Ton de Waal

1.5k citations
38 papers · 853 indexed · h-index 10

Ton de Waal

35 papers receiving 734 citations

Peers

Ton de Waal
Comparison fields: 5 of 96
  • Statistics and Probability 246
  • Artificial Intelligence 560
  • Management Science and Operations Research 174
  • Computer Science Applications 35
  • Computational Mathematics 3
Replace Leon Willenborg with:
Leon Willenborg Netherlands
Weiwei Guo China
Laura Zayatz United States
Dong Cheng China
Hanyang Luo China
Sarvnaz Karimi Australia
Szymon Jaroszewicz Poland
Richard A. Derrig United States
Divakaran Liginlal United States
Ton de Waal relative to Leon Willenborg Netherlands Leon Willenborg's profile →
Citations per field
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Leon Willenborg · 1×
Citations per year

Countries citing papers authored by Ton de Waal

Since Specialization
Citations

This map shows the geographic impact of Ton de Waal'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 Ton de Waal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ton de Waal more than expected).

Fields of papers citing papers by Ton de Waal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ton de Waal. 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 Ton de Waal. The network helps show where Ton de Waal may publish in the future.

Co-authorship network

The 10 scholars most cited alongside Ton de Waal, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ton de Waal Line = papers co-authored together Ton de Waal links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20250
3 20212
4
Solving the disclosure auditing problem for secondary cell suppression by means of linear programming
20201
5 20205
6 20184
7 20168
8 20135
9
Imputation of numerical data under linear edit restrictions
20113
10 20102
11
Protection of micro-data subject to edit constraints against Statistical Disclosure
200813
12 20078
13 20067
14
Automatic Edit and Imputation for Business Surveys: The Dutch Contribution to the EUREDIT Project
20059
15
Automatic error localisation for categorical, continuous and integer data
20053
16
A Fast and Simple Algorithm for Automatic Editing of Mixed Data
200320
17
Solving the Error Localization Problem by Means of Vertex Generation
20038
18
Optimal loca lsuppression in microdata
19989
19
Statistical Disclosure Control and Sampling Weights
19979
20
A View on Statistical Disclosure Control for Microdata
199615

About Ton de Waal

Ton de Waal is a scholar working on Statistics and Probability, Artificial Intelligence and Management Science and Operations Research, having authored 38 papers that have together received 853 indexed citations. Recurring topics across this work include Statistical Methods and Bayesian Inference (17 papers), Statistical Methods and Inference (13 papers), Bayesian Modeling and Causal Inference (7 papers), Privacy-Preserving Technologies in Data (7 papers), Advanced Statistical Methods and Models (6 papers), Census and Population Estimation (5 papers), Bayesian Methods and Mixture Models (4 papers) and Data Analysis with R (4 papers). The work is most often cited by research in Statistics and Probability (246 citations), Artificial Intelligence (560 citations) and Management Science and Operations Research (174 citations). Ton de Waal has collaborated with scholars based in Netherlands, United Kingdom and Japan. Frequent co-authors include Leon Willenborg, Jeroen Pannekoek, Natalie Shlomo, Laura Boeschoten, Arnout van Delden, Daniel L. Oberski, Piet Daas, Jeroen K. Vermunt, Marco Di Zio and Katrijn Van Deun. Their work appears in journals such as Journal of the Royal Statistical Society Series A (Statistics in Society), Structural Equation Modeling A Multidisciplinary Journal and Computational Statistics & Data Analysis.

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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