Andrew A. Neath

3.5k total citations · 3 hit papers
31 papers, 2.3k citations indexed

About

Andrew A. Neath is a scholar working on Statistics and Probability, Artificial Intelligence and Statistics, Probability and Uncertainty. According to data from OpenAlex, Andrew A. Neath has authored 31 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Statistics and Probability, 9 papers in Artificial Intelligence and 9 papers in Statistics, Probability and Uncertainty. Recurrent topics in Andrew A. Neath's work include Statistical Methods and Inference (16 papers), Statistical Methods and Bayesian Inference (14 papers) and Advanced Statistical Methods and Models (14 papers). Andrew A. Neath is often cited by papers focused on Statistical Methods and Inference (16 papers), Statistical Methods and Bayesian Inference (14 papers) and Advanced Statistical Methods and Models (14 papers). Andrew A. Neath collaborates with scholars based in United States and Russia. Andrew A. Neath's co-authors include Joseph E. Cavanaugh, Francisco J. Samaniego, Simon Davies, Pamela Newland, Tom Burr, B.D. McVey, Neil C. Schwertman, Joseph Cavanaugh, Ryan Fries and Lin Wang and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of the American Statistical Association and The American Statistician.

In The Last Decade

Andrew A. Neath

29 papers receiving 2.2k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Testing Statistical Hypotheses

2006 732 citations Andrew A. Neath Journal of the American Statistical Association profile →
The Bayesian information criterion: background, derivation, and applications

2011 615 citations Andrew A. Neath, Joseph E. Cavanaugh Wiley Interdisciplinary Reviews Computational Statistics profile →
The Akaike information criterion: Background, derivation, properties, application, interpretation, and refinements

2019 536 citations Joseph E. Cavanaugh, Andrew A. Neath Wiley Interdisciplinary Reviews Computational Statistics profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Andrew A. Neath United States	11	550	392	177	151	146	31	2.3k
Christophe Ley Belgium	17	486 0.9×	559 1.4×	171 1.0×	134 0.9×	109 0.7×	86	4.1k
David M. Allen United States	21	737 1.3×	363 0.9×	138 0.8×	169 1.1×	235 1.6×	109	3.3k
S. L. Singapore	7	357 0.6×	440 1.1×	148 0.8×	127 0.8×	162 1.1×	9	2.0k
Clive Loader United States	14	787 1.4×	404 1.0×	276 1.6×	157 1.0×	157 1.1×	22	2.3k
Richard L. Scheaffer United States	23	622 1.1×	363 0.9×	223 1.3×	149 1.0×	213 1.5×	77	3.8k
Alvin C. Rencher United States	21	489 0.9×	405 1.0×	195 1.1×	155 1.0×	171 1.2×	32	3.7k
Alexander J. Collins United Kingdom	9	356 0.6×	257 0.7×	140 0.8×	65 0.4×	124 0.8×	16	2.5k
Michael R. Chernick United States	27	458 0.8×	234 0.6×	238 1.3×	149 1.0×	221 1.5×	67	3.4k
Scott D. Grimshaw United States	14	394 0.7×	311 0.8×	240 1.4×	213 1.4×	139 1.0×	33	2.2k
Manuel Febrero–Bande Spain	23	959 1.7×	547 1.4×	171 1.0×	276 1.8×	157 1.1×	66	2.6k

Countries citing papers authored by Andrew A. Neath

Since Specialization

Citations

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

Fields of papers citing papers by Andrew A. Neath

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Andrew A. Neath. 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 Andrew A. Neath. The network helps show where Andrew A. Neath may publish in the future.

Co-authorship network of co-authors of Andrew A. Neath

This figure shows the co-authorship network connecting the top 25 collaborators of Andrew A. Neath. A scholar is included among the top collaborators of Andrew A. Neath 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 Andrew A. Neath. Andrew A. Neath is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Cavanaugh, Joseph E. & Andrew A. Neath. (2019). The Akaike information criterion: Background, derivation, properties, application, interpretation, and refinements. Wiley Interdisciplinary Reviews Computational Statistics. 11(3). 536 indexed citations breakdown →

Cavanaugh, Joseph E. & Andrew A. Neath. (2012). Model Selection Criteria Based on Computationally Intensive Estimators of the Expected Optimism. Journal | MESA. 3(4). 343–356.

Neath, Andrew A., et al.. (2012). A Note on the Comparison of the Bayesian and Frequentist Approaches to Estimation. SHILAP Revista de lepidopterología. 2012. 1–12. 1 indexed citations

Neath, Andrew A., et al.. (2012). A bootstrap method for assessing uncertainty in Kullback-Leibler discrepancy model selection problems. Journal | MESA. 3(4). 381–391. 2 indexed citations

Newland, Pamela, et al.. (2012). Symptom Clusters in Women With Relapsing-Remitting Multiple Sclerosis. Journal of Neuroscience Nursing. 44(2). 66–71. 26 indexed citations

Neath, Andrew A. & Joseph E. Cavanaugh. (2011). The Bayesian information criterion: background, derivation, and applications. Wiley Interdisciplinary Reviews Computational Statistics. 4(2). 199–203. 615 indexed citations breakdown →

Burr, Tom, et al.. (2008). Performance of Variable Selection Methods in Regression Using Variations of the Bayesian Information Criterion. Communications in Statistics - Simulation and Computation. 37(3). 507–520. 9 indexed citations

Neath, Andrew A.. (2006). Testing Statistical Hypotheses. Journal of the American Statistical Association. 101(474). 847–848. 732 indexed citations breakdown →

Davies, Simon, Andrew A. Neath, & Joseph E. Cavanaugh. (2005). Cross validation model selection criteria for linear regression based on the Kullback–Leibler discrepancy. Statistical Methodology. 2(4). 249–266. 11 indexed citations

10.

Neath, Andrew A.. (2003). Polya tree distributions for statistical modeling of censored data. Journal of Applied Mathematics and Decision Sciences. 7(3). 175–186. 8 indexed citations

11.

Neath, Andrew A. & Joseph E. Cavanaugh. (2000). A regression model selection criterion based on bootstrap bumping for use with resistant fitting. Computational Statistics & Data Analysis. 35(2). 155–169. 2 indexed citations

12.

Cavanaugh, Joseph E. & Andrew A. Neath. (1999). Generalizing the derivation of the schwarz information criterion. Communication in Statistics- Theory and Methods. 28(1). 49–66. 75 indexed citations

13.

Neath, Andrew A.. (1999). Bayesian Data Analysis. Journal of Quality Technology. 31(1). 127–127. 92 indexed citations

14.

Neath, Andrew A. & Joseph E. Cavanaugh. (1997). Regression and time series model selection using variants of the schwarz information criterion. Communication in Statistics- Theory and Methods. 26(3). 559–580. 59 indexed citations

15.

Neath, Andrew A. & Francisco J. Samaniego. (1997). On Bayesian estimation of the multiple decrement function in the competing risks problem, II: The discrete case. Statistics & Probability Letters. 35(4). 345–354.

16.

Neath, Andrew A. & Francisco J. Samaniego. (1997). On the Efficacy of Bayesian Inference for Nonidentifiable Models. The American Statistician. 51(3). 225–225. 20 indexed citations

17.

Neath, Andrew A. & Francisco J. Samaniego. (1997). On the Efficacy of Bayesian Inference for Nonidentifiable Models. The American Statistician. 51(3). 225–232. 43 indexed citations

18.

Samaniego, Francisco J. & Andrew A. Neath. (1996). How to Be a Better Bayesian. Journal of the American Statistical Association. 91(434). 733–733. 4 indexed citations

19.

Neath, Andrew A. & Francisco J. Samaniego. (1996). On the distinguished role of the multivariate exponential distribution in Bayesian estimation in competing risks problems. Statistics & Probability Letters. 31(1). 69–74. 3 indexed citations

20.

Schwertman, Neil C., et al.. (1989). A monte carlo study of successive difference analysis of growth curve data at random observation times. Journal of Statistical Computation and Simulation. 34(1). 11–28. 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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