Cusum control for data following the von Mises distribution

Hawkins, Douglas M.; Lombard, F.

doi:10.1080/02664763.2016.1202217

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…Nonparametric tests include the permutation test of Byrne et al (2009) and the CUSUM test of Lombard et al (2017). In vM-distributed series, likelihood (Ghosh et al 1999;Hawkins and Lombard 2015) and CUSUM (Hawkins and Lombard 2017) procedures have been advocated. Bayesian approaches have also been considered (Ghosh et al 1999;SenGupta and Laha 2008).…”

Section: Other Testing Scenariosmentioning

confidence: 99%

Recent advances in directional statistics

Pewsey

García‐Portugués

2021

TEST

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Mainstream statistical methodology is generally applicable to data observed in Euclidean space. There are, however, numerous contexts of considerable scientific interest in which the natural supports for the data under consideration are Riemannian manifolds like the unit circle, torus, sphere, and their extensions. Typically, such data can be represented using one or more directions, and directional statistics is the branch of statistics that deals with their analysis. In this paper, we provide a review of the many recent developments in the field since the publication of Mardia and Jupp (Wiley 1999), still the most comprehensive text on directional statistics. Many of those developments have been stimulated by interesting applications in fields as diverse as astronomy, medicine, genetics, neurology, space situational awareness, acoustics, image analysis, text mining, environmetrics, and machine learning. We begin by considering developments for the exploratory analysis of directional data before progressing to distributional models, general approaches to inference, hypothesis testing, regression, nonparametric curve estimation, methods for dimension reduction, classification and clustering, and the modelling of time series, spatial and spatio-temporal data. AnWe are most grateful to three anonymous referees and, in alphabetical order, to

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Section: Other Testing Scenariosmentioning

confidence: 99%

Recent advances in directional statistics

Pewsey

García‐Portugués

2021

TEST

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“…It has been used in critically tracing the care provider path for individual patients (Chen et al, 2016;Xie et al, 2016;Baretta et al, 2016). The transition paths through the ED can be presented by Markov chain models (Lee et al, 2016;Sajadi et al, 2016;Hawkins and Lombard, 2016). The assumptions made during the events from a transition of one state to another are that the future transition of a patient in a hospital depends on the present state and is not affected by the other past states.…”

Section: Related Literaturementioning

confidence: 99%

Determining optimal policy for emergency department using Markov decision process

Hamasha

Rumbe

2017

WJE

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Purpose Emergency departments (ED) are faced with the challenge of capacity planning that caused by the high demand for patients and limited resources. Consequently, inadequate resources lead to increased delays, impacts on the quality of care and increase the health-care costs. Such circumstances necessitate utilizing operational research modules, such as the Markov decision process (MDP) to enable better decision-making. The purpose of this paper is to demonstrate the applicability and usage of MDP on ED. Design/methodology/approach The adoption of MDP provides invaluable insights into system operations based on the different system states (e.g. very busy to unoccupied) to ensure optimal assigning of resources and reduced costs. In this paper, a descriptive health system model based on the MDP is presented, and a numerical example is illustrated to elaborate its appropriateness in optimal policy decision determination. Findings Faced with numerous decisions, hospital managers have to ensure that the appropriate technique is used to minimize any undesired outcomes. MDP has been shown to be a robust approach which provides support to the critical decision-making processes. Additionally, MDP also provides insights on the associated costs which enable the hospital managers to efficiently allocate resources ensuring quality health care and increased throughput while minimizing costs. Originality/value Applying MDP in the ED is a unique and good starting. MDP is powerful tool helps in making a decision in the critical situations, and the ED needs such tool.

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“…However, the fact that κ is not a scale parameter of the distribution of X complicates matters. Hawkins and Lombard (2017) showed that even if the mean direction ν is known, control limits for a specified in-control ARL in a von Mises CUSUM for detecting change away from κ 0 depend upon κ 0 . Nonetheless, the locally most powerful test statistic suggests application of a CUSUM based on…”

Section: Concentration Changementioning

confidence: 99%

Nonparametric Cusum Charts for Angular Data with Applications in Health Science and Astrophysics

Lombard,

Hawkins,

Potgieter

2017

Preprint

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Cusum control for data following the von Mises distribution

Cited by 7 publications

References 11 publications

Recent advances in directional statistics

Recent advances in directional statistics

Determining optimal policy for emergency department using Markov decision process

Nonparametric Cusum Charts for Angular Data with Applications in Health Science and Astrophysics

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