Wilfried R Vanhonacker
Biographic Data
| ID | 8920327 |
|---|---|
| NAME | Wilfried R Vanhonacker |
| GIVEN NAMES | Wilfried R |
| FAMILY NAME | Vanhonacker |
| SIGNATURE | VANHONACKER W R |
| AFFILIATIONS | INSEAD |
| VERIFIED | No |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 4 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 1988 |
| LATEST PUBLICATION YEAR | 2006 |
| H-INDEX | 1 |
Rewards of technology
Using Meta-Analysis Results in Bayesian Updating
Bayesian estimation incorporating prior information has been a popular approach to gaining estimation efficiency. Although prior information can take a variety of forms, generalizations derived from meta-analyses have been suggested as being useful. This article shows that these priors can be problematic in light of the many empty cells observed in meta-analysis designs. Design reduction, which gives rise to an unbiased prior, is found to be the …
Combining Related and Sparse Data in Linear Regression Models
A Heterogeneous Conditional Logit Model of Choice
Aggregate forecasts using McFadden's conditional logit model of discrete choice harbor the unrealistic implicit assumption of a random-utility distribution that is homogeneous across both alternatives and individuals. This article relaxes that assumption. A choice model is developed that describes the random-utility component as a probability-mixture model. Some numerical results illustrate that the derived model is not constrained by the indepen…
A Heterogeneous Conditional Logit Model of Choice
Aggregate forecasts using McFadden's conditional logit model of discrete choice harbor the unrealistic implicit assumption of a random-utility distribution that is homogeneous across both alternatives and individuals. This article relaxes that assumption. A choice model is developed that describes the random-utility component as a probability-mixture model. Some numerical results illustrate that the derived model is not constrained by the indepen…
Combining Related and Sparse Data in Linear Regression Models
Using Meta-Analysis Results in Bayesian Updating
Bayesian estimation incorporating prior information has been a popular approach to gaining estimation efficiency. Although prior information can take a variety of forms, generalizations derived from meta-analyses have been suggested as being useful. This article shows that these priors can be problematic in light of the many empty cells observed in meta-analysis designs. Design reduction, which gives rise to an unbiased prior, is found to be the …
Rewards of technology
Econometrics (2 works) · Economics (2 works) · Mathematics (2 works) · Statistics (2 works) · Advanced Statistical Process Monitoring (1 work) · Art (1 work) · Artificial Intelligence (1 work) · Bayes estimator (1 work) · Bayesian probability (1 work) · Business (1 work)