RELATION OF SECOND YEAR MBA GRADES TO BUSINESS EARNINGS<sup>1</sup>

Harrell, Thomas W.; Harrell, Margaret S.

doi:10.1111/j.1744-6570.1974.tb01170.x

Cited by 11 publications

(5 citation statements)

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“…This is implemented via a non-parametric bootstrapping approach [47] with a repeated sample of the observed data with replacement with the study subject as the sampling unit to form 1000 simulated datasets of each sample size. Traditionally, Monte-Carlo simulation studies based on data generated from statistical models have been used for this kind of comparative study.…”

Section: Methodsmentioning

confidence: 99%

“…Traditionally, Monte-Carlo simulation studies based on data generated from statistical models have been used for this kind of comparative study. Resampling has the advantage that the data in resampled datasets are based on observations from real datasets and thus reflect the appropriate level of diversity and variability found in realistic populations [47,48]. Our datasets are large enough to permit numerous samples of reasonable size to arrive at stable conclusions within the resampled data [47].…”

Section: Methodsmentioning

confidence: 99%

“…Resampling has the advantage that the data in resampled datasets are based on observations from real datasets and thus reflect the appropriate level of diversity and variability found in realistic populations [47,48]. Our datasets are large enough to permit numerous samples of reasonable size to arrive at stable conclusions within the resampled data [47]. Performance of methods in terms of bias and efficiency will be benchmarked against results from the original full data.…”

Section: Methodsmentioning

confidence: 99%

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Fitting parametric random effects models in very large data sets with application to VHA national data

Gebregziabher

Egede

Gilbert

et al. 2012

BMC Med Res Methodol

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BackgroundWith the current focus on personalized medicine, patient/subject level inference is often of key interest in translational research. As a result, random effects models (REM) are becoming popular for patient level inference. However, for very large data sets that are characterized by large sample size, it can be difficult to fit REM using commonly available statistical software such as SAS since they require inordinate amounts of computer time and memory allocations beyond what are available preventing model convergence. For example, in a retrospective cohort study of over 800,000 Veterans with type 2 diabetes with longitudinal data over 5 years, fitting REM via generalized linear mixed modeling using currently available standard procedures in SAS (e.g. PROC GLIMMIX) was very difficult and same problems exist in Stata’s gllamm or R’s lme packages. Thus, this study proposes and assesses the performance of a meta regression approach and makes comparison with methods based on sampling of the full data.DataWe use both simulated and real data from a national cohort of Veterans with type 2 diabetes (n=890,394) which was created by linking multiple patient and administrative files resulting in a cohort with longitudinal data collected over 5 years.Methods and resultsThe outcome of interest was mean annual HbA1c measured over a 5 years period. Using this outcome, we compared parameter estimates from the proposed random effects meta regression (REMR) with estimates based on simple random sampling and VISN (Veterans Integrated Service Networks) based stratified sampling of the full data. Our results indicate that REMR provides parameter estimates that are less likely to be biased with tighter confidence intervals when the VISN level estimates are homogenous.ConclusionWhen the interest is to fit REM in repeated measures data with very large sample size, REMR can be used as a good alternative. It leads to reasonable inference for both Gaussian and non-Gaussian responses if parameter estimates are homogeneous across VISNs.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Fitting parametric random effects models in very large data sets with application to VHA national data

Gebregziabher

Egede

Gilbert

et al. 2012

BMC Med Res Methodol

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“…63 The chief licence inspector deferred to Bonham's expertise and denied Zuk's application. 64 Zuk requested an appeal on the grounds that he had already purchased his equipment and, more importantly, emphasized that "in answer to the medical question which appears to be based on the fact tattooing under unsanitary conditions causes infectious hepatitis, I can assure you that this is well-known to tattoo artists and at the present time we now have sterilizing equipment which absolutely ensures that all the needles will be used under the most sterile conditions." 65 After a unanimous motion, city council made arrangements at their 7 May meeting to allow Zuk to make his case the following week.…”

Section: Bryan Zuk At City Councilmentioning

confidence: 99%

Bad Bastards?: Tattooing, Health, and Regulation in Twentieth-Century Vancouver

Jelinski¹

2018

Urban History Review

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Using local newspapers and archival documents from the City of Vancouver Archives, this article investigates how state actors-namely the city of Vancouver's Health Departmentattempted to regulate professional tattooing in Vancouver. Simultaneously, I consider how tattooists understood health and sanitation relative to tattooing and, in the process, responded to and challenged regulation. In doing so, I investigate the complex relationship between Vancouver's professional tattooists and state actors, focusing on the methods used to regulate tattooing. I argue that although unfounded or inadequately established health concerns typically concealed efforts to control tattooing, tattooists worked within and against regulation to maintain a presence in Vancouver. À partir des journaux locaux et de documents d'archives des Archives de la Ville de Vancouver, cet article examine comment les intervenants du Département de la santé publique de la Ville de Vancouver ont tenté de réglementer le tatouage professionnel dans leur ville. Nous considérons également comment les tatoueurs ont compris les implications hygiéniques et de santé de leur pratique, et comment ils ont réagi à la nouvelle réglementation. Ainsi, en se concentrant sur les méthodes employées pour réglementer le tatouage, nous examinons la relation complexe des tatoueurs professionnels de la ville de Vancouver avec ses instances municipales. Nous avançons que même si les préoccupations sanitaires, qu'elles soient sans fondement ou insuffisamment démontrées, ont servi à masquer les intentions de contrôler le tatouage, les tatoueurs ont travaillé simultanément avec et contre cette réglementation afin de maintenir leur présence dans la ville.Jamie Jelinski is a doctoral candidate in cultural studies at Queen's University. During the fall of 2019, he will begin a postdoctoral fellowship in the Department of Art History and Communication Studies at McGill University. Jelinski's research contributions include published and forthcoming articles in Journal of Canadian Studies, Visual Anthropology, and Études Inuit Studies. Jamie Jelinski est un étudiant de doctorat en études culturelles à l'Université Queen's. En 2019, il débutera des études postdoctorales au Department of Art History and Communication Studies de l'Université McGill. Ses travaux récents et à venir paraissent dans des revues spécialisées telles que Journal of Canadian Studies, Visual Anthropology, et Études Inuit Studies.

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“…Hmisc. Harrell (2010) MI 2 aregImpute(as.formula(~I(x)+I(y)),n.impute=D,type='regression',mat ch='closest',nk=0,curtail=T,boot.method="approximate bayesian") MI 3 aregImpute(as.formula(~I(x)+I(y)),n.impute=D,type='regression',mat ch='closest',nk=0,curtail=F,boot.method="approximate bayesian") MI 4 aregImpute(as.formula(~I(x)+I(y)),n.impute=D,type='regression',mat ch='weighted',nk=0,curtail=T,boot.method="approximate bayesian") MI 5 aregImpute(as.formula(~I(x)+I(y)),n.impute=D,type='pmm',match=' closest',nk=0,curtail=T,boot.method="approximate bayesian") MI 6 aregImpute(as.formula(~I(x)+I(y)),n.impute=D,type='pmm',match=' weighted',nk=0,curtail=T,boot.method="approximate bayesian") MI 7 aregImpute(as.formula(~I(x)+I(y)),n.impute=D,type='regression',mat ch='closest',nk=c(0,3:5),B=10,curtail=T,boot.method= "approximate bayesian") MI 8 aregImpute(as.formula(~I(x)+I(y)),n.impute=D,type='regression',mat ch='closest',nk=c(0,3:5),B=10,tlinear=F,curtail=T,boot.method="app roximate bayesian") mi. Gelman (2010) MI 9 mi(data.frame(data),n.imp=D,add.noise=noise.control(method="resh uffling",K=1,post.run.iter=20),n.iter=30) MI 10 mi(data.frame(data),n.imp=D,add.noise=noise.control(method="fadi ng",pct.aug=10,post.run.iter=20),n.iter=30) mice.…”

Section: Appendicesmentioning

confidence: 99%

Bias reduction of finite population imputation by kernel methods

Pettersson

2013

SiTns

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Missing data is a nuisance in statistics. Real donor imputation can be used with item nonresponse. A pool of donor units with similar values on auxiliary variables is matched to each unit with missing values. The missing value is then replaced by a copy of the corresponding observed value from a randomly drawn donor. Such methods can to some extent protect against nonresponse bias. But bias also depends on the estimator and the nature of the data. We adopt techniques from kernel estimation to combat this bias. Motivated by Pólya urn sampling, we sequentially update the set of potential donors with units already imputed, and use multiple imputations via Bayesian bootstrap to account for imputation uncertainty. Simulations with a single auxiliary variable show that our imputation method performs almost as well as competing methods with linear data, but better when data is nonlinear, especially with large samples.

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RELATION OF SECOND YEAR MBA GRADES TO BUSINESS EARNINGS¹

Cited by 11 publications

References 2 publications

Fitting parametric random effects models in very large data sets with application to VHA national data

Fitting parametric random effects models in very large data sets with application to VHA national data

Bad Bastards?: Tattooing, Health, and Regulation in Twentieth-Century Vancouver

Bias reduction of finite population imputation by kernel methods

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RELATION OF SECOND YEAR MBA GRADES TO BUSINESS EARNINGS1

Cited by 11 publications

References 2 publications

Fitting parametric random effects models in very large data sets with application to VHA national data

Fitting parametric random effects models in very large data sets with application to VHA national data

Bad Bastards?: Tattooing, Health, and Regulation in Twentieth-Century Vancouver

Bias reduction of finite population imputation by kernel methods

Contact Info

Product

Resources

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RELATION OF SECOND YEAR MBA GRADES TO BUSINESS EARNINGS¹