What is he best known for?
The fields of study he is best known for:
- Statistics
- Normal distribution
- Internal medicine
His primary scientific interests are in Statistics, Econometrics, Covariate, Regression analysis and Regression.
John D. Kalbfleisch integrates Statistics with Time data in his research.
His studies in Econometrics integrate themes in fields like Survival function and Data analysis.
John D. Kalbfleisch has researched Covariate in several fields, including Mixture model, Mixture distribution, Logistic distribution and Pseudolikelihood.
As part of the same scientific family, John D. Kalbfleisch usually focuses on Regression analysis, concentrating on Hazard and intersecting with Identifiability, Censoring, Maximum likelihood and Estimator.
His Regression study incorporates themes from Sampling bias and Missing data.
His most cited work include:
- The Statistical Analysis of Failure Time Data. (9455 citations)
- A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C (2798 citations)
- The statistical analysis of failure time data (2198 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Statistics, Econometrics, Proportional hazards model, Estimator and Covariate.
His Statistics study frequently links to adjacent areas such as Applied mathematics.
In his study, Likelihood-ratio test is inextricably linked to Asymptotic distribution, which falls within the broad field of Applied mathematics.
His study of Accelerated failure time model is a part of Econometrics.
His research integrates issues of Dialysis and Hazard ratio in his study of Proportional hazards model.
His work deals with themes such as Restricted maximum likelihood and Expectation–maximization algorithm, which intersect with Likelihood function.
He most often published in these fields:
- Statistics (46.58%)
- Econometrics (27.40%)
- Proportional hazards model (14.38%)
What were the highlights of his more recent work (between 2009-2021)?
- Statistics (46.58%)
- Econometrics (27.40%)
- Kidney Paired Donation (6.16%)
In recent papers he was focusing on the following fields of study:
John D. Kalbfleisch mainly focuses on Statistics, Econometrics, Kidney Paired Donation, Estimator and Proportional hazards model.
His work on Inference expands to the thematically related Statistics.
In the field of Econometrics, his study on Accelerated failure time model overlaps with subjects such as Inverse probability.
His Estimator study integrates concerns from other disciplines, such as Marginal model, Event, Research design, Mean squared error and Propensity score matching.
John D. Kalbfleisch focuses mostly in the field of Proportional hazards model, narrowing it down to topics relating to Linear regression and, in certain cases, Graft failure, Kidney transplant, Outcomes research and Regression.
The study incorporates disciplines such as Health administration, Extreme value theory and Outlier in addition to Covariate.
Between 2009 and 2021, his most popular works were:
- Pregnancy-onset habitual snoring, gestational hypertension, and preeclampsia: prospective cohort study. (97 citations)
- Statistical Analysis of Illness–Death Processes and Semicompeting Risks Data (89 citations)
- Access and Outcomes Among Minority Transplant Patients, 1999–2008, with a Focus on Determinants of Kidney Graft Survival (73 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Normal distribution
- Internal medicine
His primary areas of investigation include Statistics, Pregnancy, Cohort study, Estimator and Selection.
All of his Statistics and Covariate, Joint probability distribution, Multivariate statistics, Categorical variable and Propensity score matching investigations are sub-components of the entire Statistics study.
The subject of his Covariate research is within the realm of Econometrics.
John D. Kalbfleisch works mostly in the field of Cohort study, limiting it down to topics relating to Incidence and, in certain cases, Proportional hazards model, as a part of the same area of interest.
In his research, Observational study, Marginal model, Interval and Delta method is intimately related to Event, which falls under the overarching field of Estimator.
His Selection research incorporates elements of Exponential family, Missing data and Expectation–maximization algorithm.
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