clustering via matrix-variate mixture models (see e.g. Melnykov and Zhu 2019;Tomarchio et al 2022and Zhu and Melnykov 2021. This allows for both clustering units in homogeneous groups, defined according to similarities between matrixvariate data, and separately modeling the association between variables and times.…”

Section: Introductionmentioning

confidence: 99%

Parsimonious hidden Markov models for matrix-variate longitudinal data

2022

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Hidden Markov models (HMMs) have been extensively used in the univariate and multivariate literature. However, there has been an increased interest in the analysis of matrix-variate data over the recent years. In this manuscript we introduce HMMs for matrix-variate balanced longitudinal data, by assuming a matrix normal distribution in each hidden state. Such data are arranged in a four-way array. To address for possible overparameterization issues, we consider the eigen decomposition of the covariance matrices, leading to a total of 98 HMMs. An expectation-conditional maximization algorithm is discussed for parameter estimation. The proposed models are firstly investigated on simulated data, in terms of parameter recovery, computational times and model selection. Then, they are fitted to a four-way real data set concerning the unemployment rates of the Italian provinces, evaluated by gender and age classes, over the last 16 years.

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Section: Introductionmentioning

confidence: 99%

Parsimonious hidden Markov models for matrix-variate longitudinal data

2022

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Multiple change point clustering of count processes with application to California COVID data

Sarkar

Zhu

2022

Pattern Recognition Letters

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A Mixture Model for Random Responding Behavior in Forced-Choice Noncognitive Assessment: Implication and Application in Organizational Research

Peng

Man

Veldkamp

et al. 2023

Organizational Research Methods

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For various reasons, respondents to forced-choice assessments (typically used for noncognitive psychological constructs) may respond randomly to individual items due to indecision or globally due to disengagement. Thus, random responding is a complex source of measurement bias and threatens the reliability of forced-choice assessments, which are essential in high-stakes organizational testing scenarios, such as hiring decisions. The traditional measurement models rely heavily on nonrandom, construct-relevant responses to yield accurate parameter estimates. When survey data contain many random responses, fitting traditional models may deliver biased results, which could attenuate measurement reliability. This study presents a new forced-choice measure-based mixture item response theory model (called M-TCIR) for simultaneously modeling normal and random responses (distinguishing completely and incompletely random). The feasibility of the M-TCIR was investigated via two Monte Carlo simulation studies. In addition, one empirical dataset was analyzed to illustrate the applicability of the M-TCIR in practice. The results revealed that most model parameters were adequately recovered, and the M-TCIR was a viable alternative to model both aberrant and normal responses with high efficiency.

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On Finite Mixture Modeling of Change-point Processes

Cited by 3 publications

References 32 publications

Parsimonious hidden Markov models for matrix-variate longitudinal data

Parsimonious hidden Markov models for matrix-variate longitudinal data

Multiple change point clustering of count processes with application to California COVID data

A Mixture Model for Random Responding Behavior in Forced-Choice Noncognitive Assessment: Implication and Application in Organizational Research

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