An Object-Oriented Random-Number Package with Many Long Streams and Substreams

L’Ecuyer, Pierre; Simard, Richard; Chen, E. Jack; Kelton, W. David

doi:10.1287/opre.50.6.1073.358

Cited by 261 publications

(150 citation statements)

References 10 publications

Supporting

Mentioning

147

Contrasting

Unclassified

Order By: Relevance

“…This is contrary to prior work exploring the robustness of the LMM to normality assumptions (Maas & Hox, 2004a;LeBeau, 2013). Results from this prior work found that the simulated random effect distribution did not produce bias in the fixed effects, but did introduce bias into the random effects.…”

Section: Discussioncontrasting

confidence: 87%

“…Data generation was undertaken via an author written program. In order to replicate the results, a random seed was chosen and to ensure independent replications, the random number generation was based on the procedure by L'Ecuyer (L'Ecuyer, Simard, Chen, & Kelton, 2002). This procedure has the advantage of producing very large strings of random numbers without worrying about duplication and supports multiple threads of random number generation which allowed multiple cores of the processor to be used simultaneously improving the data simulation speed.…”

Section: Softwarementioning

confidence: 99%

See 1 more Smart Citation

Impact of Serial Correlation Misspecification with the Linear Mixed Model

LeBeau

2016

J. Mod. App. Stat. Meth.

View full text Add to dashboard Cite

Linear mixed models are popular models for use with clustered and longitudinal data due to their ability to model variation at different levels of clustering. A Monte Carlo study was used to explore the impact of assumption violations on the bias of parameter estimates and the empirical type I error rates. Simulated conditions included in this study are: simulated serial correlation structure, fitted serial correlation structure, random effect distribution, cluster sample size, and number of measurement occasions. Results showed that the fixed effects are unbiased, but the random components tend to be overestimated and the empirical Type I error rates tend to be inflated. Implications for applied researchers were discussed.

show abstract

Section: Discussioncontrasting

confidence: 87%

Section: Softwarementioning

confidence: 99%

Impact of Serial Correlation Misspecification with the Linear Mixed Model

LeBeau

2016

J. Mod. App. Stat. Meth.

View full text Add to dashboard Cite

show abstract

“…In this case, K 2 = 3 and K 3 = 5, and starting values are identical across chains; however, different seeds are used within parallel streams of generated random numbers (for details, see [42]). For predictive purposes, the various models are fitted to data from 2012 to 2014, and results are reported for out-of-sample data.…”

Section: Resultsmentioning

confidence: 99%

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

Perez

Brown

Pinchin

et al. 2016

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

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...

show abstract

“…We provide a summary of high performance computing R packages that might be of most general of research interest, rpvm 8 , Rmpi 9 , nws 10 , snow 11 , snowfall 12 , foreach 13 , multicore 14 , and parallel 15 . A more complete overview is given in the task views 16 .…”

Section: Some Contributed Parallel Packages To Cranmentioning

confidence: 99%

A Survey of R Software for Parallel Computing

Mahdi

2014

AJAMS

View full text Add to dashboard Cite

This article provides a summary of a selection of some of the high-performance parallel packages (libraries) available from the Comprehensive R Archive Network (CRAN) using the statistical software R. These packages can utilize multicore systems often found in modern personal computers as well as computer cluster or grid computing in order to provide linear speed up the computations in many of advanced statistical modern applications. Some illustrative R parallel codes are given in order to introduce the reader to some basic ideas about parallel programming in R packages. Keywords

show abstract

An Object-Oriented Random-Number Package with Many Long Streams and Substreams

Cited by 261 publications

References 10 publications

Impact of Serial Correlation Misspecification with the Linear Mixed Model

Impact of Serial Correlation Misspecification with the Linear Mixed Model

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

A Survey of R Software for Parallel Computing

Contact Info

Product

Resources

About