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Selecting the rational subgroup is crucial in developing a robust monitoring plan for dispersion of normally distributed data

Communications in statistics. Simulation and computation, 2022-12, Vol.51 (12), p.6925-6935

Sparks, Ross Stewart

2022

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Home Telemonitoring of Vital Signs-Technical Challenges and Future Directions

IEEE journal of biomedical and health informatics, 2015-01, Vol.19 (1), p.82-91

2015

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Automated monitoring of tweets for early detection of the 2014 Ebola epidemic

PloS one, 2020-03, Vol.15 (3), p.e0230322-e0230322

2020

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Early warning CUSUM plans for surveillance of negative binomial daily disease counts

Journal of applied statistics, 2010-11, Vol.37 (11), p.1911-1929

2010

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An investigation into social media syndromic monitoring

Communications in statistics. Simulation and computation, 2017-09, Vol.46 (8), p.5901-5923

2017

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CUSUM Charts for Signalling Varying Location Shifts

Journal of quality technology, 2000-04, Vol.32 (2), p.157-171

Sparks, Ross S.

2000

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Tele-Health Monitoring of Patient Wellness

Journal of intelligent systems, 2016-10, Vol.25 (4), p.515-528

2016

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Efficient algorithms for real-time syndromic surveillance

Journal of biomedical informatics, 2023-10, Vol.146, p.104236-104236, Article 104236

2023

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Monitoring events with application to syndromic surveillance using social media data

Engineering reports (Hoboken, N.J.), 2020-05, Vol.2 (5), p.n/a

2020

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A review of some sampling and aggregation strategies for basic statistical process monitoring: Discussion by Ross Sparks

Journal of quality technology, 2021-01, Vol.53 (1), p.23-25

Sparks, Ross Stewart

2021

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Telehealth Monitoring of Patients in the Community

Journal of intelligent systems, 2016-01, Vol.25 (1), p.37-53

2016

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Modelling hospital length of stay using convolutive mixtures distributions

Statistics in medicine, 2017-01, Vol.36 (1), p.122-135

2017

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Syndromic surveillance to detect disease outbreaks using time between emergency department presentations

Emergency medicine Australasia, 2022-02, Vol.34 (1), p.92-98

2022

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Detecting Periods of Significant Increased Communication Levels for Subgroups of Targeted Individuals

Quality and reliability engineering international, 2016-07, Vol.32 (5), p.1871-1888

Sparks, Ross

2016

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Characteristics of patients with COVID‐19 hospitalised in South Australia during the first wave of the pandemic

Emergency medicine Australasia, 2022-02, Vol.34 (1), p.122-126

2022

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Linking EWMA p Charts and the Risk Adjustment Control Charts

Quality and reliability engineering international, 2017-04, Vol.33 (3), p.617-636

Sparks, Ross

2017

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Prediction and surveillance of influenza epidemics

Medical journal of Australia, 2011-02, Vol.194 (4), p.S28-S33

2011

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Shewhart dispersion charts made easy for mild to moderately autocorrelated normally distributed data

Quality engineering, 2018-04, Vol.30 (2), p.236-252

Sparks, Ross

2018

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Improving the clinical value of at home telehealth

International journal of integrated care, 2014-12, Vol.14 (9)

2014

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Monitoring communication outbreaks among an unknown team of actors in dynamic networks

Journal of quality technology, 2019-10, Vol.51 (4), p.353-374

2019

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Monitoring Highly Correlated Multivariate Processes Using Hotelling's T 2 Statistic: Problems and Possible Solutions

Quality and reliability engineering international, 2015-10, Vol.31 (6), p.1089-1097

Sparks, Ross

2015

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Detecting Periods of Significant Increased Communication Levels for Subgroups of Targeted Individuals: R. SPARKS

Quality and reliability engineering international, 2016-07, Vol.32 (5), p.1871-1888

Sparks, Ross

2016

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Spatio-Temporal Surveillance Using CUSUM of Order Statistics

Quality and reliability engineering international, 2015-12, Vol.31 (8), p.1743-1760

Sparks, Ross

2015

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Aggregation levels when the time between events is Weibull distributed

Quality engineering, 2021-06, Vol.33 (2), p.348-360

2021

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Monitoring Highly Correlated Multivariate Processes Using Hotelling's T2 Statistic: Problems and Possible Solutions

Quality and reliability engineering international, 2015-10, Vol.31 (6), p.1089-1097

Sparks, Ross

2015

Volltextzugriff (PDF)

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Selecting the rational subgroup is crucial in developing a robust monitoring plan for dispersion of normally distributed data

Home Telemonitoring of Vital Signs-Technical Challenges and Future Directions

Automated monitoring of tweets for early detection of the 2014 Ebola epidemic

Early warning CUSUM plans for surveillance of negative binomial daily disease counts

An investigation into social media syndromic monitoring

CUSUM Charts for Signalling Varying Location Shifts

Tele-Health Monitoring of Patient Wellness

Efficient algorithms for real-time syndromic surveillance

Monitoring events with application to syndromic surveillance using social media data

A review of some sampling and aggregation strategies for basic statistical process monitoring: Discussion by Ross Sparks

Telehealth Monitoring of Patients in the Community

Modelling hospital length of stay using convolutive mixtures distributions

Syndromic surveillance to detect disease outbreaks using time between emergency department presentations

Detecting Periods of Significant Increased Communication Levels for Subgroups of Targeted Individuals

Characteristics of patients with COVID‐19 hospitalised in South Australia during the first wave of the pandemic

Linking EWMA p Charts and the Risk Adjustment Control Charts

Prediction and surveillance of influenza epidemics

Shewhart dispersion charts made easy for mild to moderately autocorrelated normally distributed data

Improving the clinical value of at home telehealth

Monitoring communication outbreaks among an unknown team of actors in dynamic networks

Monitoring Highly Correlated Multivariate Processes Using Hotelling's T 2 Statistic: Problems and Possible Solutions

Detecting Periods of Significant Increased Communication Levels for Subgroups of Targeted Individuals: R. SPARKS

Spatio-Temporal Surveillance Using CUSUM of Order Statistics

Aggregation levels when the time between events is Weibull distributed

Monitoring Highly Correlated Multivariate Processes Using Hotelling's T2 Statistic: Problems and Possible Solutions

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