R packages

Zucchini, Walter; MacDonald, Iain L.; Langrock, Roland

doi:10.1201/b20790-8

Cited by 54 publications

(128 citation statements)

References 0 publications

Supporting

Mentioning

128

Contrasting

Order By: Relevance

“…Hidden Markov models are used to estimate transition probabilities between categorical variables for time series. Observed values are used to estimate the underlying and unobserved Markov process, also known as a Markov chain, which rests on the assumption that the probability of a current state is dependent on the previous state (Zucchini et al, 2016 ; Muthén and Muthén, 2017 ).…”

Section: Methodsmentioning

confidence: 99%

A Cross-Lagged Study of Developmental Trajectories of Video Game Engagement, Addiction, and Mental Health

et al. 2018

View full text Add to dashboard Cite

Objectives: Video game addiction has been associated with an array of mental health variables. There is a paucity of longitudinal studies investigating such associations, and studies differentiating addicted gaming from problem and engaged (i.e., frequent but non-problem) gaming. The current explorative study investigate the natural course of gaming behavior in three sub-studies. The aim of study 1 was to investigate antecedents and consequences of video game addiction measured as a unidimensional construct (pathological gaming). Aim of study 2 was to investigate the same associations in terms of typologies of gamers (“engaged,” “problem,” “addicted”). Furthermore, study 3 aimed to investigate the estimated stability and transitions occurring between the aforementioned typologies, and a non-pathological gaming group.Methods: A nationally representative sample of 3,000 adolescents aged 17.5 years was drawn from the population registry of Norway in 2012 and invited to participate in annual surveys spanning 3 years (NT1 = 2,059, NT2 = 1,334, NT3 = 1,277). The respondents completed measures of video game addiction, depression, anxiety, loneliness, aggression, and alcohol use disorder. Statistical analysis comprised cross-lagged path modeling, Satorra-Bentler chi square test (study 1), regression analyses (study 2), hidden Markov model of transition probabilities (study 3).Results: Findings in study 1 showed that depression and loneliness were reciprocally associated with pathological gaming. Physical aggression was identified as an antecedent, and anxiety was a consequence of pathological gaming. Investigation of the three typologies of gamers (study 2) identified loneliness and physical aggression as antecedents, and depression as a consequence of all typologies. Depression was found to be an antecedent of problem and engaged gamers. Loneliness was found as a consequence of problem gamers, and anxiety was a consequence of addicted gamers. High alcohol consumption was found antecedent to addicted gamers, and low alcohol consumption was found antecedent to problem gamers. The estimated stability of video game addiction was 35%.Conclusion: A reciprocal relationship between pathological gaming and measures of mental health problems seems to exist. The stability of video game addiction indicates a condition that for a substantial number of people does not resolve spontaneously over the course of 2 years.

show abstract

Section: Methodsmentioning

confidence: 99%

A Cross-Lagged Study of Developmental Trajectories of Video Game Engagement, Addiction, and Mental Health

et al. 2018

View full text Add to dashboard Cite

show abstract

“…, n . This assumption is common in Markov modeling (e.g., Bacci, Pandolfi, & Pennoni, 2014; Gudicha, Schmittmann, & Vermunt, 2016; Zucchini, MacDonald, & Langrock, 2016, p. 15). Besides being common practice, here, we also assume time homogeneity of the Markov structure to prevent the model from becoming too complex.…”

Section: Challenges and A Possible Solutionmentioning

confidence: 99%

A heteroscedastic hidden Markov mixture model for responses and categorized response times

2019

View full text Add to dashboard Cite

Various mixture modeling approaches have been proposed to identify within-subjects differences in the psychological processes underlying responses to psychometric tests. Although valuable, the existing mixture models are associated with at least one of the following three challenges: (1) A parametric distribution is assumed for the response times that—if violated—may bias the results; (2) the response processes are assumed to result in equal variances (homoscedasticity) in the response times, whereas some processes may produce more variability than others (heteroscedasticity); and (3) the different response processes are modeled as independent latent variables, whereas they may be related. Although each of these challenges has been addressed separately, in practice they may occur simultaneously. Therefore, we propose a heteroscedastic hidden Markov mixture model for responses and categorized response times that addresses all the challenges above in a single model. In a simulation study, we demonstrated that the model is associated with acceptable parameter recovery and acceptable resolution to distinguish between various special cases. In addition, the model was applied to the responses and response times of the WAIS-IV block design subtest, to demonstrate its use in practice.

show abstract

“…We can show (see, for instance, Cappé, Moulines, & Rydén, ; McLachlan & Krishnan, ) that our EM algorithm generates a sequence of estimates,

θ^{false(c false)}

c \geq 1

, satisfying

l false(θ^{false(c + 1 false)} false) \geq l false(θ^{false(c false)} false)

. One could also perform a numerical likelihood maximization as described in MacDonald () and Zucchini, MacDonald, & Langrock ().…”

Section: Overview Of the Proposed Methodologymentioning

confidence: 99%

Switching nonparametric regression models for multi‐curve data

Souza

Heckman

2017

Can J Statistics

View full text Add to dashboard Cite

We develop and apply an approach for analyzing multi‐curve data where each curve is driven by a latent state process. The state at any particular point determines a smooth function, forcing the individual curve to “switch” from one function to another. Thus each curve follows what we call a switching nonparametric regression model. We develop an EM algorithm to estimate the model parameters. We also obtain standard errors for the parameter estimates of the state process. We consider three types of hidden states: those that are independent and identically distributed, those that follow a Markov structure, and those that are independent but with distribution depending on some covariate(s). A simulation study shows the frequentist properties of our estimates. We apply our methods to a building's power usage data. The Canadian Journal of Statistics 45: 442–460; 2017 © 2017 Statistical Society of Canada

show abstract

R packages

Cited by 54 publications

References 0 publications

A Cross-Lagged Study of Developmental Trajectories of Video Game Engagement, Addiction, and Mental Health

A Cross-Lagged Study of Developmental Trajectories of Video Game Engagement, Addiction, and Mental Health

A heteroscedastic hidden Markov mixture model for responses and categorized response times

Switching nonparametric regression models for multi‐curve data

Contact Info

Product

Resources

About