Finite Mixture Modeling of Gaussian Regression Time Series with Application to Dendrochronology

Michael, Semhar; Melnykov, Volodymyr

doi:10.1007/s00357-016-9216-4

Cited by 15 publications

(13 citation statements)

References 33 publications

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“…These 2 steps are iterated until a convergence criterion is met indicating that the best solution was achieved. More details on this model are available (14). This mixture model is used to find similar counties and to build a single regression ARMA model within each cluster.…”

Section: Methodsmentioning

confidence: 99%

“…The goals of our project were to forecast the number of participants for South Dakota’s 66 counties and to identify county clusters within the state that share similar socioeconomic characteristics and that are rural with low populations. We fit a model within each cluster simultaneously by using a finite mixture of Gaussian regression time series models (14). …”

Section: Introductionmentioning

confidence: 99%

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Forecasting Participants in the All Women Count! Mammography Program

2018

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IntroductionThe All Women Count! (AWC!) program is a no-cost breast and cervical cancer screening program for qualifying women in South Dakota. Our study aimed to identify counties with similar socioeconomic characteristics and to estimate the number of women who will use the program for the next 5 years.MethodsWe used AWC! data and sociodemographic predictor variables (eg, poverty level [percentage of the population with an annual income at or below 200% of the Federal Poverty Level], median income) and a mixture of Gaussian regression time series models to perform clustering and forecasting simultaneously. Model selection was performed by using Bayesian information criterion (BIC). Forecasting of the predictor variables was done by using an autoregressive integrated moving average model.ResultsBy using BIC, we identified 5 clusters showing the groups of South Dakota counties with similar characteristics in terms of predictor variables and the number of participants. The mixture model identified groups of counties with increasing or decreasing trends in participation and forecast averages per cluster.ConclusionThe mixture of regression time series model used in this study allowed for the identification of similar counties and provided a forecasting model for future years. Although several predictors contributed to program participation, we believe our forecasting analysis by county may provide useful information to improve the implementation of the AWC! program by informing program managers on the expected number of participants in the next 5 years. This, in turn, will help in data-driven resource allocation.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting Participants in the All Women Count! Mammography Program

2018

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“…This is so, because the dimension of the within-cluster covariance matrices does not depend on the length of the time-series. We thereby extend the work of Michael and Melnykov (2016) to the multivariate case. Using Equation 2…”

Section: Estimationmentioning

confidence: 97%

Inter-individual differences in multivariate time series: Latent class vector-autoregressive modelling

Ernst¹,

Albers²,

Jeronimus³

et al. 2020

Preprint

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Theories of emotion regulation posit the existence of individual differences in emotion dynamics. Current multi-subject time-series models account for differences in dynamics across individuals only to a very limited extent. This results in an aggregation that may poorly apply at the individual level. We present the exploratory method of latent class vector-autoregressive modelling (LCVAR), which extends the time-series models to include clustering of individuals with similar dynamic processes. LCVAR can identify individuals with similar emotion dynamics in intensive time-series, which may be of unequal length. The method performs excellently under a range of simulated conditions. The value of identifying clusters in time-series is illustrated using affect measures of 410 individuals, assessed at over 70 time points per individual. LCVAR discerned six clusters of distinct emotion dynamics with regard to diurnal patterns and augmentation and blunting processes between eight emotions.

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“…Then, after the similarity between all pairs of time series is computed, any standard clustering procedure that allows for the analysis of a proximity matrix can be used. With the current approach, the toolbox for clustering time series data continues to grow (see Euan, Ombao, and Ortega, 2018;Michael and Melnykov, 2016;Rahmanishamsi, Dolati, and Aghabozorgi, 2018), and I expect that this will continue to be in the case in the near future. Further, an open area of research will be the comparison of the time series methods with other methods for clustering longitudinal data, such as growth mixture modeling (see Ram and Grimm, 2009).…”

Section: Editorialmentioning

confidence: 99%

Editorial

Steinley

2018

J Classif

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Finite Mixture Modeling of Gaussian Regression Time Series with Application to Dendrochronology

Cited by 15 publications

References 33 publications

Forecasting Participants in the All Women Count! Mammography Program

Forecasting Participants in the All Women Count! Mammography Program

Inter-individual differences in multivariate time series: Latent class vector-autoregressive modelling

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