Iris Shimizu

828 total citations
21 papers, 636 citations indexed

About

Iris Shimizu is a scholar working on General Health Professions, Economics and Econometrics and Sociology and Political Science. According to data from OpenAlex, Iris Shimizu has authored 21 papers receiving a total of 636 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in General Health Professions, 10 papers in Economics and Econometrics and 5 papers in Sociology and Political Science. Recurrent topics in Iris Shimizu's work include Healthcare Policy and Management (10 papers), Survey Methodology and Nonresponse (5 papers) and Statistical Methods and Bayesian Inference (4 papers). Iris Shimizu is often cited by papers focused on Healthcare Policy and Management (10 papers), Survey Methodology and Nonresponse (5 papers) and Statistical Methods and Bayesian Inference (4 papers). Iris Shimizu collaborates with scholars based in United States and Canada. Iris Shimizu's co-authors include Esther Hing, Richard W Niska, Gordon Scott Bonham, Vladislav Beresovsky, Catharine W. Burt, Kenneth D. Kochanek, Margaret D. Carroll, Frances McCarty, Christopher L. Moriarity and Jennifer D. Parker and has published in prestigious journals such as Journal of the American Statistical Association, Statistics in Medicine and Suicide and Life-Threatening Behavior.

In The Last Decade

Iris Shimizu

21 papers receiving 580 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Iris Shimizu United States 11 146 116 85 82 80 21 636
Judith Green-McKenzie United States 17 162 1.1× 95 0.8× 52 0.6× 78 1.0× 43 0.5× 58 815
Matthew M. Davis United States 16 323 2.2× 186 1.6× 89 1.0× 85 1.0× 69 0.9× 40 806
Jong-Yeon Kim South Korea 13 120 0.8× 147 1.3× 105 1.2× 75 0.9× 71 0.9× 40 678
Stefano Campostrini Italy 17 238 1.6× 184 1.6× 84 1.0× 184 2.2× 130 1.6× 67 991
Elvan Daniels United States 15 274 1.9× 194 1.7× 70 0.8× 90 1.1× 45 0.6× 20 746
Melissa Fox Australia 7 261 1.8× 120 1.0× 171 2.0× 102 1.2× 53 0.7× 10 917
Mary Faith Marshall United States 13 238 1.6× 262 2.3× 72 0.8× 45 0.5× 63 0.8× 35 640
Adrian O’Dowd United Kingdom 11 284 1.9× 116 1.0× 68 0.8× 47 0.6× 32 0.4× 296 670
Atsushi Miyawaki Japan 17 217 1.5× 123 1.1× 101 1.2× 76 0.9× 42 0.5× 66 656
Andrea Moser Canada 13 264 1.8× 92 0.8× 70 0.8× 82 1.0× 35 0.4× 36 481

Countries citing papers authored by Iris Shimizu

Since Specialization
Citations

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

Fields of papers citing papers by Iris Shimizu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Iris Shimizu

This figure shows the co-authorship network connecting the top 25 collaborators of Iris Shimizu. A scholar is included among the top collaborators of Iris Shimizu 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 Iris Shimizu. Iris Shimizu 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.
2.
Parker, Jennifer D., Makram Talih, Donald Malec, et al.. (2017). National Center for Health Statistics Data Presentation Standards for Proportions.. PubMed. 1–22. 256 indexed citations
3.
Hing, Esther, et al.. (2016). Nonresponse Bias in Estimates From the 2012 National Ambulatory Medical Care Survey.. PubMed. 1–42. 18 indexed citations
4.
He, Yulei, Iris Shimizu, Susan M. Schappert, et al.. (2016). A Note on the Effect of Data Clustering on the Multiple-Imputation Variance Estimator: A Theoretical Addendum to the Lewis et al. article in JOS 2014. Journal of Official Statistics. 32(1). 147–164. 3 indexed citations
5.
Goldberg, Elizabeth M., Nathaniel Schenker, Vladislav Beresovsky, et al.. (2014). The Relative Impacts of Design Effects and Multiple Imputation on Variance Estimates: A Case Study with the 2008 National Ambulatory Medical Care Survey. Journal of Official Statistics. 30(1). 147–161. 7 indexed citations
6.
7.
Shimizu, Iris, et al.. (2014). Negative Binomials Regression Model in Analysis of Wait Time at Hospital Emergency Department.. PubMed. 0. 3 indexed citations
8.
Stone, Gregory S., Alden Henderson, Stephanie Davis, et al.. (2011). Lessons from the 2006 Louisiana Health and Population Survey. Disasters. 36(2). 270–290. 2 indexed citations
9.
Niska, Richard W & Iris Shimizu. (2011). Hospital preparedness for emergency response: United States, 2008.. PubMed. 1–14. 50 indexed citations
10.
Dwyer, Lisa L., et al.. (2010). Redesign and operation of the National Home And Hospice Care Survey, 2007.. PubMed. 1–192. 13 indexed citations
11.
Shimizu, Iris, et al.. (2010). Reliability of Relative Standard Errors Computed from NHDS Public Use Data Files. 1 indexed citations
12.
Shimizu, Iris, et al.. (2009). Imputation Variance Estimation by Multiple Imputation Method for the National Hospital Discharge Survey. 2 indexed citations
13.
Sirken, Monroe G. & Iris Shimizu. (2007). Linked surveys of health services utilization. Statistics in Medicine. 26(8). 1788–1801. 1 indexed citations
14.
Claassen, Cynthia A., Madhukar H. Trivedi, Iris Shimizu, et al.. (2006). Epidemiology of Nonfatal Deliberate Self‐Harm in the United States as Described in Three Medical Databases. Suicide and Life-Threatening Behavior. 36(2). 192–212. 53 indexed citations
15.
Hing, Esther, Susan M. Schappert, Catharine W. Burt, & Iris Shimizu. (2005). Effects of form length and item format on response patterns and estimates of physician office and hospital outpatient department visits. National Ambulatory Medical Care Survey and National Hospital Ambulatory Medical Care Survey, 2001.. PubMed. 1–32. 15 indexed citations
16.
Hing, Esther, et al.. (2003). Guide to Using Masked Design Variables to Estimate Standard Errors in Public Use Files of the National Ambulatory Medical Care Survey and the National Hospital Ambulatory Medical Care Survey. INQUIRY The Journal of Health Care Organization Provision and Financing. 40(4). 401–415. 61 indexed citations
17.
Schenker, Nathaniel, Jane F. Gentleman, Deborah Rose, Esther Hing, & Iris Shimizu. (2002). Combining estimates from complementary surveys: a case study using prevalence estimates from national health surveys of households and nursing homes. Public Health Reports. 117(4). 393–407. 12 indexed citations
18.
Shimizu, Iris, et al.. (1988). Sample design, sampling variance, and estimation procedures for the National Ambulatory Medical Care Survey.. PubMed. 1–39. 52 indexed citations
19.
Wd, Mosher, et al.. (1985). National Survey of Family Growth, Cycle III: sample design, weighting, and variance estimation.. PubMed. 1–22. 38 indexed citations
20.
Shimizu, Iris & Gordon Scott Bonham. (1978). Randomized Response Technique in a National Survey. Journal of the American Statistical Association. 73(361). 35–35. 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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