Time series of count data: modeling, estimation and diagnostics

Jung, Robert C.; Kukuk, Martin; Liesenfeld, Roman

doi:10.1016/j.csda.2006.08.001

Cited by 156 publications

(101 citation statements)

References 35 publications

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“…[24,30]); thus introducing both auto-regression and over-dispersion. Less common multivariate extensions can handle both contemporary and serial correlations, and therefore tackle questions not addressed by marginal models (cf.…”

Section: State-space Models For Multivariate Count Data 185mentioning

confidence: 99%

“…In this work, we will employ univariate Poisson regressions and observation-driven Poisson autoregressive models as a basis for drawing predictive comparisons 250 and discussing congruency in results within Section 5. For references describing these methods, we refer the reader to [24,33] or [37].…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, observation-driven 255 specifications for Poisson autoregressive models offer straightforward and efficient ways to draw inference over short-horizons ( [24,38]). These benefit from easy to calculate likelihoods; however, stationarity and ergodicity properties are hard to derive, and they most importantly suffer from a lack of interpretability on covariates when compared to the parameter-driven alternative discussed in…”

Section: Related Workmentioning

confidence: 99%

“…It allows to not underestimate true serial dependencies and control for discreteness and over-dispersion ( [23]); moreover, link functions may allow for intuitive interpretations of covariates' effects (e.g. [24]). Thus, by means of a latent parameter-driven model, we show it is possible to draw inference on contemporary and serial correlations on demand, over different medical and surgical 155 specialities within a local facility.…”

Section: Introductionmentioning

confidence: 99%

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Out of hours workload management: Bayesian inference for decision support in secondary care

Perez

Brown

Pinchin

et al. 2016

Artificial Intelligence in Medicine

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Objective: In this paper, we aim to evaluate the use of electronic technologies in Out of Hours (OoH) task-management for assisting the design of effective support systems in health care; targeting local facilities, wards or specific working groups. In addition, we seek to draw and validate conclusions with relevance to a frequently revised service, subject to increasing pressures.Methods and Material : We have analysed 4 years of digitised demand-data extracted from a recently deployed electronic task-management system, within the Hospital at Night setting in two jointly coordinated hospitals in the United Kingdom. The methodology employed relies on Bayesian inference methods and parameter-driven state-space models for multivariate series of count data.Results: Main results support claims relating to (i) the importance of datadriven staffing alternatives and (ii) demand forecasts serving as a basis to intelligent scheduling within working groups. We have displayed a split in workload patterns across groups of medical and surgical specialities, and sustained assertions regarding staff behaviour and work-need changes according to shifts or days of the week. Also, we have provided evidence regarding the relevance of day-to-day planning and prioritisation.Conclusions: The work exhibits potential contributions of electronic tasking alternatives for the purpose of data-driven support systems design; for scheduling, prioritisation and management of care delivery. Response to reviewersGeneral Comments: We are grateful to all reviewers for taking the time to review our paper and make a number of helpful comments that we believe have significantly improved the quality of the manuscript. All comments have been, according to their nature, merged, described and addressed here: 5Comment 1: The major concern shared by reviewers relates to a lack practical relevance of the results presented; for instance, managerial conclusions are not well exploited, the paper lacks a managerial perspective of the output from professionals involved in the study, or results do not extrapolate to different settings neither is it clear why they support the decision making. 10Response to Comment 1: The authors agree that the previous version of the manuscript offered a mostly descriptive study of the data and lacked relevant interpretations and conclusions drawn from results. In addition, we believe we were not successful in clearly conveying the aims and scope of our work; yet, we have made the efforts to undertake the alterations necessary in order to correct 15 these problems.Nationwide hospital settings are indeed subject to disparate workload pressures; these may drastically vary within distant geographical regions and in relation to specialities covered by each facility. The focus of our research team and this work relates to support systems for effective management that can 20 target local facilities and working groups. This is indeed a great problem, in that policy alterations and intelligent design of support systems (for instan...

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Section: State-space Models For Multivariate Count Data 185mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Out of hours workload management: Bayesian inference for decision support in secondary care

Perez

Brown

Pinchin

et al. 2016

Artificial Intelligence in Medicine

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“…The fields include insurance industry (e.g., the number of claim counts), economics (e.g., the number of transactions of some stock), engineering (e.g., the number of traffic accidents) and epidemiology (e.g., the number of people with a certain disease). For a general review of time series of counts, we refer to Jung et al (2006) and Weiß (2008). It is widely recognized that time series models applied to various fields such as finance often undergo parameter changes.…”

Section: Introductionmentioning

confidence: 99%

Robust CUSUM test for time series of counts and its application to analyzing the polio incidence data

Kang¹

2015

Journal of the Korean Data and Information Science Society

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In this paper, we analyze the polio incidence data based on the Poisson autoregressive models, focusing particularly on change-point detection. Since the data include some strongly deviating observations, we employ the robust cumulative sum (CUSUM) test proposed by Kang and Song (2015) to perform the test for parameter change. Contrary to the result of Kang and Lee (2014), our data analysis indicates that there is no significant change in the case of the CUSUM test with strong robustness and the same result is obtained after ridding the polio data of outliers. We additionally consider the comparison of the forecasting performance. All the results demonstrate that the robust CUSUM test performs adequately in the presence of seemingly outliers.Keywords: Minimum density power divergence estimator, Poisson autoregressive model, robust parameter change test, the polio incidence data.

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Over‐and Underdispersion Models

Kokonendji¹

2014

Methods and Applications of Statistics in Clinical Trials

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Time series of count data: modeling, estimation and diagnostics

Cited by 156 publications

References 35 publications

Out of hours workload management: Bayesian inference for decision support in secondary care

Out of hours workload management: Bayesian inference for decision support in secondary care

Robust CUSUM test for time series of counts and its application to analyzing the polio incidence data

Over‐and Underdispersion Models

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