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.
Statistical Methods for Reliability Data
1998784 citationsWayne Nelson, William Q. Meeker et al.Technometricsprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by William Q. Meeker
Since
Specialization
Citations
This map shows the geographic impact of William Q. Meeker'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 William Q. Meeker with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites William Q. Meeker more than expected).
Fields of papers citing papers by William Q. Meeker
This network shows the impact of papers produced by William Q. Meeker. 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 William Q. Meeker. The network helps show where William Q. Meeker may publish in the future.
Co-authorship network of co-authors of William Q. Meeker
This figure shows the co-authorship network connecting the top 25 collaborators of William Q. Meeker.
A scholar is included among the top collaborators of William Q. Meeker 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 William Q. Meeker. William Q. Meeker is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Doganaksoy, Necip, Gerald J. Hahn, & William Q. Meeker. (2017). Fallacies of Statistical Significance. Quality progress. 50(11). 56.1 indexed citations
4.
Meeker, William Q., Gerald J. Hahn, & Luis A. Escobar. (2017). Statistical Intervals. Wiley series in probability and statistics.140 indexed citations
5.
Doganaksoy, Necip, Gerald J. Hahn, & William Q. Meeker. (2016). Identify and Act: Performing product life data analysis with unidentified subpopulations. Quality progress. 49(12). 61.
6.
Meeker, William Q., et al.. (2015). Quantile Probability of Detection: Distinguishing Between Uncertainty and Variability in Nondestructive Testing. Materials Evaluation. 73(1).1 indexed citations
7.
Doganaksoy, Necip, Gerald J. Hahn, & William Q. Meeker. (2014). Timely Reliability Assessment: Using destructive degradation tests. Quality progress. 47(11). 52.1 indexed citations
8.
Meeker, William Q., et al.. (2014). More pitfalls of accelerated tests. Quality Engineering. 59(4). 349–352.3 indexed citations
Doganaksoy, Necip, Gerald J. Hahn, & William Q. Meeker. (2006). How To Analyze Reliability Data For Repairable Products. Quality progress. 39(6). 93.1 indexed citations
11.
Doganaksoy, Necip, Gerald J. Hahn, & William Q. Meeker. (2006). Improving Reliability Through Warranty Data Analysis. Quality progress. 39(11). 63.2 indexed citations
Doganaksoy, Necip, Gerald J. Hahn, & William Q. Meeker. (2003). Reliability analysis by failure mode. Quality Engineering. 48(1). 101–102.16 indexed citations
14.
Doganaksoy, Necip, William Q. Meeker, & Gerald J. Hahn. (2003). Speedier Reliability Analysis. Quality progress. 36(6). 58–63.3 indexed citations
15.
Meeker, William Q., et al.. (2002). A Methodology for the Assessment of the Capability of Inspection Systems for Detection of Subsurface Flaws in Aircraft Turbine Engine Components. Defense Technical Information Center (DTIC).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.