Timo Schmid

585 total citations
36 papers, 341 citations indexed

About

Timo Schmid is a scholar working on Economics and Econometrics, Management Science and Operations Research and Statistics and Probability. According to data from OpenAlex, Timo Schmid has authored 36 papers receiving a total of 341 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Economics and Econometrics, 13 papers in Management Science and Operations Research and 11 papers in Statistics and Probability. Recurrent topics in Timo Schmid's work include Spatial and Panel Data Analysis (15 papers), demographic modeling and climate adaptation (12 papers) and Statistical Methods and Bayesian Inference (5 papers). Timo Schmid is often cited by papers focused on Spatial and Panel Data Analysis (15 papers), demographic modeling and climate adaptation (12 papers) and Statistical Methods and Bayesian Inference (5 papers). Timo Schmid collaborates with scholars based in Germany, United Kingdom and Italy. Timo Schmid's co-authors include Nikos Tzavidis, Nicola Salvati, Li‐Chun Zhang, Ralf Münnich, Emily Midouhas, Eirini Flouri, Matthias Templ, Ulrich Rendtel, Sebastian M. Schmon and Ray Chambers and has published in prestigious journals such as Journal of Statistical Software, Journal of the Royal Statistical Society Series C (Applied Statistics) and Journal of the Royal Statistical Society Series A (Statistics in Society).

In The Last Decade

Timo Schmid

33 papers receiving 322 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Timo Schmid Germany 9 146 99 96 93 38 36 341
Yolanda Marhuenda García Spain 7 154 1.1× 90 0.9× 78 0.8× 105 1.1× 31 0.8× 15 330
Marcello D’Orazio Italy 9 74 0.5× 127 1.3× 59 0.6× 62 0.7× 13 0.3× 21 346
L. Santamaría Spain 5 193 1.3× 111 1.1× 73 0.8× 149 1.6× 25 0.7× 10 344
Ralf Münnich Germany 10 73 0.5× 125 1.3× 54 0.6× 89 1.0× 8 0.2× 63 287
Matthew Sobek United States 11 78 0.5× 23 0.2× 206 2.1× 47 0.5× 16 0.4× 37 359
Matheus Pereira Libório Brazil 11 101 0.7× 16 0.2× 94 1.0× 104 1.1× 39 1.0× 79 380
Colin Wymer United Kingdom 11 100 0.7× 10 0.1× 100 1.0× 24 0.3× 22 0.6× 14 311
R. Carter Hill United States 3 200 1.4× 70 0.7× 31 0.3× 42 0.5× 15 0.4× 4 390
Jonathan Kropko United States 9 82 0.6× 103 1.0× 104 1.1× 9 0.1× 11 0.3× 17 389
Ajax Reynaldo Bello Moreira Brazil 9 151 1.0× 51 0.5× 11 0.1× 60 0.6× 24 0.6× 49 286

Countries citing papers authored by Timo Schmid

Since Specialization
Citations

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

Fields of papers citing papers by Timo Schmid

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Timo Schmid. 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 Timo Schmid. The network helps show where Timo Schmid may publish in the future.

Co-authorship network of co-authors of Timo Schmid

This figure shows the co-authorship network connecting the top 25 collaborators of Timo Schmid. A scholar is included among the top collaborators of Timo Schmid 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 Timo Schmid. Timo Schmid 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.
Schmid, Timo, et al.. (2025). Analysing opportunity cost of care work using mixed effects random forests under aggregated auxiliary data. Journal of the Royal Statistical Society Series C (Applied Statistics). 75(1). 1–20.
2.
Krug, Manfred, et al.. (2024). Latent-Variable Modelling of Ordinal Outcomes in Language Data Analysis. Journal of Quantitative Linguistics. 31(2). 77–106. 2 indexed citations
3.
Newhouse, David, et al.. (2024). Small Area Estimation of Poverty in Four West African Countries by Integrating Survey and Geospatial Data. Journal of Official Statistics. 41(1). 96–124. 1 indexed citations
4.
Newhouse, David, et al.. (2024). Small Area Estimation of Poverty in Four West African Countries by Integrating Survey and Geospatial Data. Washington, DC: World Bank eBooks. 1 indexed citations
5.
Koebe, Till, et al.. (2023). Releasing survey microdata with exact cluster locations and additional privacy safeguards. Humanities and Social Sciences Communications. 10(1).
6.
Salvati, Nicola, et al.. (2023). A Framework for Producing Small Area Estimates Based on Area-Level Models in R. The R Journal. 15(1). 316–341. 5 indexed citations
7.
Schmid, Timo, et al.. (2023). Estimating regional unemployment with mobile network data for Functional Urban Areas in Germany. Statistical Methods & Applications. 33(1). 205–233. 1 indexed citations
8.
Schmid, Timo, et al.. (2023). Small Area with Multiply Imputed Survey Data. Journal of Official Statistics. 39(4). 507–533.
9.
Schmid, Timo, et al.. (2022). Flexible Domain Prediction using Mixed Effects Random Forests. Journal of the Royal Statistical Society Series C (Applied Statistics). 71(5). 1865–1894. 1 indexed citations
10.
Schmid, Timo, et al.. (2021). Domain Prediction with Grouped Income Data. Journal of the Royal Statistical Society Series A (Statistics in Society). 184(4). 1501–1523. 2 indexed citations
11.
Rendtel, Ulrich, et al.. (2021). Kernel density smoothing of composite spatial data on administrative area level. AStA Wirtschafts- und Sozialstatistisches Archiv. 16(1). 25–49. 2 indexed citations
12.
Rendtel, Ulrich, et al.. (2020). Switching Between Different Non-Hierachical Administrative Areas via Simulated Geo-Coordinates: A Case Study for Student Residents in Berlin. Journal of Official Statistics. 36(2). 297–314. 6 indexed citations
13.
Steorts, Rebecca C., Timo Schmid, & Nikos Tzavidis. (2020). Smoothing and Benchmarking for Small Area Estimation. International Statistical Review. 88(3). 580–598. 4 indexed citations
14.
Schmid, Timo, et al.. (2019). A Practical Guide for the Computation of Domain-Level Estimates with the Socio-Economic Panel (and Other Household Surveys). Econstor (Econstor). 1 indexed citations
15.
Schmid, Timo, et al.. (2019). The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators. Journal of Statistical Software. 91(7). 35 indexed citations
16.
Tzavidis, Nikos, et al.. (2018). From Start to Finish: A Framework for the Production of Small Area Official Statistics. Journal of the Royal Statistical Society Series A (Statistics in Society). 181(4). 927–979. 73 indexed citations
17.
Schmid, Timo, et al.. (2017). Construction of regional consumer price indices using small area estimation. ePrints Soton (University of Southampton). 1 indexed citations
18.
Schmid, Timo, Nikos Tzavidis, Ralf Münnich, & Ray Chambers. (2016). Outlier Robust Small‐Area Estimation Under Spatial Correlation. Scandinavian Journal of Statistics. 43(3). 806–826. 16 indexed citations
19.
Borgoni, Riccardo, Paola Del Bianco, Nicola Salvati, Timo Schmid, & Nikos Tzavidis. (2016). Modelling the distribution of health-related quality of life of advanced melanoma patients in a longitudinal multi-centre clinical trial using M-quantile random effects regression. Statistical Methods in Medical Research. 27(2). 549–563. 15 indexed citations
20.
Tzavidis, Nikos, Nicola Salvati, Timo Schmid, Eirini Flouri, & Emily Midouhas. (2015). Longitudinal Analysis of the Strengths and Difficulties Questionnaire Scores of the Millennium Cohort Study Children in England UsingM-Quantile Random-Effects Regression. Journal of the Royal Statistical Society Series A (Statistics in Society). 179(2). 427–452. 34 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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