What is he best known for?
The fields of study he is best known for:
- Statistics
- Marketing
- Artificial intelligence
Greg M. Allenby mainly investigates Econometrics, Statistics, Marketing, Bayesian probability and Probit model.
His work in the fields of Panel data overlaps with other areas such as Random effects model.
His study in the field of Sample, Multivariate normal distribution, Univariate and Rating scale also crosses realms of Scale.
His Marketing research incorporates elements of Choice based conjoint, Bayesian statistics and Operations research.
His Markov chain Monte Carlo study in the realm of Bayesian probability interacts with subjects such as Interdependence.
His research integrates issues of Mathematical model, Decision theory and Flexibility in his study of Marketing research.
His most cited work include:
- Bayesian Statistics and Marketing (1042 citations)
- Marketing models of consumer heterogeneity (626 citations)
- The Value of Purchase History Data in Target Marketing (566 citations)
What are the main themes of his work throughout his whole career to date?
Greg M. Allenby mainly focuses on Econometrics, Marketing, Bayesian probability, Microeconomics and Statistics.
His Econometrics research includes elements of Bayes estimator, Logistic regression and Aggregate.
His Marketing research is multidisciplinary, incorporating elements of Advertising and Conjoint analysis.
His work deals with themes such as Covariate and Management science, which intersect with Bayesian probability.
In general Microeconomics, his work in Budget constraint and Profit is often linked to Utility model linking many areas of study.
His Logit and Probit model study in the realm of Statistics connects with subjects such as Random effects model.
He most often published in these fields:
- Econometrics (37.06%)
- Marketing (19.29%)
- Bayesian probability (19.29%)
What were the highlights of his more recent work (between 2016-2020)?
- Econometrics (37.06%)
- Microeconomics (14.72%)
- Product design (3.55%)
In recent papers he was focusing on the following fields of study:
Greg M. Allenby focuses on Econometrics, Microeconomics, Product design, Management science and Identification.
His Econometrics study frequently links to other fields, such as Competition.
His work on Production and Budget constraint as part of general Microeconomics research is frequently linked to Utility model and Market data, thereby connecting diverse disciplines of science.
The study incorporates disciplines such as New product development, Marketing and Conjoint analysis in addition to Product design.
His research integrates issues of Bayes estimator and Environmental economics in his study of Marketing.
His research in Covariate focuses on subjects like Variation, which are connected to Bayesian probability.
Between 2016 and 2020, his most popular works were:
- Advancing Non-compensatory Choice Models in Marketing (10 citations)
- Benefit-Based Conjoint Analysis (10 citations)
- Assessment of atherosclerotic luminal narrowing of coronary arteries based on morphometrically generated visual guides. (5 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Marketing
- Artificial intelligence
Greg M. Allenby mostly deals with Econometrics, Identification, Product design, Conjoint analysis and Preference heterogeneity.
In his papers, he integrates diverse fields, such as Econometrics and Separable space.
The Identification study combines topics in areas such as Sequential decision, Management science, Process and Biochemical engineering.
His Product design research includes themes of Bayes estimator, Conjoint model, Marketing and Environmental economics.
His Conjoint analysis study combines topics from a wide range of disciplines, such as Probit model, Similarity, Covariance and Covariance matrix.
His work on Covariate expands to the thematically related Preference heterogeneity.
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.
