What is he best known for?
The fields of study he is best known for:
- Statistics
- Law
- Social science
His primary areas of study are Econometrics, Statistics, Survival analysis, Regression analysis and Linear regression.
His studies link Regression with Econometrics.
His work on Imputation, Missing data and Minimax estimator as part of general Statistics study is frequently linked to Negative multinomial distribution and Binomial test, bridging the gap between disciplines.
His study in Missing data is interdisciplinary in nature, drawing from both Structural equation modeling and Monte Carlo method.
Survival analysis is a subfield of Internal medicine that Paul D. Allison studies.
His work deals with themes such as Ordered logit, Multinomial logistic regression, Poisson regression and Log-linear model, which intersect with Linear regression.
His most cited work include:
- Survival analysis using the SAS system : a practical guide (2074 citations)
- Event History Analysis (1705 citations)
- Logistic Regression Using the SAS System : Theory and Application (1698 citations)
What are the main themes of his work throughout his whole career to date?
Paul D. Allison spends much of his time researching Econometrics, Statistics, Panel data, Logistic regression and Structural equation modeling.
Paul D. Allison interconnects Goodness of fit, Survival analysis, Linear regression and Regression diagnostic in the investigation of issues within Econometrics.
His research in Survival analysis intersects with topics in Covariate, Standard error and Proportional hazards model.
His study on Regression analysis, Maximum likelihood, Logit and Estimator is often connected to Estimation as part of broader study in Statistics.
In his research on the topic of Logistic regression, Poisson regression is strongly related with Multinomial logistic regression.
The study incorporates disciplines such as Cross lagged, Generalized method of moments, Instrumental variable and Missing data in addition to Structural equation modeling.
He most often published in these fields:
- Econometrics (33.33%)
- Statistics (30.00%)
- Panel data (12.50%)
What were the highlights of his more recent work (between 2015-2021)?
- Panel data (12.50%)
- Econometrics (33.33%)
- Statistics (30.00%)
In recent papers he was focusing on the following fields of study:
His main research concerns Panel data, Econometrics, Statistics, Structural equation modeling and Maximum likelihood.
His work on Causal inference as part of his general Econometrics study is frequently connected to Zero, thereby bridging the divide between different branches of science.
His Causal inference research incorporates elements of Granger causality, Fixed effects model and Generalized least squares.
His Estimator and Logit study in the realm of Statistics interacts with subjects such as Multivariate normal distribution, Transformation and Distribution.
His work carried out in the field of Structural equation modeling brings together such families of science as Cross lagged, Generalized method of moments and Instrumental variable.
Paul D. Allison combines subjects such as Specification, Normality, Missing data and Goodness of fit with his study of Instrumental variable.
Between 2015 and 2021, his most popular works were:
- Maximum Likelihood for Cross-lagged Panel Models with Fixed Effects: (77 citations)
- Mapping Activity Patterns to Quantify Risk of Violent Assault in Urban Environments. (35 citations)
- Linear Dynamic Panel-data Estimation Using Maximum Likelihood and Structural Equation Modeling: (34 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Law
- Social science
His primary areas of study are Econometrics, Structural equation modeling, Panel data, Statistics and Maximum likelihood.
His Econometrics research is multidisciplinary, incorporating elements of First-difference estimator, Consistent estimator and Minimum-variance unbiased estimator.
Structural equation modeling is frequently linked to Cross lagged in his study.
His Cross lagged research includes themes of Granger causality, Moving average, Causal inference and Autoregressive model.
His is doing research in Bias of an estimator, Bayes estimator, Estimation theory, Efficient estimator and Estimator, both of which are found in Statistics.
His Maximum likelihood research integrates issues from Reciprocal determinism, Generalized method of moments and Instrumental variable.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
