Cited by 4,316Statistical Methods and Bayesian InferenceStatistical Methods in EpidemiologySurvey Sampling and Estimation Techniques

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Logistic Regression in Rare Events Data

Open access

King, Gary, Zeng, Langche·2001·Political Analysis

DOI 10.1093/oxfordjournals.pan.a004868

Open this paper in CoChat — free account Open PDF

Abstract

We study rare events data, binary dependent variables with dozens to thousands of times fewer ones (events, such as wars, vetoes, cases of political activism, or epidemiological infections) than zeros (“nonevents”). In many literatures, these variables have proven difficult to explain and predict, a problem that seems to have at least two sources. First, popular statistical procedures, such as logistic regression, can sharply underestimate the probability of rare events. We recommend corrections that outperform existing methods and change the estimates of absolute and relative risks by as much as some estimated effects reported in the literature. Second, commonly used data collection strategies are grossly inefficient for rare events data. The fear of collecting data with too few events has led to data collections with huge numbers of observations but relatively few, and poorly measured, explanatory variables, such as in international conflict data with more than a quarter-million dyads, only a few of which are at war. As it turns out, more efficient sampling designs exist for making valid inferences, such as sampling all available events (e.g., wars) and a tiny fraction of nonevents (peace). This enables scholars to save as much as 99% of their (nonfixed) data collection costs or to collect much more meaningful explanatory variables. We provide methods that link these two results, enabling both types of corrections to work simultaneously, and software that implements the methods developed.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1093/oxfordjournals.pan.a004868

Cites

0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Designing Social Inquiry: Scientific Inference in Qualitative Research
King, G., Keohane, R. O., & Verba, S. (1994) · Princeton University Press · doi:10.1515/9781400821211 · Cited by 7,225
Self-citation

Show all 0 references

Research works printed but not linked: 47

Verba, 1995, Voice and Equality: Civic Voluntarism in American Politics, 10.2307/j.ctv1pnc1k7 · DOI
10.1007/978-1-4612-4024-2 · DOI
Smith, 1998, Bayesian Statistics
10.1111/0020-8833.00113 · DOI
10.2307/2585396 · DOI
Scott, 1986, Fitting Logistic Models Under Case-Control or Choice Based Sampling, Journal of the Royal Statistical Society, B, 48, 170, 10.1111/j.2517-6161.1986.tb01400.x · DOI
10.1017/CBO9780511812651 · DOI
10.1093/biomet/66.3.403 · DOI
10.1002/sim.4780140806 · DOI
10.2307/2938740 · DOI
Manski, 1981, Structural Analysis of Discrete Data with Econometric Applications
10.2307/1914121 · DOI · Paper page
Manski, 1999, Nonlinear Statistical Inference: Essays in Honor of Takeshi Amemiya
10.1016/0304-4076(94)01698-4 · DOI
10.1007/978-1-4899-3242-6 · DOI · Paper page
10.2307/2669316 · DOI · Paper page
King Gary , and Zeng Langche . 2000b. “Explaining Rare Events in International Relations.” International Organization (in press).
King Gary , and Zeng Langche . 2000a. “Inference in Case-Control Studies with Limited Auxilliary Information” (in press). (Preprint at http://Gking.harvard.edu.)
10.2307/2951544 · DOI
10.1080/01621459.1985.10478165 · DOI
10.1017/CBO9780511521713 · DOI
10.1007/978-1-4899-4467-2 · DOI
10.1093/biomet/80.1.27 · DOI · Paper page
10.2307/1912755 · DOI
Bueno de Mesquita, 1992, War and Reason: Domestic and International Imperatives, 10.2307/j.ctt1bh4dhm · DOI
Bueno de Mesquita, 1981, The War Trap
10.1080/01621459.1996.10476660 · DOI
10.2307/1913609 · DOI
10.1214/ss/1177011454 · DOI
Breslow, 1980, Statistical Methods in Cancer Research
10.1002/sim.4780020108 · DOI
Rothman, 1998, Modern Epidemiology
Cosslett, 1981b, Structural Analysis of Discrete Data with Econometric Applications
Rosenau, 1976, In Search of Global Patterns
10.1017/CBO9780511583483 · DOI
Levy, 1989, Behavior, Society, and Nuclear War, 1, 2120
McCullagh, 1987, Tensor Methods in Statistics
Mehta, 1997, Exact Inference for Categorical Data
10.1177/0049124189017003003 · DOI
10.2307/1957394 · DOI
10.1017/S0003055400220078 · DOI
Greene, 1993, Econometric Analysis
10.1016/0378-3758(93)E0091-T · DOI
10.1177/0193841X8801200301 · DOI
Lancaster, 1996b, Efficient Estimation and Stratified Sampling, Journal of Econometrics, 74, 289, 10.1016/0304-4076(95)01756-9 · DOI
10.1002/(SICI)1097-0258(19970315)16:5<545::AID-SIM421>3.0.CO;2-3 · DOI
Cordeiro, 1991, Bias Correction in Generalized Linear Models, Journal of the Royal Statistical Society, B, 53, 629, 10.1111/j.2517-6161.1991.tb01852.x · DOI

Sorted by citations

Items 801–900 of 1,016

Underground natural gas storage facility operations and well leakage events in the United States
Lackey, G., Mundia-Howe, M., & Pekney, N. (2024) · Geoenergy Science and Engineering · doi:10.1016/j.geoen.2023.212630 · Cited by 23
Predicting clinical outcomes in COVID-19 using radiomics on chest radiographs
Varghese, B., Shin, H., Desai, B., Gholamrezanezhad, A., Lei, X., Perkins, M., Oberai, A., Nanda, N., Cen, S., & Duddalwar, V. (2021) · British Journal of Radiology · doi:10.1259/bjr.20210221 · Cited by 23
Performance and Characteristics of Wearable Sensor Systems Discriminating and Classifying Older Adults According to Fall Risk: A Systematic Review
Kristoffersson, A., Du, J., & Ehn, M. (2021) · Italian National Conference on Sensors · doi:10.3390/s21175863 · Cited by 23
Detecting the target of sarcasm is hard: Really??
Parameswaran, P., Trotman, A., Liesaputra, V., & Eyers, D. M. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102599 · Cited by 24
What if your boss is a woman? Evidence on gender discrimination at the workplace
Lucifora, C., & Vigani, D. (2021) · Review of Economics of the Household · doi:10.1007/s11150-021-09562-x · Cited by 30
Firm age and loan financing with patents as collateral of Chinese startups: the roles of innovations and experience
Yang, X., Gu, X., & Yang, X. (2021) · Economics of Innovation and New Technology · doi:10.1080/10438599.2021.1916486 · Cited by 23
Long-term impact of systematic pain and sedation management on cognitive, motor, and behavioral outcomes of extremely preterm infants at preschool age
Steinbauer, P., Deindl, P., Fuiko, R., Unterasinger, L., Cardona, F., Wagner, M., Edobor, J., Werther, T., Berger, A., Olischar, M., et al. (2020) · Pediatric Research · doi:10.1038/s41390-020-0979-2 · Cited by 27
Individual internet usage and the availability of online content of local interest: A multilevel approach
Tranos, E., & Stich, C. (2020) · Computers, Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2019.101371 · Cited by 23
“Logistic regression and by extension ordinal regression models signiﬁcantly misjudge the probability of rare events (King and Zeng, 2001).”
Method
Bring More Data!—A Good Advice? Removing Separation in Logistic Regression by Increasing Sample Size
Šinkovec, H., Geroldinger, A., & Heinze, G. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16234658 · Cited by 23
“Despite analytically attractive properties under some regulatory conditions as the sample size increases [1,2], in analyses of small or sparse data sets the properties of ML estimator become questionable: ML coefficient estimates are biased away from zero and very unstable [3] or may even not exist [4].”
Background
Integration of genetic and clinical information to improve imputation of data missing from electronic health records
Li, R., Chen, Y., & Moore, J. H. (2019) · J. Am. Medical Informatics Assoc. · doi:10.1093/jamia/ocz041 · Cited by 23
“For consent groups with a low number of diagnosed patients, their power to detect genetic association could be reduced.(33) Identifying phenotype–genotype associations is one of the major knowledge discoveries being carried out in EHR data.”
Background
Signaling a language of possibility space: Management of a dialogic discourse modality through speculation and reasoning word usage
Boyd, M. P., Chiu, M. M., & Kong, Y. (2019) · Linguistics and Education · doi:10.1016/j.linged.2019.03.002 · Cited by 27
Nonmedical use of prescription drugs for cognitive enhancement as response to chronic stress especially when social support is lacking
Sattler, S. (2018) · Stress and Health · doi:10.1002/smi.2846 · Cited by 24
A question of justice: are holiday clubs serving the most deprived communities in England?
Mann, E. S., Long, M. A., Stretesky, P., & Defeyter, M. (2018) · Local Environment : the International Journal of Justice and Sustainability · doi:10.1080/13549839.2018.1518415 · Cited by 23
“We also estimated the models using rare events logistic regression as there are nearly thirty-one thousand neighbourhoods without a club and only 98 neighbourhoods with one or more clubs (King and Zeng 2001).”
Method
A novel logistic regression model combining semi-supervised learning and active learning for disease classification
Chai, H., Liang, Y., Wang, S., & Shen, H.-W. (2018) · Scientific Reports · doi:10.1038/s41598-018-31395-5 · Cited by 23
Effects of Islamic religiosity on bilateral trust in trade: The case of Turkish exports
Turco, A. L., & Maggioni, D. (2018) · Journal of comparative economics (Print) · doi:10.1016/j.jce.2018.02.001 · Cited by 23
Ethnic Stratification and the Equilibrium of Inequality: Ethnic Conflict in Postcolonial States
Vogt, M. (2017) · International Organization · doi:10.1017/s0020818317000479 · Cited by 52
“King and Zeng 2001.”
Institutions, governmental performance and the rise of political newcomers
Carreras, M. (2017) · European Journal of Political Research · doi:10.1111/1475-6765.12181 · Cited by 23
Synthetic Multiple-Imputation Procedure for Multistage Complex Samples
Zhou, H., Elliott, M., & Raghunathan, T. (2016) · Journal of Official Statistics · doi:10.1515/jos-2016-0011 · Cited by 23
“It is well known that maximum-likelihood estimation of logistic regression models typically suffers from small sample bias, the degree of which is strongly dependent on the number of sample cases in the less frequent of the two categories (King and Zeng 2001).”
Background
The Differentiation of Security Forces and the Onset of Genocidal Violence
Pilster, U., Böhmelt, T., & Tago, A. (2016) · doi:10.1177/0095327x14559515 · Cited by 23
Predicting the evolution of social networks with life cycle events
Sharmeen, F., Arentze, T., & Timmermans, H. (2015) · Transportation · doi:10.1007/s11116-015-9644-8 · Cited by 23
“Sampling leads to biases in the estimation of constants; methods exist to correct for such biases (King and Zeng 2001) which is relevant when the model is to be used for prediction.”
Method
The roles of outlet density and norms in alcohol use disorder
Ahern, J., Balzer, L., & Galea, S. (2015) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2015.03.014 · Cited by 23
“This is, in part, because uncommon outcomes are challenging to analyze due to data sparsity (e.g., the number of outcome events is smaller than the number of confounder strata; Harrell et al., 1996; King and Zeng, 2001; Peduzzi et al., 1996 ).”
Background
The dynamics of hedge fund share restrictions
Hong, X. (2014) · doi:10.1016/j.jbankfin.2014.08.002 · Cited by 23
Low knowledge and anecdotal use of unauthorized online HIV self-test kits among attendees at a street-based HIV rapid testing programme in Spain.
Belza, M., Figueroa, C., Rosales-Statkus, M., Ruiz, M., Vallejo, F., & de la Fuente, L. (2014) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2014.03.1379 · Cited by 23
A Troubled Marriage? Divided Minority Government, Cohabitation, Presidential Powers, President-Parliamentarism and Semi-Presidentialism
Kim, Y. (2014) · Government and Opposition · doi:10.1017/gov.2014.23 · Cited by 23
“rare events logit analysis clustered by country because there is a relatively limited number of these events (King and Zeng 2001).”
BackgroundMethod
Detecting Genetic Interactions in Pathway‐Based Genome‐Wide Association Studies
Huang, A., Martin, E. R., Vance, J. M., & Cai, X. (2014) · Genetic Epidemiology · doi:10.1002/gepi.21803 · Cited by 32
“This imbalance in the dataset may compromise the performance of the logistic regression model [King and Zeng, 2001].”
Method
Due North? Do American Theories of Legislative Committees Apply to Canada?
Raymond, C. D., & Holt, J. (2014) · doi:10.1080/13572334.2013.846546 · Cited by 23
“To do this, we use rare events logistic regression (King & Zeng, 1999a, 1999b; Tomz, King, & Zeng, 1999) because the number of MPs serving on some committees is small, which may lead to underestimated probabilities of such rare events.8 While the technique most often employed by similar studies is…”
Method
ENTREPRENEURSHIP IN RURAL AMERICA ACROSS TYPOLOGIES, GENDER AND MOTIVATION
Figueroa-Armijos, M., & Johnson, T. G. (2013) · Journal of Developmental Entrepreneurship · doi:10.1142/s1084946713500143 · Cited by 21
“King and Zeng (1999) argue this method corrects for data with rare events in the dependent variable, thus generating unbiased estimates of logit coefficients.”
BackgroundMethod
Drug Use and Sex Work: Competing Risk Factors for Newly Acquired HIV in Yunnan, China
Xu, J.-J., Smith, M., Ding, G., Chu, J., Wang, H.-B., Li, Q.-H., Chang, D., Wang, G., Shang, H., Jiang, Y., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0059050 · Cited by 23
Predicting Soft Tissue Sarcoma Response to Neoadjuvant Chemotherapy Using an MRI-Based Delta-Radiomics Approach
Fields, B. K. K., Demirjian, N. L., Cen, S., Varghese, B., Hwang, D., Lei, X., Desai, B., Duddalwar, V., & Matcuk, G. (2023) · Molecular Imaging and Biology · doi:10.1007/s11307-023-01803-y · Cited by 22
Resource Extension and Status Identity: Marriage Ties among Family Business Groups in an Emerging Economy
Chung, C.-N., Lee, Z.-R., & Zhu, H.-J. (2021) · American Journal of Sociology · doi:10.1086/718027 · Cited by 22
“Our choice-based sampling strategy includedall dyadswithmarriage ties and randomlymatched them with dyads in which intermarriage did not occur by using a 1:5 ratio.9 9 The ratio 1:5 offers the optimal efficiency of estimation (King and Zeng 2001).”
BackgroundMethod
Winnowing Out High-psm Candidates: The Adverse Selection Effect of Competitive Public Service Exams
Chen, C.-A., Chen, D.-H., Liao, Z., & Kuo, M.-F. (2020) · International Public Management Journal · doi:10.1080/10967494.2019.1658663 · Cited by 22
Narco-Messages: Competition and Public Communication by Criminal Groups
Phillips, B., & Ríos, V. (2020) · Latin American Politics and Society · doi:10.1017/lap.2019.43 · Cited by 22
“The model is a rare events logistic regression (King and Zeng 2001) because about 95 percent of the observations are zeros.”
Background
More than money? An empirical investigation of socio-psychological drivers of financial citizen participation in the German energy transition
Schall, D. (2020) · Cogent Economics & Finance · doi:10.1080/23322039.2020.1777813 · Cited by 22
“…occurs in situations with much rarer events than in this sample (e.g., decisions of citizens to run for political office or infections by uncommon diseases) and the bias—if even existing—would result in this study’s estimates being too conservative instead of exaggerating (King & Zeng, 2001).”
BackgroundMethodResult
Parental Financial Socialization: Is Too Much Help Leading to Debt Ignorance among College Students?
Letkiewicz, J., Lim, H., Heckman, S. J., & Montalto, C. P. (2019) · Family and Consumer Sciences Research Journal · doi:10.1111/fcsr.12341 · Cited by 22
Perbandingan Metode Regresi Logistik dan Random Forest untuk Klasifikasi Data Imbalanced (Studi Kasus: Klasifikasi Rumah Tangga Miskin di Kabupaten Karangasem, Bali Tahun 2017)
Purwa, T. (2019) · Jurnal Matematika Statistika dan Komputasi · doi:10.20956/jmsk.v16i1.6494 · Cited by 22
“Akan tetapi penggunaan metode Regresi Logistik tidak tepat apabila digunakan pada kondisi data imbalanced (tidak seimbang), dimana model klasifikasi cenderung menihilkan peluang dari kelompok minoritas sehingga hasil prediksi akan cenderung kepada kategori mayoritas [5].”
Background
Multi-Subject Stochastic Blockmodels for Adaptive Analysis of Individual Differences in Human Brain Network Cluster Structure
Pavlović, D., Guillaume, B. R. L., Towlson, E. K., Kuek, N. M. Y., Afyouni, S., Vértes, P., Yeo, T. B. T., Bullmore, E., & Nichols, T. E. (2019) · bioRxiv · doi:10.1101/672071 · Cited by 22
Word Emphasis Prediction for Expressive Text to Speech
Mass, Y., Shechtman, S., Mordechay, M., Hoory, R., Shalom, O. S., Lev, G., & Konopnicki, D. (2018) · Interspeech · doi:10.21437/interspeech.2018-1159 · Cited by 22
“Another method for handling unbalanced data is to apply over/under sampling in the training set and fix the prediction bias [17] as we did in Section 6 for the Logistic Regression classifier (Equation 3).”
BackgroundMethod
The Temporal Dynamics of Neighborhood Disadvantage in Childhood and Subsequent Problem Behavior in Adolescence
Kleinepier, T., & van Ham, M. (2018) · Journal of Youth and Adolescence · doi:10.1007/s10964-018-0878-6 · Cited by 28
Social and Institutional Origins of Political Islam
Brooke, S., & Ketchley, N. (2018) · American Political Science Review · doi:10.1017/s0003055417000636 · Cited by 22
“The maximum likelihood estimator used in logistic regression can give biased estimates if there are only a small number of events on the rarer of the two outcomes (Allison 2012; King and Zeng 2001).”
Method
Confidence Bands for ROC Curves With Serially Dependent Data
Lahiri, K., & Yang, L. (2018) · doi:10.1080/07350015.2015.1073593 · Cited by 22
“In a similar vein, King and Zeng (2001) found that in finite samples the slope parameter of a logit model would be less precisely estimated when the event is rare.”
Background
The School as a Socialization Context
Keppens, G., & Spruyt, B. (2017) · Youth & Society · doi:10.1177/0044118x17722305 · Cited by 41
“King and Zeng (2001), however, point out that the bias in logistic regression due to rare events typically underestimates the parameters.”
Background
Incidence and Risk Factors of Mastitis in Shiraz, Iran: Results of a Cohort Study
Zarshenas, M., Zhao, Y., Poorarian, S., Binns, C., & Scott, J. (2017) · Breastfeeding Medicine · doi:10.1089/bfm.2016.0153 · Cited by 22
“The small number of cases introduced a small case bias 19 and caused a computational problem statistically with maximum likelihood estimation used in the logistic regression analysis, resulting in a wide 95% CI.”
Functional network connectivity predicts treatment outcome during treatment of nicotine use disorder
Wilcox, C., Calhoun, V., Rachakonda, S., Claus, E., Littlewood, R. A., Mickey, J., Arenella, P., & Hutchison, K. (2017) · Psychiatry research. Neuroimaging · doi:10.1016/j.pscychresns.2017.04.011 · Cited by 22
“=3 at 12 weeks for 30 day point prevalence), rendering logistic regression problematic as a method of analysis for measuring the interaction term (FNC*TrGrp), given small sample bias (King and Zeng, 2001).”
Method
Effects of Sequences of Cognitions on Group Performance Over Time
Molenaar, I., & Chiu, M. M. (2017) · Small Group Research · doi:10.1177/1046496416689710 · Cited by 22
“Furthermore, infrequent outcomes ( 25% occurrence) can bias Logit regression results (G. King & Zeng, 2001).”
BackgroundMethod
Repression, Education, and Politically Motivated Cyberattacks
Asal, V. H., Mauslein, J. A., Murdie, A. M., Young, J. K., Cousins, K., & Bronk, C. (2016) · Journal of Global Security Studies · doi:10.1093/jogss/ogw006 · Cited by 19
The spatial structure of foreign subsidiaries and MNE expansion strategy
Jiang, G. F., Holburn, G. L. F., & Beamish, P. W. (2015) · Journal of World Business · doi:10.1016/j.jwb.2015.12.001 · Cited by 24
Knowledge, Proximity and R&D Exodus
Livanis, G., & Lamin, A. (2016) · doi:10.1016/j.respol.2015.09.002 · Cited by 22
Soil and water conservation agriculture in subsistence systems: Determinants of adoption in southwestern Uganda
Turinawe, A., Mugisha, J., & Drake, L. (2015) · Journal of Soil and Water Conservation · doi:10.2489/jswc.70.2.133 · Cited by 22
Entrepreneurship in regions: differentiated impacts of the socio cultural and gender types
Mancilla, C., & Amorós, J. (2015) · doi:10.1108/arla-04-2013-0031 · Cited by 22
The Politics of Selecting the Bench from the Bar: The Legal Profession and Partisan Incentives to Politicize the JudiciaryPreprint
Bonica, A., & Sen, M. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.2577378 · Cited by 14
Statistical Discourse Analysis: A method for modeling online discussion processes
Chiu, M. M., & Fujita, N. (2014) · Journal of Learning Analytics · doi:10.18608/jla.2014.13.5 · Cited by 22
Deception for Drugs: Self-Reported “Doctor Shopping” Among Young Adults
Stogner, J., Sanders, A. N., & Miller, B. (2014) · Journal of the American Board of Family Medicine · doi:10.3122/jabfm.2014.05.140107 · Cited by 22
Statistical discourse analysis of online discussions: informal cognition, social metacognition and knowledge creation
Chiu, M. M., & Fujita, N. (2014) · International Conference on Learning Analytics and Knowledge · doi:10.1145/2567574.2567580 · Cited by 22
“As analyzing rare events (target processes occurred in less than 10% of all messages) with Logit/Probit regressions can bias regression coefficient estimates, King and Zeng’s [30] bias estimator was used to adjust them.”
BackgroundMethod
Not-So-Clean Fun: A Profile of Bath Salt Users Among a College Sample in the United States
Miller, B., & Stogner, J. (2014) · Journal of Psychoactive Drugs · doi:10.1080/02791072.2013.876520 · Cited by 22
“Models of heavily imbalanced outcomes can suffer from great biases even with samples of thousands (King & Zeng 2001).”
BackgroundMethodInfluential
Why Target the “Good Guys”? The Determinants of Terrorism Against NGOs
Murdie, A. M., & Stapley, C. S. (2014) · International Interactions · doi:10.1080/03050629.2013.863192 · Cited by 22
Explaining physical retail store closures in digital times
Kupfer, A., Marchand, A., & Hennig‐Thurau, T. (2024) · Journal of Retailing · doi:10.1016/j.jretai.2024.07.001 · Cited by 21
“To enhance the predictive accuracy of these rare events, we employ a rare-case logistic regression technique with penalized least squares (Firth adjustment) to reduce the potential bias in the maximum likelihood estimates ( Firth, 1993 ; Heinze & Schemper, 2002 ; King & Zeng, 2001 ).”
Cofinancing and infrastructure project outcomes in Chinese lending and overseas development finance
Lu, Y., Springer, C., & Steffen, B. (2024) · World Development · doi:10.1016/j.worlddev.2023.106473 · Cited by 21
An automatic sediment-facies classification approach using machine learning and feature engineering
Lee, A.-S., Enters, D., Huang, J., Liou, S. Y. H., & Zolitschka, B. (2022) · Communications Earth & Environment · doi:10.1038/s43247-022-00631-2 · Cited by 21
“The algorithms were implemented with the balanced class (i.e., facies) weights 61 to deal with our imbalanced facies distribution (Supplementary Fig.”
Method
Temporal Label Smoothing for Early Event Prediction
Yèche, H., Pace, A., Rätsch, G., & Kuznetsova, R. (2022) · International Conference on Machine Learning · doi:10.48550/arxiv.2208.13764 · Cited by 21
“Static class reweighting was used for sepsis or circulatory failure prediction [25; 7] through a balanced cross-entropy, which assigns a higher weight to samples from the minority class [17].”
Method
How Rare Is Rare? How Common Is Common? Empirical Issues Associated With Binary Dependent Variables With Rare Or Common Event Rates
Woo, H., Berns, J. P., & Solanelles, P. (2022) · Organizational Research Methods · doi:10.1177/10944281221083197 · Cited by 21
“…arising from analyzing binary DVs with rare or common event rates using traditional approaches (i.e., logit and probit models), two primary alternative analytic tools have been proposed –– (1) Firth logit (Firth, 1993) and (2) Rare Event (RE) logit regression models (King & Zeng, 2001a, 2001b).”
BackgroundMethodInfluential
Cross-Scanner Harmonization of Neuromelanin-Sensitive MRI for Multisite Studies
Wengler, K., Cassidy, C., van der Pluijm, M., Weinstein, J., Abi-Dargham, A., van de Giessen, E. M., & Horga, G. (2021) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27679 · Cited by 21
Not All Elections Are Created Equal: Election Quality and Civil Conflict
Donno, D., Morrison, K., & Savun, B. (2021) · The Journal of Politics · doi:10.1086/714778 · Cited by 30
Perceived risk factors for severe Covid-19 symptoms and their association with health behaviours: Findings from the HEBECO study
Herbeć, A., Brown, J., Jackson, S., Kale, D., Zatoński, M., Garnett, C., Chadborn, T., & Shahab, L. (2020) · Acta Psychologica · doi:10.1016/j.actpsy.2021.103458 · Cited by 21
An interactive model of democratic peace
Altman, D., Rojas-de-Galarreta, F., & Urdínez, F. (2020) · Journal of Peace Research · doi:10.1177/0022343319883672 · Cited by 40
“19 We performed the correction for the occurrence of rare events as proposed by King & Zeng (2001).”
Method
Learning transferable deep convolutional neural networks for the classification of bacterial virulence factors
Zheng, D., Pang, G., Liu, B., Chen, L., & Yang, J. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa230 · Cited by 20
You’ve Got a Friend: Examining Board Interlock Formation After Financial Restatements
Withers, M. C., Howard, M. D., & Tihanyi, L. (2020) · doi:10.1287/orsc.2019.1319 · Cited by 21
Ecological drivers of Mycobacterium avium subsp. paratuberculosis detection in mongoose (Herpestes ichneumon) using IS900 as proxy
Cunha, M. V., Rosalino, L., Leão, C., Bandeira, V., Fonseca, C., Botelho, A., & Reis, A. C. (2020) · Scientific Reports · doi:10.1038/s41598-020-57679-3 · Cited by 21
Bias control in the analysis of case-control studies with incidence density sampling.
Cheung, Y.-B., Ma, X.-M., Lam, K.-F., Li, J.-L., & Milligan, P. (2019) · International Journal of Epidemiology · doi:10.1093/ije/dyz116 · Cited by 21
When are technocratic cabinets formed?
Brunclík, M., & Pařízek, M. (2019) · Comparative European Politics · doi:10.1057/s41295-018-0124-3 · Cited by 21
Emotion dysregulation as an explanatory factor in the relation between negative affectivity and non-medical use of opioid in a diverse young adult sample
Bakhshaie, J., Rogers, A. H., Kauffman, B. Y., Tran, N., Buckner, J., Ditre, J., & Zvolensky, M. J. (2019) · Addictive Behaviours · doi:10.1016/j.addbeh.2019.02.025 · Cited by 21
Is ADHD, independent of ODD, associated with whether and why college students misuse stimulant medication?
Benson, K., Woodlief, D. T., Flory, K., Siceloff, E. R., Coleman, K., & Lamont, A. E. (2018) · Experimental and Clinical Psychopharmacology · doi:10.1037/pha0000202 · Cited by 26
“King and Zeng (2001) demonstrated that the maximum likelihood estimation used by the conventional logistic model could be biased when the rarer of the two outcomes has a small number of cases.”
Background
Meta-learning for resampling recommendation systems
Smolyakov, D., Korotin, A., Erofeev, P., Papanov, A., & Burnaev, E. (2017) · International Conference on Machine Vision · doi:10.1117/12.2523103 · Cited by 21
Not leaving home: grandmothers and male dispersal in a duolocal human society
He, Q.-Q., Wu, J., Ji, T., Tao, Y., & Mace, R. H. (2016) · Behavioral Ecology · doi:10.1093/beheco/arw053 · Cited by 24
“Thus, in order to minimize biases, relogit regression (King and Zeng 2001a, 2001b; Choirat et al.”
MethodInfluential
Stylistic Accommodation on an Internet Forum as Bonding
Welbers, K., & de Nooy, W. (2014) · American Behavioral Scientist · doi:10.1177/0002764214527086 · Cited by 20
Problembewusstsein und Inanspruchnahme von Hilfe bei substanzbezogenen Problemen
de Matos, E. D., Kraus, L., Pabst, A., & Piontek, D. (2013) · SUCHT - Zeitschrift für Wissenschaft und Praxis / Journal of Addiction Research and Practice · doi:10.1024/0939-5911.a000278 · Cited by 21
Prediction of Intrastate Conflict Using State Structural Factors and Events Data
Tikuisis, P., Carment, D., & Samy, Y. (2013) · Journal of Conflict Resolution · doi:10.1177/0022002712446129 · Cited by 21
Buying Future Endorsements from Prospective Influencers on User-Generated Content Platforms
Lanz, A., Goldenberg, J., Shapira, D., & Stahl, F. (2023) · Journal of Marketing Research · doi:10.1177/00222437231207323 · Cited by 20
“…line) of the probability of false discovery as a function of the selection size S, that is, (1) the logit model using the SMOTE training set, (2) King and Zeng ’ s (2001) oversampling technique including bias correction, and (3) extreme gradient boosting (with the outgoing activity and the…”
BackgroundMethodInfluential
Forecasting weather‐related power outages using weighted logistic regression
Sharma, V., Hong, T., Cecchi, V., Hofmann, A., & Lee, J. (2023) · IET Smart Grid · doi:10.1049/stg2.12109 · Cited by 20
Classification of COVID-19 X-ray images using transfer learning with visual geometrical groups and novel sequential convolutional neural networks
Kumar, S., & Kumar, H. (2023) · MethodsX · doi:10.1016/j.mex.2023.102295 · Cited by 34
“(3) provides the class weights for the individual classes [28] .”
Background
Ethohydraulic experiments on the fish protection potential of the hybrid system FishProtector at hydropower plants
Tutzer, R., Röck, S., Walde, J., Zeiringer, B., Unfer, G., Führer, S., Brinkmeier, B., Haug, J., & Aufleger, M. (2021) · doi:10.1016/j.ecoleng.2021.106370 · Cited by 20
Reproductive consequences of material use in avian nest construction.
Breen, A. J., Healy, S., & Guillette, L. (2021) · Behavioural Processes · doi:10.1016/j.beproc.2021.104507 · Cited by 20
Predicting Visual Search Task Success from Eye Gaze Data as a Basis for User-Adaptive Information Visualization Systems
Spiller, M., Liu, Y., Hossain, M. Z., Gedeon, T., Geissler, J., & Nürnberger, A. (2021) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3446638 · Cited by 20
Open Issues in Statistical Forecasting of Solar Proton Events: A Machine Learning Perspective
Stumpo, M., Benella, S., Laurenza, M., Alberti, T., Consolini, G., & Marcucci, M. F. (2021) · Space Weather · doi:10.1029/2021sw002794 · Cited by 20
“In order to adjust the model with respect the new prior probability of the occurrence of a SPE associated event, the following correction (King & Zeng, 2001) could be introduced without retraining the model:”
BackgroundMethodInfluential
Does political influence distort banking regulation? Evidence from the US
Papadimitri, P., Pasiouras, F., Pescetto, G., & Wohlschlegel, A. (2020) · Journal of Financial Stability · doi:10.1016/j.jfs.2020.100835 · Cited by 24
Does party polarization mobilize or de-mobilize voters? The answer depends on where voters stand
Muñoz, M. M., & Meguid, B. M. (2021) · Electoral Studies · doi:10.1016/j.electstud.2021.102279 · Cited by 42
“Sensitivity Analyses We perform several additional tests to evaluate the robustness of these models and their findings.19 Given the relative infrequency of respondent-reported turnout change between election rounds,20 we rerun the analyses using rare events logit instead (See King and Zeng 2001).”
Method
COMPARISON OF MACHINE LEARNING METHODS IN CLASSIFYING POVERTY IN INDONESIA IN 2018
Sihombing, P. R., & Arsani, A. M. (2021) · Jurnal Teknik Informatika (Jutif) · doi:10.20884/1.jutif.2021.2.1.52 · Cited by 20
“King dan Zeng [11] menyatakan bahwa ketika metode klasifikasi digunakan pada kasus imbalanced data, maka pengklasifikasian cenderung menihilkan peluang dari kelas minoritas karena nilai prediksi akan cenderung pada kelas mayoritas, sehingga tingkat ketepatan klasifikasi yang dihasilkan menjadi kurang baik.”
Differentiation between complicated and uncomplicated appendicitis: diagnostic model development and validation study
Kim, H. Y., Park, J. H., Lee, S.-S., Jeon, J.-J., Yoon, C., & Lee, K. (2020) · Abdominal Radiology · doi:10.1007/s00261-020-02737-7 · Cited by 20
Image retake rates of cone beam computed tomography in a dental institution
Hung, K., Hui, L., Yeung, A. W. K., Scarfe, W., & Bornstein, M. (2020) · Clinical Oral Investigations · doi:10.1007/s00784-020-03315-3 · Cited by 20
Interregional Inequality and Civil Conflict: Are Spatial Disparities a Threat to Stability and Peace?
Ezcurra, R. (2018) · Defence and Peace Economics · doi:10.1080/10242694.2018.1446621 · Cited by 27
“We also employ the so-called ‘rare events’ logit model proposed by King and Zeng (2001), which corrects for any bias inmaximum-likelihood estimation thatmay arise because of the small number of conflict observations relative to the total.”
Method
Determinants of in‐hospital feeding practices in Shiraz, Iran: Results of a prospective cohort study
Zarshenas, M., Zhao, Y., Binns, C. W., & Scott, J. A. (2018) · Birth · doi:10.1111/birt.12385 · Cited by 25
The pyramid of Okun’s coefficient for Italy
Zanin, L. (2016) · Empirica · doi:10.1007/s10663-016-9343-5 · Cited by 20
“Our purpose is to extend and refine the analysis of Okun’s coefficient proposed by Hutengs and Stadtmann (2013) and Zanin (2014). As a case study, we focus on Italy, which is among the most suffering European countries regarding the negative effects associated with the great economic recession related to the financial…”
Influential
Gender differences in associations between cancer-related problems and relationship dissolution among cancer survivors
Stephens, C., Westmaas, J. L., Kim, J., Cannady, R. S., & Stein, K. (2016) · Journal of cancer survivorship · doi:10.1007/s11764-016-0532-9 · Cited by 20
Reverse Mortgage Participation in the United States: Evidence from a National Study
Chatterjee, S. (2016) · International Journal of Financial Studies · doi:10.3390/ijfs4010005 · Cited by 20
Financiamento de campanha e apoio parlamentar à Agenda Legislativa da Indústria na Câmara dos Deputados
Santos, M. L., Batista, M., Filho, D. B. F., & da Rocha, E. C. (2015) · Opinião Pública · doi:10.1590/1807-019121133 · Cited by 17
Screening Out Risk: IGOs, Member State Selection, and Interstate Conflict, 1951–2000
Donno, D., Metzger, S. K., & Russett, B. (2015) · doi:10.1111/isqu.12177 · Cited by 20
Merchant Steering of Consumer Payment Choice: Evidence from a 2012 Diary Survey
Shy, O., & Stavins, J. (2015) · doi:10.2139/ssrn.2533173 · Cited by 20
The Relative Odds of Lifetime Health Conditions and Infectious Diseases Among Men Who Have Sex With Men Compared With a Matched General Population Sample
Swartz, J. A. (2014) · American Journal of Men s Health · doi:10.1177/1557988314533379 · Cited by 25
A replication note on downward nominal and real wage rigidity: survey evidence from European firms
Dias, D. A., Marques, C. R., & Martins, F. (2015) · doi:10.1007/s00181-014-0902-y · Cited by 20
Compliance With New York City’s Beverage Regulations and Beverage Consumption Among Children in Early Child Care Centers
Kakietek, J., Osuji, T., O’Dell, S., Breck, A., & Kettel-Khan, L. (2014) · Preventing Chronic Disease · doi:10.5888/pcd11.130430 · Cited by 20
Going Abroad: Transnational Solicitation and Contention by Ethnopolitical Organizations
Asal, V. H., Conrad, J. M., & White, P. B. (2014) · International Organization · doi:10.1017/s0020818314000228 · Cited by 17
“See Abbot and Snidal 1998; Koremenos, Lipson, and Snidal 2001; Barnett and Finnemore 2004; Lipson 2007; and Checkel 2005.”
Background

Authors: King, G., & Zeng, L.
Affiliations: Harvard University (US)
George Washington University (US)
ORCID: King, Gary
Venue: Political Analysis
Publisher: Cambridge University Press (CUP)
Published: 2001
Volume: 9
Issue: 2
Pages: 137-163
ISSN: 1047-1987 (ISSN-L)
1476-4989
DOI: 10.1093/oxfordjournals.pan.a004868
OpenAlex: W2163848058
Cited by: 4,316
References: 0
Citations by year: 2026: 273 · 2025: 223 · 2024: 261 · 2023: 235 · 2022: 277 · 2021: 362 · 2020: 316 · 2019: 307 · 2018: 236 · 2017: 176 · 2016: 205 · 2015: 189 · 2014: 238 · 2013: 185 · 2012: 142
FWCI: 61.75
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.cambridge.org/core/terms (unspecified, 5847 d)
Locations: Political Analysis (publishedVersion)
Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
RePEc: Research Papers in Economics (submittedVersion)
Funders: National Science Foundation (9729884, 9753126)
University of Cambridge
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Physical Sciences › Mathematics › Statistics and Probability › Survey Sampling and Estimation Techniques
Keywords: rare events, logistic regression, binary dependent variables, sampling design, event sampling, war, risk estimation, data collection efficiency

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Logistic Regression in Rare Events Data

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Metadata

Record

About these checks