Tom Wansbeek

3.4k total citations
78 papers, 1.7k citations indexed

About

Tom Wansbeek is a scholar working on Economics and Econometrics, Statistics and Probability and Marketing. According to data from OpenAlex, Tom Wansbeek has authored 78 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Economics and Econometrics, 18 papers in Statistics and Probability and 12 papers in Marketing. Recurrent topics in Tom Wansbeek's work include Spatial and Panel Data Analysis (16 papers), Consumer Market Behavior and Pricing (12 papers) and Statistical Methods and Inference (11 papers). Tom Wansbeek is often cited by papers focused on Spatial and Panel Data Analysis (16 papers), Consumer Market Behavior and Pricing (12 papers) and Statistical Methods and Inference (11 papers). Tom Wansbeek collaborates with scholars based in Netherlands, United States and Japan. Tom Wansbeek's co-authors include Arie Kapteyn, Jan Roelf Bult, Heinz Neudecker, Erik Meijer, Ruud H. Koning, Wim P. Krijnen, Jos M. F. ten Berge, Michel Wedel, Alexander Shapiro and Paul A. Bekker and has published in prestigious journals such as Journal of the American Statistical Association, Econometrica and The Review of Economics and Statistics.

In The Last Decade

Tom Wansbeek

76 papers receiving 1.6k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Tom Wansbeek Netherlands 23 693 381 210 200 186 78 1.7k
John Liechty United States 17 592 0.9× 746 2.0× 272 1.3× 148 0.7× 352 1.9× 47 1.9k
Dominique Haughton United States 21 246 0.4× 222 0.6× 310 1.5× 217 1.1× 104 0.6× 86 1.6k
Peter Hackl Austria 19 212 0.3× 277 0.7× 216 1.0× 422 2.1× 506 2.7× 50 2.2k
Rick L. Andrews United States 20 890 1.3× 1.1k 2.8× 222 1.1× 68 0.3× 254 1.4× 40 1.8k
Wolfgang Jank United States 23 259 0.4× 597 1.6× 156 0.7× 231 1.2× 607 3.3× 84 1.6k
Peter Lenk United States 22 792 1.1× 912 2.4× 170 0.8× 334 1.7× 347 1.9× 102 2.1k
R. L. Basmann United States 19 711 1.0× 157 0.4× 218 1.0× 265 1.3× 132 0.7× 63 1.5k
Germán Molina United States 14 208 0.3× 125 0.3× 153 0.7× 394 2.0× 101 0.5× 46 1.3k
Dilek Önkal Türkiye 23 309 0.4× 106 0.3× 213 1.0× 64 0.3× 824 4.4× 72 1.8k
Dennis Fok Netherlands 17 334 0.5× 521 1.4× 138 0.7× 21 0.1× 214 1.2× 53 1.0k

Countries citing papers authored by Tom Wansbeek

Since Specialization
Citations

This map shows the geographic impact of Tom Wansbeek's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Tom Wansbeek with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tom Wansbeek more than expected).

Fields of papers citing papers by Tom Wansbeek

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tom Wansbeek. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Tom Wansbeek. The network helps show where Tom Wansbeek may publish in the future.

Co-authorship network of co-authors of Tom Wansbeek

This figure shows the co-authorship network connecting the top 25 collaborators of Tom Wansbeek. A scholar is included among the top collaborators of Tom Wansbeek based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Tom Wansbeek. Tom Wansbeek is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Wessels, Roberto E., Tom Wansbeek, & Lammertjan Dam. (2016). What is the Relation (if any) Between a Firm’s Corporate Governance Arrangements and its Financial Performance?. Multinational Finance Journal. 20(4). 323–354. 3 indexed citations
2.
Wessels, Roberto E. & Tom Wansbeek. (2015). What is the Relation (If Any) between a Firm's Corporate Governance Arrangements and its Financial Performance?. SSRN Electronic Journal. 1 indexed citations
3.
Meijer, Erik, Susann Rohwedder, & Tom Wansbeek. (2011). Measurement Error in Earnings Data: Using a Mixture Model Approach to Combine Survey and Register Data. Journal of Business and Economic Statistics. 30(2). 191–201. 23 indexed citations
4.
Meijer, Erik, Susann Rohwedder, & Tom Wansbeek. (2008). Prediction of Latent Variables in a Mixture of Structural Equation Models, with an Application to the Discrepancy between Survey and Register Data. SSRN Electronic Journal. 2 indexed citations
5.
Wansbeek, Tom, et al.. (1999). Estimating a dynamic panel data model with heterogeneous trends. Annals of Economics and Statistics. 331–349. 11 indexed citations
6.
Berge, Jos M. F. ten, Wim P. Krijnen, Tom Wansbeek, & Alexander Shapiro. (1999). Some new results on correlation-preserving factor scores prediction methods. Linear Algebra and its Applications. 289(1-3). 311–318. 85 indexed citations
7.
Meijer, Erik & Tom Wansbeek. (1999). Quadratic Prediction of Factor Scores. Psychometrika. 64(4). 495–507. 3 indexed citations
8.
Wansbeek, Tom & Michel Wedel. (1998). Marketing and econometrics: Editors' introduction. Journal of Econometrics. 89(1-2). 1–14. 17 indexed citations
9.
Haaijer, Rinus, Michel Wedel, Marco Vriens, & Tom Wansbeek. (1998). Utility Covariances and Context Effects in Conjoint MNP Models. Marketing Science. 17(3). 236–252. 58 indexed citations
10.
Bult, Jan Roelf, et al.. (1997). Interaction between target and mailing characteristics in direct marketing, with an application to health care fund raising. International Journal of Research in Marketing. 14(4). 301–308. 28 indexed citations
11.
Vriens, Marco, et al.. (1996). Predictions in Conjoint Choice Experiments: The X-Factor Probit Model. University of Groningen research database (University of Groningen / Centre for Information Technology).
12.
Bekker, Paul A. & Tom Wansbeek. (1996). Proxies versus omitted variables in regression analysis. Linear Algebra and its Applications. 237-238. 301–312. 3 indexed citations
13.
Wansbeek, Tom, et al.. (1996). Matrix algebra and sampling theory: The case of the Horvitz-Thompson estimator. Linear Algebra and its Applications. 237-238. 225–238. 3 indexed citations
14.
Krijnen, Wim P., Tom Wansbeek, & Jos M. F. ten Berge. (1996). Best linear predictors for factor scores. Communication in Statistics- Theory and Methods. 25(12). 3013–3025. 34 indexed citations
15.
Bekker, Paul A., et al.. (1996). The APT Model as Reduced-Rank Regression. Journal of Business and Economic Statistics. 14(2). 199–199. 1 indexed citations
16.
Koning, Ruud H., Heinz Neudecker, & Tom Wansbeek. (1992). Unbiased estimation of fourth-order matrix moments. Linear Algebra and its Applications. 160. 163–174. 13 indexed citations
17.
Koning, Ruud H., Heinz Neudecker, & Tom Wansbeek. (1991). Block Kronecker products and the vecb operator. Linear Algebra and its Applications. 149. 165–184. 78 indexed citations
18.
Wansbeek, Tom, et al.. (1990). The algebra of multimode factor analysis. Linear Algebra and its Applications. 127. 631–639. 5 indexed citations
19.
Neudecker, Heinz & Tom Wansbeek. (1987). Fourth-order properties of normally distributed random matrices. Linear Algebra and its Applications. 97. 13–21. 21 indexed citations
20.
Wansbeek, Tom, et al.. (1985). ERRORS IN VARIABLES IN ECONOMETRICS: NEW DEVELOPMENTS AND RECURRENT THEMES*. Statistica Neerlandica. 39(2). 129–141. 3 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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