Generalized Additive Models.

Regression shrinkage and selection via the lasso

Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482

Deep learning in neural networks: an overview

Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570

Bayesian Measures of Model Complexity and Fit

Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & Van Der Linde, A. (2002) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00353 · Cited by 151

brms : An R Package for Bayesian Multilevel Models Using Stan

Bürkner, P.-C. (2017) · Journal of Statistical Software · doi:10.18637/jss.v080.i01 · Cited by 9,476

A working guide to boosted regression trees

Elith, J., Leathwick, J. R., & Hastie, T. (2008) · Journal of Animal Ecology · doi:10.1111/j.1365-2656.2008.01390.x · Cited by 6,484

Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation

Phillips, S. J., & Dudík, M. (2008) · Ecography · doi:10.1111/j.0906-7590.2008.5203.x · Cited by 5,890

Methods to account for spatial autocorrelation in the analysis of species distributional data: a review.

F. Dormann, C., M. McPherson, J., B. Araújo, M., Bivand, R., Bolliger, J., Carl, G., G. Davies, R., Hirzel, A., Jetz, W., Daniel Kissling, W., et al. (2007) · Ecography · doi:10.1111/j.2007.0906-7590.05171.x · Cited by 3,433

Generalized additive models for location, scale and shape.

Rigby, R. A., & Stasinopoulos, D. M. (2005) · Journal of the Royal Statistical Society Series C: Applied Statistics · doi:10.1111/j.1467-9876.2005.00510.x · Cited by 3,228

Sample selection bias and presence-only distribution models: Implications for background and pseudo-absence data

Phillips, S. J., Dudík, M., Elith, J., Graham, C. H., Lehmann, A., Leathwick, J., & Ferrier, S. (2009) · Ecological Applications · doi:10.1890/07-2153.1 · Cited by 2,996

Multiple imputation of discrete and continuous data by fully conditional specification

van Buuren, S. (2007) · Statistical Methods in Medical Research · doi:10.1177/0962280206074463 · Cited by 2,827

The art of modelling range-shifting species: The art of modelling range-shifting species

Elith, J., Kearney, M., & Phillips, S. (2010) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2010.00036.x · Cited by 2,813

Distributed lag non‐linear models

Gasparrini, A., Armstrong, B., & Kenward, M. G. (2010) · Statistics in Medicine · doi:10.1002/sim.3940 · Cited by 2,481

Generalized linear and generalized additive models in studies of species distributions: setting the scene

Guisan, A., Edwards, T. C., & Hastie, T. (2002) · Ecological Modelling · doi:10.1016/s0304-3800(02)00204-1 · Cited by 2,356

Dichotomizing continuous predictors in multiple regression: a bad idea

Royston, P., Altman, D. G., & Sauerbrei, W. (2006) · Statistics in Medicine · doi:10.1002/sim.2331 · Cited by 2,312

Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission

Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2788613 · Cited by 1,685

The use of fractional polynomials to model continuous risk variables in epidemiology

Royston, P., Ambler, G., & Sauerbrei, W. (1999) · International Journal of Epidemiology · doi:10.1093/ije/28.5.964 · Cited by 1,066

Quantitative magnetic resonance imaging of human brain development: ages 4–18

Giedd, J. N., Snell, J. W., Lange, N., Rajapakse, J. C., Casey, B. J., Kozuch, P. L., Vaituzis, A. C., Vauss, Y. C., Hamburger, S. D., Kaysen, D., et al. (1996) · Cerebral Cortex · doi:10.1093/cercor/6.4.551 · Cited by 1,024

Global Pyrogeography: the Current and Future Distribution of Wildfire

Krawchuk, M. A., Moritz, M. A., Parisien, M.-A., Van Dorn, J., & Hayhoe, K. (2009) · PLoS ONE · doi:10.1371/journal.pone.0005102 · Cited by 1,000

Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook

Lee, D., Hosanagar, K., & Nair, H. S. (2018) · Management Science · doi:10.1287/mnsc.2017.2902 · Cited by 1,069

Pre-eclampsia rates in the United States, 1980-2010: age-period-cohort analysis

Ananth, C. V., Keyes, K. M., & Wapner, R. J. (2013) · BMJ · doi:10.1136/bmj.f6564 · Cited by 940

Ocean's least productive waters are expanding

Polovina, J. J., Howell, E. A., & Abecassis, M. (2008) · Geophysical Research Letters · doi:10.1029/2007gl031745 · Cited by 836

Serum Vaccine Antibody Concentrations in Children Exposed to Perfluorinated Compounds

Grandjean, P., Andersen, E. W., Budtz-Jørgensen, E., Nielsen, F., Mølbak, K., Weihe, P., & Heilmann, C. (2012) · JAMA · doi:10.1001/jama.2011.2034 · Cited by 809

On the use of generalized additive models in time-series studies of air pollution and health

Dominici, F. (2002) · American Journal of Epidemiology · doi:10.1093/aje/kwf062 · Cited by 728

Prevalence of Symptomatic Androgen Deficiency in Men

Araujo, A. B., Esche, G. R., Kupelian, V., O’Donnell, A. B., Travison, T. G., Williams, R. E., Clark, R. V., & McKinlay, J. B. (2007) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2007-1245 · Cited by 720

Interpretable Decision Sets: A Joint Framework for Description and Prediction

Lakkaraju, H., Bach, S. H., & Leskovec, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939874 · Cited by 687

Time trends in the incidence of type 1 diabetes in Finnish children: a cohort study

Harjutsalo, V., Sjöberg, L., & Tuomilehto, J. (2008) · The Lancet · doi:10.1016/s0140-6736(08)60765-5 · Cited by 666

The uncertain nature of absences and their importance in species distribution modelling

Lobo, J. M., Jiménez‐Valverde, A., & Hortal, J. (2010) · Ecography · doi:10.1111/j.1600-0587.2009.06039.x · Cited by 656

Untitled work

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A Longitudinal Study of Cardiorespiratory Fitness and Cognitive Function in Healthy Older Adults

Barnes, D. E., Yaffe, K., Satariano, W. A., & Tager, I. B. (2003) · Journal of the American Geriatrics Society · doi:10.1046/j.1532-5415.2003.51153.x · Cited by 608

A review of spline function procedures in R

Perperoglou, A., Sauerbrei, W., Abrahamowicz, M., & Schmid, M. (2019) · BMC Medical Research Methodology · doi:10.1186/s12874-019-0666-3 · Cited by 585

Modeling chemical processes using prior knowledge and neural networks'

Thompson, M. L., & Kramer, M. A. (1994) · AIChE Journal · doi:10.1002/aic.690400806 · Cited by 596

Interpretable Machine Learning in Healthcare

Ahmad, M. A., Eckert, C., & Teredesai, A. (2018) · Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics · doi:10.1145/3233547.3233667 · Cited by 583

Model Choice in Time Series Studies of Air Pollution and Mortality

Peng, R. D., Dominici, F., & Louis, T. A. (2006) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/j.1467-985x.2006.00410.x · Cited by 583

Season, sex, age, and education as modifiers of the effects of outdoor air pollution on daily mortality in Shanghai, China: the Public Health and Air Pollution in Asia (PAPA) Study

Kan, H., London, S. J., Chen, G., Zhang, Y., Song, G., Zhao, N., Jiang, L., & Chen, B. (2008) · Environmental Health Perspectives · doi:10.1289/ehp.10851 · Cited by 582

Accurate intelligible models with pairwise interactions

Lou, Y., Caruana, R., Gehrke, J., & Hooker, G. (2013) · Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2487575.2487579 · Cited by 564

Intelligible models for classification and regression

Lou, Y., Caruana, R., & Gehrke, J. (2012) · Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2339530.2339556 · Cited by 547

Global biomass burning: a synthesis and review of Holocene paleofire records and their controls

Marlon, J. R., Bartlein, P. J., Daniau, A.-L., Harrison, S. P., Maezumi, S. Y., Power, M. J., Tinner, W., & Vanniére, B. (2013) · Quaternary Science Reviews · doi:10.1016/j.quascirev.2012.11.029 · Cited by 438

Advancing paternal age and risk of autism: new evidence from a population-based study and a meta-analysis of epidemiological studies

Hultman, C. M., Sandin, S., Levine, S. Z., Lichtenstein, P., & Reichenberg, A. (2011) · Molecular Psychiatry · doi:10.1038/mp.2010.121 · Cited by 444

Association of body mass index with health care expenditures in the united states by age and sex

Ward, Z. J., Bleich, S. N., Long, M. W., & Gortmaker, S. L. (2021) · PLOS ONE · doi:10.1371/journal.pone.0247307 · Cited by 428

Association of urinary phthalate metabolite concentrations with body mass index and waist circumference: a cross-sectional study of NHANES data, 1999–2002

Hatch, E. E., Nelson, J. W., Qureshi, M. M., Weinberg, J., Moore, L. L., Singer, M., & Webster, T. F. (2008) · Environmental Health · doi:10.1186/1476-069x-7-27 · Cited by 436

Effects of a universal classroom behavior management program in first and second grades on young adult behavioral, psychiatric, and social outcomes

Kellam, S. G., Brown, C. H., Poduska, J. M., Ialongo, N. S., Wang, W., Toyinbo, P., Petras, H., Ford, C., Windham, A., & Wilcox, H. C. (2008) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2008.01.004 · Cited by 431

Does microdamage accumulation affect the mechanical properties of bone?

Burr, D. B., Turner, C. H., Naick, P., Forwood, M. R., Ambrosius, W., Sayeed Hasan, M., & Pidaparti, R. (1998) · Journal of Biomechanics · doi:10.1016/s0021-9290(98)00016-5 · Cited by 417

Predictability of biomass burning in response to climate changes

Daniau, A. ‐., Bartlein, P. J., Harrison, S. P., Prentice, I. C., Brewer, S., Friedlingstein, P., Harrison‐Prentice, T. I., Inoue, J., Izumi, K., Marlon, J. R., et al. (2012) · Global Biogeochemical Cycles · doi:10.1029/2011gb004249 · Cited by 417

A new malaria vector in Africa: Predicting the expansion range of Anopheles stephensi and identifying the urban populations at risk

Sinka, M. E., Pironon, S., Massey, N. C., Longbottom, J., Hemingway, J., Moyes, C. L., & Willis, K. J. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2003976117 · Cited by 385

Computer game play reduces intrusive memories of experimental trauma via reconsolidation update mechanisms

James, E. L., Bonsall, M. B., Hoppitt, L., Tunbridge, E. M., Geddes, J. R., Milton, A. L., & Holmes, E. A. (2015) · Psychological Science · doi:10.1177/0956797615583071 · Cited by 333

Differentiating the effects of fine and coarse particles on daily mortality in Shanghai, China

Kan, H., London, S. J., Chen, G., Zhang, Y., Song, G., Zhao, N., Jiang, L., & Chen, B. (2007) · Environment International · doi:10.1016/j.envint.2006.12.001 · Cited by 339

Long-term impacts of prescribed burning on regional extent and incidence of wildfires-Evidence from 50 years of active fire management in SW Australian forests

Boer, M. M., Sadler, R. J., Wittkuhn, R. S., McCaw, L., & Grierson, P. F. (2009) · Forest Ecology and Management · doi:10.1016/j.foreco.2009.10.005 · Cited by 309

Shape constrained additive models

Pya, N., & Wood, S. N. (2015) · Statistics and Computing · doi:10.1007/s11222-013-9448-7 · Cited by 315

Percentage of Gestational Diabetes Mellitus Attributable to Overweight and Obesity

Kim, S. Y., England, L., Wilson, H. G., Bish, C., Satten, G. A., & Dietz, P. (2010) · American Journal of Public Health · doi:10.2105/ajph.2009.172890 · Cited by 302

Post-fire vegetation and fuel development influences fire severity patterns in reburns

Coppoletta, M., Merriam, K. E., & Collins, B. M. (2016) · Ecological Applications · doi:10.1890/15-0225 · Cited by 295

Biomass production in switchgrass across the United States: Database description and determinants of yield

Wullschleger, S. D., Davis, E. B., Borsuk, M. E., Gunderson, C. A., & Lynd, L. R. (2010) · Agronomy Journal · doi:10.2134/agronj2010.0087 · Cited by 289

Combining Evidence on Air Pollution and Daily Mortality from the 20 Largest US Cities: A Hierarchical Modelling Strategy

Dominici, F., Samet, J. M., & Zeger, S. L. (2000) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/1467-985x.00170 · Cited by 286

Preventing intrusive memories after trauma via a brief intervention involving Tetris computer game play in the emergency department: A proof-of-concept randomized controlled trial

Iyadurai, L., Blackwell, S. E., Meiser-Stedman, R., Watson, P. C., Bonsall, M. B., Geddes, J. R., Nobre, A. C., & Holmes, E. A. (2018) · Molecular Psychiatry · doi:10.1038/mp.2017.23 · Cited by 266

Bayesian Inference for Comparative Research

Western, B., & Jackman, S. (1994) · American Political Science Review · doi:10.2307/2944713 · Cited by 257

Inflammation and nutritional science for programs/policies and interpretation of research evidence (INSPIRE)

Raiten, D. J., Ashour, F. A. S., Ross, A. C., Meydani, S. N., Dawson, H. D., Stephensen, C. B., Brabin, B. J., Suchdev, P. S., & van Ommen, B. (2015) · The Journal of Nutrition · doi:10.3945/jn.114.194571 · Cited by 234

Benchmarking Computational Doublet-Detection Methods for Single-Cell RNA Sequencing Data

Xi, N. M., & Li, J. J. (2021) · Cell Systems · doi:10.1016/j.cels.2020.11.008 · Cited by 208

“How many images do I need?” Understanding how sample size per class affects deep learning model performance metrics for balanced designs in autonomous wildlife monitoring

Shahinfar, S., Meek, P., & Falzon, G. (2020) · Ecological Informatics · doi:10.1016/j.ecoinf.2020.101085 · Cited by 229

Estimating state of charge and health of lithium-ion batteries with guided waves using built-in piezoelectric sensors/actuators

Ladpli, P., Kopsaftopoulos, F., & Chang, F.-K. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.02.056 · Cited by 214

Multivariable Modeling Strategies

Harrell, F. E. (2015) · Springer Series in Statistics · doi:10.1007/978-3-319-19425-7_4 · Cited by 221

Global habitat preferences of commercially valuable tuna

Arrizabalaga, H., Dufour, F., Kell, L., Merino, G., Ibaibarriaga, L., Chust, G., Irigoien, X., Santiago, J., Murua, H., Fraile, I., et al. (2015) · Deep Sea Research Part II: Topical Studies in Oceanography · doi:10.1016/j.dsr2.2014.07.001 · Cited by 194

Mortality in type 1 diabetes in the DCCT/EDIC versus the general population

The Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) Study Research Group (2016) · Diabetes Care · doi:10.2337/dc15-2399 · Cited by 194

Mortality Risk Score Prediction in an Elderly Population Using Machine Learning

Rose, S. (2013) · American Journal of Epidemiology · doi:10.1093/aje/kws241 · Cited by 193

Modelling potential spawning habitat of sardine ( Sardina pilchardus ) and anchovy ( Engraulis encrasicolus ) in the Bay of Biscay

PLANQUE, B., BELLIER, E., & LAZURE, P. (2007) · Fisheries Oceanography · doi:10.1111/j.1365-2419.2006.00411.x · Cited by 184

Climate change, biological invasions, and the shifting distribution of Mediterranean fishes: A large‐scale survey based on local ecological knowledge

Azzurro, E., Sbragaglia, V., Cerri, J., Bariche, M., Bolognini, L., Ben Souissi, J., Busoni, G., Coco, S., Chryssanthi, A., Fanelli, E., et al. (2019) · Global Change Biology · doi:10.1111/gcb.14670 · Cited by 179

Methods for testing theory and evaluating impact in randomized field trials: Intent-to-treat analyses for integrating the perspectives of person, place, and time

Brown, C. H., Wang, W., Kellam, S. G., Muthén, B. O., Petras, H., Toyinbo, P., Poduska, J., Ialongo, N., Wyman, P. A., Chamberlain, P., et al. (2008) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2007.11.013 · Cited by 177

Association of subclinical hearing loss with cognitive performance

Golub, J. S., Brickman, A. M., Ciarleglio, A. J., Schupf, N., & Luchsinger, J. A. (2020) · JAMA Otolaryngology–Head & Neck Surgery · doi:10.1001/jamaoto.2019.3375 · Cited by 176

Particulate matter, sulfur dioxide, and daily mortality in Chongqing China

Venners, S. A., Wang, B., Xu, Z., Schlatter, Y., Wang, L., & Xu, X. (2003) · Environmental Health Perspectives · doi:10.1289/ehp.5664 · Cited by 173

Geoclimatic influences on invasive aspergillosis after hematopoietic stem cell transplantation

Panackal, A. A., Li, H., Kontoyiannis, D. P., Mori, M., Perego, C. A., Boeckh, M., & Marr, K. A. (2010) · Clinical Infectious Diseases · doi:10.1086/652761 · Cited by 169

Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning

Walmsley, M., Smith, L., Lintott, C., Gal, Y., Bamford, S., Dickinson, H., Fortson, L., Kruk, S., Masters, K., Scarlata, C., et al. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz2816 · Cited by 160

Behavioral Research Methods in Human-Computer Interaction

Landauer, T. K. (1997) · Handbook of Human-Computer Interaction · doi:10.1016/b978-044481862-1.50075-3 · Cited by 162

Albacore (Thunnus alalunga) fishing ground in relation to oceanographic conditions in the western North Pacific Ocean using remotely sensed satellite data

ZAINUDDIN, M., SAITOH, K., & SAITOH, S. (2008) · Fisheries Oceanography · doi:10.1111/j.1365-2419.2008.00461.x · Cited by 154

Leukaemia and myeloid malignancy among people exposed to low doses (<100 mSv) of ionising radiation during childhood: a pooled analysis of nine historical cohort studies

Little, M. P., Wakeford, R., Borrego, D., French, B., Zablotska, L. B., Adams, M. J., Allodji, R., de Vathaire, F., Lee, C., Brenner, A. V., et al. (2018) · The Lancet Haematology · doi:10.1016/s2352-3026(18)30092-9 · Cited by 152

The Wildlife Picture Index: monitoring top trophic levels

O'Brien, T. G., Baillie, J. E. M., Krueger, L., & Cuke, M. (2010) · Animal Conservation · doi:10.1111/j.1469-1795.2010.00357.x · Cited by 151

Habitat characteristics of skipjack tuna ( Katsuwonus pelamis ) in the western North Pacific: a remote sensing perspective

MUGO, R., SAITOH, S., NIHIRA, A., & KUROYAMA, T. (2010) · Fisheries Oceanography · doi:10.1111/j.1365-2419.2010.00552.x · Cited by 149

Lumbar Lordosis and Pelvic Inclination in Adults With Chronic Low Back Pain

Youdas, J. W., Garrett, T. R., Egan, K. S., & Therneau, T. M. (2000) · Physical Therapy · doi:10.1093/ptj/80.3.261 · Cited by 146

Biotic and Climatic Velocity Identify Contrasting Areas of Vulnerability to Climate Change

Carroll, C., Lawler, J. J., Roberts, D. R., & Hamann, A. (2015) · PLOS ONE · doi:10.1371/journal.pone.0140486 · Cited by 143

Circulating 25-hydroxyvitamin D3 in pregnancy and infant neuropsychological development

Morales, E., Guxens, M., Llop, S., Rodríguez-Bernal, C. L., Tardón, A., Riaño, I., Ibarluzea, J., Lertxundi, N., Espada, M., Rodriguez, A., et al. (2012) · Pediatrics · doi:10.1542/peds.2011-3289 · Cited by 140

Effect of A1C and glucose on postoperative mortality in noncardiac and cardiac surgeries

van den Boom, W., Schroeder, R. A., Manning, M. W., Setji, T. L., Fiestan, G.-O., & Dunson, D. B. (2018) · Diabetes Care · doi:10.2337/dc17-2232 · Cited by 140

Determining clinically meaningful decline in preclinical Alzheimer disease

Insel, P. S., Weiner, M., Mackin, R. S., Mormino, E., Lim, Y. Y., Stomrud, E., Palmqvist, S., Masters, C. L., Maruff, P. T., Hansson, O., et al. (2019) · Neurology · doi:10.1212/wnl.0000000000007831 · Cited by 128

Effects of ambient particulate matter and ozone on daily mortality in Rotterdam, the Netherlands

Hoek, G., Schwartz, J. D., Groot, B., & Eilers, P. (1997) · Archives of Environmental Health An International Journal · doi:10.1080/00039899709602224 · Cited by 133

Variability of human brain structure size: ages 4–20 years

Lange, N., Giedd, J. N., Xavier Castellanos, F., Vaituzis, A. C., & Rapoport, J. L. (1997) · Psychiatry Research: Neuroimaging · doi:10.1016/s0925-4927(96)03054-5 · Cited by 134

Predictive mapping of reef fish species richness, diversity and biomass in Zanzibar using IKONOS imagery and machine-learning techniques

Knudby, A., LeDrew, E., & Brenning, A. (2010) · Remote Sensing of Environment · doi:10.1016/j.rse.2010.01.007 · Cited by 129

Stochastic earned value analysis using Monte Carlo simulation and statistical learning techniques

Acebes, F., Pereda, M., Poza, D., Pajares, J., & Galán, J. M. (2015) · International Journal of Project Management · doi:10.1016/j.ijproman.2015.06.012 · Cited by 120

Automobile insurance classification ratemaking based on telematics driving data

Huang, Y., & Meng, S. (2019) · Decision Support Systems · doi:10.1016/j.dss.2019.113156 · Cited by 119

Improved marketing decision making in a customer churn prediction context using generalized additive models

Coussement, K., Benoit, D. F., & Van den Poel, D. (2010) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.07.029 · Cited by 117

Plant species as predictors of soil pH: Replacing expert judgement with measurements

Wamelink, G. W. W., Goedhart, P. W., Van Dobben, H. F., & Berendse, F. (2005) · Journal of Vegetation Science · doi:10.1111/j.1654-1103.2005.tb02386.x · Cited by 116

Estimating and Modelling Bias of the Hierarchical Partitioning Public-Domain Software: Implications in Environmental Management and Conservation

Olea, P. P., Mateo-Tomás, P., & de Frutos, Á. (2010) · PLoS ONE · doi:10.1371/journal.pone.0011698 · Cited by 109

A flexible, interpretable framework for assessing sensitivity to unmeasured confounding

Dorie, V., Harada, M., Carnegie, N. B., & Hill, J. (2016) · Statistics in Medicine · doi:10.1002/sim.6973 · Cited by 104

Distribution and Habitat Associations of Billfish and Swordfish Larvae across Mesoscale Features in the Gulf of Mexico

Rooker, J. R., Simms, J. R., Wells, R. J. D., Holt, S. A., Holt, G. J., Graves, J. E., & Furey, N. B. (2012) · PLoS ONE · doi:10.1371/journal.pone.0034180 · Cited by 105

Enhancing Explainability of Neural Networks Through Architecture Constraints

Yang, Z., Zhang, A., & Sudjianto, A. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3007259 · Cited by 106

Improved predictive models for acute kidney injury with IDEA: Intraoperative Data Embedded Analytics.

Adhikari, L., Ozrazgat-Baslanti, T., Ruppert, M., Madushani, R. W. M. A., Paliwal, S., Hashemighouchani, H., Zheng, F., Tao, M., Lopes, J. M., Li, X., et al. (2019) · PLOS ONE · doi:10.1371/journal.pone.0214904 · Cited by 98

Physico-chemical properties and gestational diabetes predict transplacental transfer and partitioning of perfluoroalkyl substances

Eryasa, B., Grandjean, P., Nielsen, F., Valvi, D., Zmirou-Navier, D., Sunderland, E., Weihe, P., & Oulhote, Y. (2019) · Environment International · doi:10.1016/j.envint.2019.05.068 · Cited by 98

Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory

Kruppa, J., Liu, Y., Biau, G., Kohler, M., König, I. R., Malley, J. D., & Ziegler, A. (2014) · Biometrical Journal · doi:10.1002/bimj.201300068 · Cited by 97

ICT as a general-purpose technology: The productivity of ICT in the United States revisited

Liao, H., Wang, B., Li, B., & Weyman-Jones, T. (2016) · Information Economics and Policy · doi:10.1016/j.infoecopol.2016.05.001 · Cited by 95

Policing the homeless: An evaluation of efforts to reduce homeless‐related crime

Berk, R., & MacDonald, J. (2010) · Criminology & Public Policy · doi:10.1111/j.1745-9133.2010.00673.x · Cited by 93

Assessing local model adequacy in Bayesian hierarchical models using the partitioned deviance information criterion

Wheeler, D. C., Hickson, D. A., & Waller, L. A. (2010) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2010.01.025 · Cited by 90

Comparing California Current cetacean–habitat models developed using in situ and remotely sensed sea surface temperature data

Becker, E., Forney, K., Ferguson, M., Foley, D., Smith, R., Barlow, J., & Redfern, J. (2010) · Marine Ecology Progress Series · doi:10.3354/meps08696 · Cited by 90

The natural history of symptomatic androgen deficiency in men: onset, progression, and spontaneous remission

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