What is he best known for?
The fields of study he is best known for:
- Statistics
- Law
- Econometrics
Econometrics, Mixed logit, Estimator, Statistics and Discrete choice are his primary areas of study.
His research on Econometrics often connects related areas such as Frontier.
He combines subjects such as Multinomial logistic regression and Willingness to pay with his study of Mixed logit.
William H. Greene has researched Estimator in several fields, including Fixed effects model, Least squares and Tobit model.
His Discrete choice study combines topics in areas such as Choice set and Data collection.
His Logistic regression research is multidisciplinary, relying on both Multinomial probit and Mixed model.
His most cited work include:
- Applied Choice Analysis: A Primer (2198 citations)
- THE MIXED LOGIT MODEL: THE STATE OF PRACTICE (1382 citations)
- A latent class model for discrete choice analysis: contrasts with mixed logit (1046 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Econometrics, Statistics, Panel data, Mixed logit and Discrete choice.
His Econometrics study combines topics from a wide range of disciplines, such as Estimator and Inefficiency.
His studies in Inefficiency integrate themes in fields like Frontier, Stochastic frontier analysis and Productive efficiency.
His Mixed logit study incorporates themes from Latent class model, Multinomial logistic regression and Willingness to pay.
Discrete choice and Estimation are two areas of study in which he engages in interdisciplinary work.
William H. Greene has included themes like Probit model and Logit in his Probit study.
He most often published in these fields:
- Econometrics (46.48%)
- Statistics (21.93%)
- Panel data (10.70%)
What were the highlights of his more recent work (between 2015-2021)?
- Econometrics (46.48%)
- Cost efficiency (3.92%)
- Statistics (21.93%)
In recent papers he was focusing on the following fields of study:
William H. Greene mainly investigates Econometrics, Cost efficiency, Statistics, Internal medicine and Stochastic frontier analysis.
William H. Greene performs integrative Econometrics and Random effects model research in his work.
His research in the fields of Poisson distribution and Censoring overlaps with other disciplines such as Polychoric correlation and Outcome variable.
His study in the field of Cohort study is also linked to topics like Brain tumor and Antibiotics.
His work carried out in the field of Stochastic frontier analysis brings together such families of science as Technological change, Total factor productivity, Productivity, Outlier and Estimator.
His research integrates issues of Developing country, Mathematical model and Count data in his study of Truncation.
Between 2015 and 2021, his most popular works were:
- Persistent and Transient Productive Inefficiency: A Maximum Simulated Likelihood Approach (89 citations)
- Streetscape features related to pedestrian activity (71 citations)
- Modeling Preference and Willingness to Pay for Drought Tolerance (DT) in Maize in Rural Zimbabwe. (29 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Law
- Normal distribution
His primary scientific interests are in Econometrics, Inefficiency, Stochastic frontier analysis, Statistics and Productivity.
His Econometrics research is multidisciplinary, incorporating perspectives in Quality, Sample, Negative binomial distribution and Mixed logit.
His Mixed logit research incorporates elements of Endogeneity, Choice set, Control function and Discrete choice.
The study incorporates disciplines such as Estimator, Outlier and Normal distribution in addition to Stochastic frontier analysis.
Many of his research projects under Statistics are closely connected to Polychoric correlation with Polychoric correlation, tying the diverse disciplines of science together.
His studies deal with areas such as Production and Agricultural economics as well as Productivity.
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