Is EM really necessary here? Examples where it seems simpler not to use EM

MacDonald, Iain L.

doi:10.1007/s10182-021-00392-x

Cited by 7 publications

(2 citation statements)

References 46 publications

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“…In particular, although we could, in principle, make use of the EM (expectation-maximisation) algorithm [34] (Chapter 9) and [35] to approximate the maximum likelihood estimates of the parameters, this would not be strictly necessary in the bi-skew logistic case, cf. [36]. The only caveat, which holds independently of whether the EM algorithm is deployed or not, is the additional number of parameters present in the equations being solved.…”

Section: The Bi-skew Logistic Distribution For Modelling Epidemic Wavesmentioning

confidence: 99%

A Skew Logistic Distribution for Modelling COVID-19 Waves and Its Evaluation Using the Empirical Survival Jensen–Shannon Divergence

Levene

2022

Entropy

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A novel yet simple extension of the symmetric logistic distribution is proposed by introducing a skewness parameter. It is shown how the three parameters of the ensuing skew logistic distribution may be estimated using maximum likelihood. The skew logistic distribution is then extended to the skew bi-logistic distribution to allow the modelling of multiple waves in epidemic time series data. The proposed skew-logistic model is validated on COVID-19 data from the UK, and is evaluated for goodness-of-fit against the logistic and normal distributions using the recently formulated empirical survival Jensen–Shannon divergence (ESJS) and the Kolmogorov–Smirnov two-sample test statistic (KS2). We employ 95% bootstrap confidence intervals to assess the improvement in goodness-of-fit of the skew logistic distribution over the other distributions. The obtained confidence intervals for the ESJS are narrower than those for the KS2 on using this dataset, implying that the ESJS is more powerful than the KS2.

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Section: The Bi-skew Logistic Distribution For Modelling Epidemic Wavesmentioning

confidence: 99%

A Skew Logistic Distribution for Modelling COVID-19 Waves and Its Evaluation Using the Empirical Survival Jensen–Shannon Divergence

Levene

2022

Entropy

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“…It does not seem difficult to find published applications of EM that are unnecessary, in the sense that there are simpler methods available that will solve the estimation problems concerned. I have elsewhere documented some applications of this kind, 1,2 and will not here repeat the detail thereof. I will instead describe, in general terms, a rather obvious alternative to EM that often seems to work smoothly, with little mathematical or coding effort.…”

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confidence: 99%

Puzzle

2021

Significance

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Hidden-Markov models for ordinal time series

H. Weiß,

Swidan

2024

AStA Adv Stat Anal

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A common approach for modeling categorical time series is Hidden-Markov models (HMMs), where the actual observations are assumed to depend on hidden states in their behavior and transitions. Such categorical HMMs are even applicable to nominal data but suffer from a large number of model parameters. In the ordinal case, however, the natural order among the categorical outcomes offers the potential to reduce the number of parameters while improving their interpretability at the same time. The class of ordinal HMMs proposed in this article link a latent-variable approach with categorical HMMs. They are characterized by parametric parsimony and allow the easy calculation of relevant stochastic properties, such as marginal and bivariate probabilities. These points are illustrated by numerical examples and simulation experiments, where the performance of maximum likelihood estimation is analyzed in finite samples. The developed methodology is applied to real-world data from a health application.

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Is EM really necessary here? Examples where it seems simpler not to use EM

Cited by 7 publications

References 46 publications

A Skew Logistic Distribution for Modelling COVID-19 Waves and Its Evaluation Using the Empirical Survival Jensen–Shannon Divergence

A Skew Logistic Distribution for Modelling COVID-19 Waves and Its Evaluation Using the Empirical Survival Jensen–Shannon Divergence

Puzzle

Hidden-Markov models for ordinal time series

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