Robert G. Staudte

3.0k total citations
54 papers, 1.4k citations indexed

About

Robert G. Staudte is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty and Management Science and Operations Research. According to data from OpenAlex, Robert G. Staudte has authored 54 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Statistics and Probability, 11 papers in Statistics, Probability and Uncertainty and 7 papers in Management Science and Operations Research. Recurrent topics in Robert G. Staudte's work include Advanced Statistical Methods and Models (21 papers), Statistical Methods and Inference (13 papers) and Statistical Methods and Bayesian Inference (8 papers). Robert G. Staudte is often cited by papers focused on Advanced Statistical Methods and Models (21 papers), Statistical Methods and Inference (13 papers) and Statistical Methods and Bayesian Inference (8 papers). Robert G. Staudte collaborates with scholars based in Australia, Switzerland and United States. Robert G. Staudte's co-authors include Simon J. Sheather, Boris Iglewicz, Elena Kulinskaya, Elvezio Ronchetti, Stephan Morgenthaler, Richard Cowan, Richard Huggins, B.A. Stone, James R. Woodward and Geoffrey B. Fincher and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of the American Statistical Association and Bioinformatics.

In The Last Decade

Robert G. Staudte

51 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Robert G. Staudte Australia 17 865 350 161 114 102 54 1.4k
Douglas G. Simpson United States 24 1.1k 1.3× 455 1.3× 145 0.9× 212 1.9× 55 0.5× 75 1.9k
Ola Hössjer Sweden 22 666 0.8× 250 0.7× 145 0.9× 175 1.5× 127 1.2× 118 1.6k
Jan Hannig United States 21 939 1.1× 322 0.9× 192 1.2× 327 2.9× 81 0.8× 90 1.6k
Mariano J. Valderrama Spain 20 543 0.6× 155 0.4× 97 0.6× 203 1.8× 53 0.5× 56 1.2k
Sasanka Roy India 15 1.2k 1.4× 132 0.4× 392 2.4× 219 1.9× 68 0.7× 64 2.3k
Ibrahim A. Ahmad United States 21 1.1k 1.2× 322 0.9× 233 1.4× 277 2.4× 28 0.3× 101 1.5k
Sadanori Konishi Japan 20 898 1.0× 142 0.4× 167 1.0× 469 4.1× 139 1.4× 88 1.9k
Thomas Mathew United States 25 1.5k 1.8× 729 2.1× 469 2.9× 232 2.0× 62 0.6× 148 2.6k
M. S. Nikulin France 14 592 0.7× 274 0.8× 101 0.6× 180 1.6× 49 0.5× 49 1.3k
Graciela Boente Argentina 18 964 1.1× 308 0.9× 83 0.5× 218 1.9× 23 0.2× 86 1.2k

Countries citing papers authored by Robert G. Staudte

Since Specialization
Citations

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

Fields of papers citing papers by Robert G. Staudte

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Robert G. Staudte. 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 Robert G. Staudte. The network helps show where Robert G. Staudte may publish in the future.

Co-authorship network of co-authors of Robert G. Staudte

This figure shows the co-authorship network connecting the top 25 collaborators of Robert G. Staudte. A scholar is included among the top collaborators of Robert G. Staudte 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 Robert G. Staudte. Robert G. Staudte 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.
Staudte, Robert G. & Aihua Xia. (2018). Divergence from, and Convergence to, Uniformity of Probability Density Quantiles. Entropy. 20(5). 317–317. 1 indexed citations
2.
Staudte, Robert G.. (2016). Inference for quantile measures of kurtosis, peakedness, and tail weight. Communication in Statistics- Theory and Methods. 46(7). 3148–3163. 3 indexed citations
3.
Staudte, Robert G.. (2014). Inference for quantile measures of skewness. Test. 23(4). 751–768. 8 indexed citations
4.
Staudte, Robert G.. (2013). Distribution‐free confidence intervals for the standardized median. Stat. 2(1). 184–196. 3 indexed citations
5.
Malloy, Michael J., Luke A. Prendergast, & Robert G. Staudte. (2012). Transforming the Model T: random effects meta‐analysis with stable weights. Statistics in Medicine. 32(11). 1842–1864. 5 indexed citations
6.
Malloy, Michael J., Luke A. Prendergast, & Robert G. Staudte. (2011). Comparison of methods for fixed effect meta-regression of standardized differences of means. Electronic Journal of Statistics. 5(none). 3 indexed citations
7.
Kulinskaya, Elena, Stephan Morgenthaler, & Robert G. Staudte. (2010). Combining the evidence using stable weights. Research Synthesis Methods. 1(3-4). 284–296. 8 indexed citations
8.
Kulinskaya, Elena, Stephan Morgenthaler, & Robert G. Staudte. (2007). Meta Analysis. Wiley series in probability and statistics. 19 indexed citations
9.
Kulinskaya, Elena & Robert G. Staudte. (2006). Confidence intervals for the standardized effect arising in the comparison of two normal populations. Statistics in Medicine. 26(14). 2853–2871. 8 indexed citations
10.
Kulinskaya, Elena & Robert G. Staudte. (2006). Interval estimates of weighted effect sizes in the one‐way heteroscedastic ANOVA. British Journal of Mathematical and Statistical Psychology. 59(1). 97–111. 22 indexed citations
11.
Kulinskaya, Elena, et al.. (2003). Power Approximations in Testing for Unequal Means in a One-Way ANOVA Weighted for Unequal Variances. Communication in Statistics- Theory and Methods. 32(12). 2353–2371. 25 indexed citations
12.
Blyth, Colin R. & Robert G. Staudte. (1997). Hypothesis Estimates and Acceptability Profiles for 2 × 2 Contingency Tables. Journal of the American Statistical Association. 92(438). 694–699. 8 indexed citations
13.
Staudte, Robert G., et al.. (1997). Weighing the Evidence for Hypotheses with Small Samples of Right-censored Exponential Data. Lifetime Data Analysis. 3(4). 383–398. 2 indexed citations
14.
Ronchetti, Elvezio & Robert G. Staudte. (1994). A Robust Version of Mallows's C P. Journal of the American Statistical Association. 89(426). 550–559. 101 indexed citations
15.
Staudte, Robert G.. (1992). A bifurcating autoregression model for cell lineages with variable generation means. Journal of Theoretical Biology. 156(2). 183–195. 13 indexed citations
16.
Döllinger, Michael & Robert G. Staudte. (1991). Influence Functions of Iteratively Reweighted Least Squares Estimators. Journal of the American Statistical Association. 86(415). 709–716. 27 indexed citations
17.
Döllinger, Michael & Robert G. Staudte. (1991). Influence Functions of Iteratively Reweighted Least Squares Estimators. Journal of the American Statistical Association. 86(415). 709–709. 9 indexed citations
18.
Basawa, I. V., Richard Huggins, & Robert G. Staudte. (1985). Robust tests for time series with an application to first-order autoregressive processes. Biometrika. 72(3). 559–571. 20 indexed citations
19.
Staudte, Robert G.. (1971). A Characterization of Invariant Loss Functions. The Annals of Mathematical Statistics. 42(4). 1322–1327. 2 indexed citations
20.
Staudte, Robert G., et al.. (1970). Complex Roots of Real Characteristic Functions. Proceedings of the American Mathematical Society. 25(2). 238–238.

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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