General Methods for Monitoring Convergence of Iterative Simulations

An Introduction to the Bootstrap

Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500

An Introduction to the Bootstrap

Efron, B., & Tibshirani, R. J. (1993) · doi:10.1007/978-1-4899-4541-9 · Cited by 21,230

Inference from iterative simulation using multiple sequences

Gelman, A., & Rubin, D. B. (1992) · Statistical Science · doi:10.1214/ss/1177011136 · Cited by 16,627

Markov Chain Monte Carlo in Practice

Gilks, W. R., Richardson, S. T., & Spiegelhalter, D. J. (1995) · Chapman & Hall/CRC eBooks · doi:10.1201/b14835 · Cited by 7,927

Sampling-based approaches to calculating marginal densities

Gelfand, A. E., & Smith, A. F. M. (1990) · Journal of the American Statistical Association · doi:10.1080/01621459.1990.10476213 · Cited by 6,606

Design of Experiments

Fisher, R. A. (1936) · BMJ · doi:10.1136/bmj.1.3923.554-a · Cited by 4,980

The Calculation of Posterior Distributions by Data Augmentation

Tanner, M. A., & Wong, W. H. (1987) · Journal of the American Statistical Association · doi:10.1080/01621459.1987.10478458 · Cited by 3,929

Markov Chains for Exploring Posterior Distributions

Tierney, L. (1994) · The Annals of Statistics · doi:10.1214/aos/1176325750 · Cited by 3,474

Journal of the Royal Statistical Society.

(1923) · The Economic Journal · doi:10.1093/ej/33.130.269 · Cited by 3,289

Bayes and Empirical Bayes Methods for Data Analysis

Carlin, B. P., & Louis, T. A. (2001) · Technometrics · doi:10.1198/tech.2001.s608 · Cited by 2,110

Jointly Modeling Longitudinal and Event Time Data With Application to Acquired Immunodeficiency Syndrome

Wang, Y. Q., & Taylor, J. M. G. (2001) · Journal of the American Statistical Association · doi:10.1198/016214501753208591 · Cited by 266

Impact of different training modalities on glycaemic control and blood lipids in patients with type 2 diabetes: a systematic review and network meta-analysis

Schwingshackl, L., Missbach, B., Dias, S., König, J., & Hoffmann, G. (2014) · Diabetologia · doi:10.1007/s00125-014-3303-z · Cited by 264

The Magnetar Model for Type I Superluminous Supernovae. I. Bayesian Analysis of the Full Multicolor Light-curve Sample with MOSFiT

Nicholl, M., Guillochon, J., & Berger, E. (2017) · The Astrophysical Journal · doi:10.3847/1538-4357/aa9334 · Cited by 264

Decomposable graphical Gaussian model determination

Giudici, P. (1999) · Biometrika · doi:10.1093/biomet/86.4.785 · Cited by 263

Tenofovir and Entecavir Are the Most Effective Antiviral Agents for Chronic Hepatitis B: A Systematic Review and Bayesian Meta-analyses

Woo, G. W., TOMLINSON, G. A., Nishikawa, Y., Kowgier, M. E., Sherman, M., Wong, D., Pham, B., Ungar, W. J., Einarson, T. R., Heathcote, E. J., et al. (2010) · Gastroenterology · doi:10.1053/j.gastro.2010.06.042 · Cited by 259

On the nature of over-dispersion in motor vehicle crash prediction models

Mitra, S., & Washington, S. P. (2006) · Accident Analysis & Prevention · doi:10.1016/j.aap.2006.08.002 · Cited by 253

A Computational Framework for Infinite-Dimensional Bayesian Inverse Problems, Part II: Stochastic Newton MCMC with Application to Ice Sheet Flow Inverse Problems

Petra, N., Martin, J. L., Stadler, G., & Ghattas, O. N. (2014) · SIAM Journal on Scientific Computing · doi:10.1137/130934805 · Cited by 252

Use of Integrated Modeling to Enhance Estimates of Population Dynamics Obtained from Limited Data

Schaub, M., Giménez, O., Sierro, A., & Arlettaz, R. (2007) · Conservation Biology · doi:10.1111/j.1523-1739.2007.00743.x · Cited by 250

Spatially integrated assessment reveals widespread changes in penguin populations on the Antarctic Peninsula

Lynch, H. J., Naveen, R., Trathan, P. N., & Fagan, W. F. (2012) · Ecology · doi:10.1890/11-1588.1 · Cited by 249

Weighing Black Holes Using Tidal Disruption Events

Mockler, B., Guillochon, J., & Ramirez-Ruiz, E. (2019) · The Astrophysical Journal · doi:10.3847/1538-4357/ab010f · Cited by 246

Spatial and interspecific variability in phenological responses to warming temperatures

Primack, R. B., Ibáñez, I., Higuchi, H., Lee, S.-D., Miller‐Rushing, A. J., Wilson, A. M., & Silander, J. A. (2009) · Biological Conservation · doi:10.1016/j.biocon.2009.06.003 · Cited by 238

Soil Texture Drives Responses of Soil Respiration to Precipitation Pulses in the Sonoran Desert: Implications for Climate Change

Cable, J. M., Ogle, K., Williams, D. G., Weltzin, J. F., & Huxman, T. E. (2008) · Ecosystems · doi:10.1007/s10021-008-9172-x · Cited by 233

Overcoming Equifinality: Leveraging Long Time Series for Stream Metabolism Estimation

Appling, A., Hall, R. O., Yackulic, C. B., & Arroita, M. (2018) · Journal of Geophysical Research Biogeosciences · doi:10.1002/2017jg004140 · Cited by 261

Collision prediction models using multivariate Poisson-lognormal regression

El‐Basyouny, K., & Sayed, T. (2009) · Accident Analysis & Prevention · doi:10.1016/j.aap.2009.04.005 · Cited by 232

Concordance and discordance in cosmology

Raveri, M., & Hu, W. (2019) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.99.043506 · Cited by 230

Accident prediction models with random corridor parameters

El‐Basyouny, K., & Sayed, T. (2009) · Accident Analysis & Prevention · doi:10.1016/j.aap.2009.06.025 · Cited by 231

Combining full-shape and BAO analyses of galaxy power spectra: a 1.6% CMB-independent constraint onH0

Philcox, O. H. E., Ivanov, M. M., Simonović, M., & Zaldarriaga, M. (2020) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2020/05/032 · Cited by 220

Adjuvant treatments for resected pancreatic adenocarcinoma: a systematic review and network meta-analysis

Liao, W., Chien, K., Lin, Y., Wu, M., Lin, J., Wang, H., & Tu, Y. (2013) · The Lancet Oncology · doi:10.1016/s1470-2045(13)70388-7 · Cited by 229

MOSFiT: Modular Open Source Fitter for Transients

Guillochon, J., Nicholl, M., Villar, V. A., Mockler, B., Narayan, G., Mandel, K. S., Berger, E., & Williams, P. K. G. (2018) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/aab761 · Cited by 229

A systematic review and network meta‐analysis comparing the use of Foley catheters, misoprostol, and dinoprostone for cervical ripening in the induction of labour

Chen, W., Xue, J., Peprah, M. K., Wen, S., WALKER, M. C., Gao, Y., & Tang, Y. (2015) · BJOG An International Journal of Obstetrics & Gynaecology · doi:10.1111/1471-0528.13456 · Cited by 218

An assessment of integrated population models: bias, accuracy, and violation of the assumption of independence

Abadi, F., Giménez, O., Arlettaz, R., & Schaub, M. (2010) · Ecology · doi:10.1890/08-2235.1 · Cited by 215

Prenatal Treatment for Serious Neurological Sequelae of Congenital Toxoplasmosis: An Observational Prospective Cohort Study

Cortina‐Borja, M., Tan, H. K., Wallon, M., Paul, M., Prusa, A. R., Buffolano, W., Malm, G., Salt, A., Freeman, K. D., Petersen, E., et al. (2010) · PLoS Medicine · doi:10.1371/journal.pmed.1000351 · Cited by 213

Exploring a Bayesian hierarchical approach for developing safety performance functions for a mountainous freeway

Ahmed, M. M., Huang, H., Abdel-Aty, M. A., & Guevara, B. (2011) · Accident Analysis & Prevention · doi:10.1016/j.aap.2011.03.021 · Cited by 213

Markov chain Monte Carlo: Can we trust the third significant figure

Flegal, J. M., Haran, M., & Jones, G. L. (2007) · Cited by 211

A Microsoft-Excel-based tool for running and critically appraising network meta-analyses—an overview and application of NetMetaXL

Brown, S. C., Hutton, B., Clifford, T., Coyle, D., Grima, D. T., Wells, G. A., & Cameron, C. G. (2014) · Systematic Reviews · doi:10.1186/2046-4053-3-110 · Cited by 209

A survey of Monte Carlo methods for parameter estimation

Luengo, D., Martino, L., Bugallo, M. F., Elvira, V., & Särkkä, S. (2020) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-020-00675-6 · Cited by 238

Exploring ecological patterns with structural equation modeling and Bayesian analysis

Arhonditsis, G. B., Stow, C. A., Steinberg, L. J., Kenney, M. A., Lathrop, R. C., McBride, S. J., & Reckhow, K. H. (2005) · Ecological Modelling · doi:10.1016/j.ecolmodel.2005.07.028 · Cited by 207

Sticky Information in General Equilibrium

Mankiw, N. G., & Reis, R. A. M. R. (2007) · Journal of the European Economic Association · doi:10.1162/jeea.2007.5.2-3.603 · Cited by 205

Evolutionary drivers of thermoadaptation in enzyme catalysis

Nguyen, V., Wilson, C., Hoemberger, M., Stiller, J. B., Agafonov, R. V., Kutter, S., English, J., Theobald, D. L., & Kern, D. (2016) · Science · doi:10.1126/science.aah3717 · Cited by 201

Respondent‐driven sampling as Markov chain Monte Carlo

Goel, S., & Salganik, M. (2009) · Statistics in Medicine · doi:10.1002/sim.3613 · Cited by 203

Plant DNA metabarcoding of lake sediments: How does it represent the contemporary vegetation

Alsos, I. G., Lammers, Y., Yoccoz, N. G., Jørgensen, T., Sjögren, P., Gielly, L., & Edwards, M. E. (2018) · PLoS ONE · doi:10.1371/journal.pone.0195403 · Cited by 199

Credit and Banking in a DSGE Model of the Euro AreaPreprint

Gerali, A., Neri, S., Sessa, L., & Signoretti, F. M. (2010) · SSRN Electronic Journal · doi:10.2139/ssrn.1601937 · Cited by 199

Bayesian Graphical Models for STructural Vector Autoregressive Processes

Ahelegbey, D. F., Billio, M., & Casarin, R. (2015) · Journal of Applied Econometrics · doi:10.1002/jae.2443 · Cited by 198

Atypical Antipsychotic Augmentation for Treatment-Resistant Depression: A Systematic Review and Network Meta-Analysis

Zhou, X., Keitner, G. I., Qin, B., Ravindran, A. V., Bauer, M., Giovane, C. D., Zhao, J., Liu, Y., Fang, Y., Zhang, Y., et al. (2015) · The International Journal of Neuropsychopharmacology · doi:10.1093/ijnp/pyv060 · Cited by 199

Finite mixture modelling using the skew normal distribution

Lin, T., Lee, J. C., & Yen, S. Y. (2007) · Cited by 195

spOccupancy: An R package for single‐species, multi‐species, and integrated spatial occupancy models

Doser, J. W., Finley, A. O., Kéry, M., & Zipkin, E. F. (2022) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13897 · Cited by 195

Markov switching negative binomial models: An application to vehicle accident frequencies

Malyshkina, N. V., Mannering, F., & Tarko, A. P. (2008) · Accident Analysis & Prevention · doi:10.1016/j.aap.2008.11.001 · Cited by 194

Methotrexate monotherapy and methotrexate combination therapy with traditional and biologic disease modifying antirheumatic drugs for rheumatoid arthritis: abridged Cochrane systematic review and network meta-analysis

Hazlewood, G. S., Barnabe, C. C. M., TOMLINSON, G. A., Marshall, D. A., Devoe, D. J. A., & Bombardier, C. (2016) · BMJ · doi:10.1136/bmj.i1777 · Cited by 193

Bayesian Clustering and Product Partition Models

Quintana, F. A., & Iglesias, P. L. (2003) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/1467-9868.00402 · Cited by 185

Population dynamics of salmon lice Lepeophtheirus salmonis on Atlantic salmon and sea trout

Stien, A., Bjørn, P., Heuch, P. A., & Elston, D. (2005) · Marine Ecology Progress Series · doi:10.3354/meps290263 · Cited by 192

Temperature and Cardiovascular Deaths in the US Elderly

Barnett, A. G. (2007) · Epidemiology · doi:10.1097/01.ede.0000257515.34445.a0 · Cited by 189

Another 'futile quest'? A simulation study of Yang and Land's Hierarchical Age-Period-Cohort model

Bell, A., & Jones, K. (2014) · Demographic Research · doi:10.4054/demres.2014.30.11 · Cited by 189

Bayesian compressive sensing for cluster structured sparse signals

Yu, L., Sun, H., Barbot, J. P., & Zheng, G. (2011) · Signal Processing · doi:10.1016/j.sigpro.2011.07.015 · Cited by 190

Management of Frailty: A Systematic Review and Network Meta-analysis of Randomized Controlled Trials

Negm, A. M., Kennedy, C. C., Thabane, L., Veroniki, A. A., Adachi, J. D., Richardson, J. A., Cameron, I. D., Giangregorio, A., Πετροπούλου, Μ., Alsaad, S. M., et al. (2019) · Journal of the American Medical Directors Association · doi:10.1016/j.jamda.2019.08.009 · Cited by 194

The relative importance of landscape properties for woodland birds in agricultural environments

Radford, J. Q., & Bennett, A. F. (2007) · Journal of Applied Ecology · doi:10.1111/j.1365-2664.2007.01327.x · Cited by 194

Drug Treatment of Idiopathic Pulmonary Fibrosis

Canestaro, W. J., Forrester, S. H., Raghu, G., Ho, L. A., & Devine, B. E. (2016) · CHEST Journal · doi:10.1016/j.chest.2015.11.013 · Cited by 188

Climatology Models for Extreme Hurricane Winds near the United States

Jagger, T. H., & Elsner, J. B. (2006) · Journal of Climate · doi:10.1175/jcli3913.1 · Cited by 186

Nonpharmacologic Interventions for Reducing Blood Pressure in Adults With Prehypertension to Established Hypertension

Fu, J., Liu, Y., Zhang, L., Zhou, L., Li, D., Quan, H., Zhu, L., Hu, F., Li, X., Meng, S., et al. (2020) · Journal of the American Heart Association · doi:10.1161/jaha.120.016804 · Cited by 213

Health‐related quality of life in systemic sclerosis: A systematic review

Hudson, M., Thombs, B. D., Steele, R. J., Panopalis, P., Newton, E. G., & Baron, M. G. (2009) · Arthritis Care & Research · doi:10.1002/art.24676 · Cited by 183

Scientific Opinion on the state of the art of Toxicokinetic/Toxicodynamic (TKTD) effect models for regulatory risk assessment of pesticides for aquatic organisms

(PPR), E. P. O. P. P. P. A. T. R., Ockleford, C., Adriaanse, P., Berny, P. J., Brock, T. C. M., Duquesne, S., Grilli, S., Hernández, A. F., Bennekou, S. H., Klein, M., et al. (2018) · EFSA Journal · doi:10.2903/j.efsa.2018.5377 · Cited by 183

Estimating true instead of apparent survival using spatial C ormack– J olly– S eber models

Schaub, M., & Royle, J. A. (2013) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12134 · Cited by 182

A Bayesian approach to combining animal abundance and demographic data

Brooks, S. P., King, R., CREEM, U. O. S. A., Morgan, B. J. T., & Inst. of Matemathics, S. A. A. S. (2004) · Animal Biodiversity and Conservation · doi:10.32800/abc.2004.27.0515 · Cited by 182

Improvement of cosmological neutrino mass bounds

Giusarma, E., Gerbino, M., Mena, O., Vagnozzi, S., Ho, S., & Katherine Freese, B. (2016) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.94.083522 · Cited by 181

Impact of Different Training Modalities on Anthropometric and Metabolic Characteristics in Overweight/Obese Subjects: A Systematic Review and Network Meta-Analysis

Schwingshackl, L., Dias, S., Strasser, B., & Hoffmann, G. (2013) · PLoS ONE · doi:10.1371/journal.pone.0082853 · Cited by 180

Bayesian Structural Equation Modeling

Palomo, J., Dunson, D. B., & Bollen, K. (2007) · Elsevier eBooks · doi:10.1016/b978-044452044-9/50011-2 · Cited by 180

On Monte Carlo methods for Bayesian inference

Qian, S. S., Stow, C. A., & Borsuk, M. E. (2002) · Ecological Modelling · doi:10.1016/s0304-3800(02)00299-5 · Cited by 180

Born to lead? A twin design and genetic association study of leadership role occupancy

Jan-emmanuel De Neve, A., Mikhaylov, S., Dawes, C. T., Christakis, N. A., & Fowler, J. H. (2012) · The Leadership Quarterly · doi:10.1016/j.leaqua.2012.08.001 · Cited by 184

Data Assimilation: A Mathematical IntroductionPreprint

Law, K. J. H., Stuart, A. M., & Zygalakis, K. C. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.07825 · Cited by 178

Modeling life-span growth curves of cognition using longitudinal data with multiple samples and changing scales of measurement.

McArdle, J. J., Grimm, K. J., Hamagami, F., Bowles, R. P., & Meredith, W. H. (2009) · Psychological Methods · doi:10.1037/a0015857 · Cited by 177

Examining the utility of alternative video monitoring metrics for indexing reef fish abundance

Schobernd, Z. H., Bacheler, N. M., & Conn, P. B. (2013) · Canadian Journal of Fisheries and Aquatic Sciences · doi:10.1139/cjfas-2013-0086 · Cited by 177

Fuzzy Cognitive Diagnosis for Modelling Examinee Performance

Liu, Q., Wu, R., Chen, E., Xu, G., Su, Y., Chen, Z., & Hu, G. (2018) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3168361 · Cited by 182

Bias and Precision in QST Estimates: Problems and Some Solutions

O’Hara, R. B., & Merilä, J. (2005) · Genetics · doi:10.1534/genetics.105.044545 · Cited by 176

Dynamic models of choice

Heathcote, A., Lin, Y.-S., Reynolds, A., Strickland, L. J. G., Gretton, M. B., & Matzke, D. (2018) · Behavior Research Methods · doi:10.3758/s13428-018-1067-y · Cited by 186

Pharmacological interventions for the treatment of delirium in critically ill adults

Burry, L. D., Hutton, B., Williamson, D. R., Mehta, S., Adhikari, N. K. J., Cheng, W., Ely, E. W., Egerod, I., Fergusson, D. A., & Rose, L. (2019) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd011749.pub2 · Cited by 180

Using a B ayesian hierarchical model to improve L ake E rie cyanobacteria bloom forecasts

Obenour, D. R., Gronewold, A. D., Stow, C. A., & Scavia, D. (2014) · Water Resources Research · doi:10.1002/2014wr015616 · Cited by 175

Treatments for Shoulder Impingement Syndrome

Wei, D., Goost, H., Lin, X., Burger, C., Paul, C., Wang, Z., Zhang, T., Jiang, Z.-C., Welle, K., & Kabir, K. (2015) · Medicine · doi:10.1097/md.0000000000000510 · Cited by 175

Demographic History Has Influenced Nucleotide Diversity in EuropeanPinus sylvestrisPopulations

Pyhäjärvi, T., García‐Gil, M. R., Knürr, T., Mikkonen, M., Wachowiak, W. M., & Savolainen, O. (2007) · Genetics · doi:10.1534/genetics.107.077099 · Cited by 173

Cosmology from large-scale structure

Tröster, T., Sánchez, A. G., Asgari, M., Blake, C., Crocce, M., Heymans, C., Hildebrandt, H., Joachimi, B., Joudaki, S., Kannawadi, A., et al. (2019) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201936772 · Cited by 174

Hierarchical models of animal abundance and occurrence

Royle, J. A., & Dorazio, R. M. (2006) · Journal of Agricultural Biological and Environmental Statistics · doi:10.1198/108571106x129153 · Cited by 172

Efficacy and safety of first line treatments for patients with advanced epidermal growth factor receptor mutated, non-small cell lung cancer: systematic review and network meta-analysis

Zhao, Y., Liu, J., Cai, X., Pan, Z., Liu, J., Yin, W., Chen, H., Xie, Z., Liang, H., Wang, W., et al. (2019) · BMJ · doi:10.1136/bmj.l5460 · Cited by 170

Assessing the convergence of Markov Chain Monte Carlo methods: An example from evaluation of diagnostic tests in absence of a gold standard

Toft, N., Innocent, G. T., Gettinby, G., & Reid, S. W. J. (2007) · Preventive Veterinary Medicine · doi:10.1016/j.prevetmed.2007.01.003 · Cited by 171

Overfishing, disease, habitat loss, and potential extirpation of oysters in upper Chesapeake Bay

Wilberg, M. J., Livings, M., Barkman, J., Morris, B., & Robinson, J. M. (2011) · Marine Ecology Progress Series · doi:10.3354/meps09161 · Cited by 172

A discussion on: Sparse graphs using exchangeable random measures

CASARIN, R., Iacopini, M., & ROSSINI, L. (2017) · Bond University Research Portal (Bond University) · doi:10.1111/rssb.12233 · Cited by 180

The Effect of Glucagon-Like Peptide 1 Receptor Agonists on Weight Loss in Type 2 Diabetes: A Systematic Review and Mixed Treatment Comparison Meta-Analysis

Potts, J. E., Gray, L. J., Brady, E. M., Khunti, K., Davies, M. J., & Bodicoat, D. H. (2015) · PLOS ONE · doi:10.1371/journal.pone.0126769 · Cited by 163

Joint estimation over multiple individuals improves behavioural state inference from animal movement data

Jonsen, I. D. (2016) · Scientific Reports · doi:10.1038/srep20625 · Cited by 169

Adapting fit indices for Bayesian structural equation modeling: Comparison to maximum likelihood.

Garnier‐Villarreal, M., & Jorgensen, T. D. (2019) · Psychological Methods · doi:10.1037/met0000224 · Cited by 177

First evidence that marine protected areas can work for marine mammals

Gormley, A. M., Slooten, E., Dawson, S., Barker, R., Rayment, W., Fresne, S. D., & Bräger, S. (2012) · Journal of Applied Ecology · doi:10.1111/j.1365-2664.2012.02121.x · Cited by 167

Effect of Statins on Kidney Disease Outcomes: A Systematic Review and Meta-analysis

Su, X., Zhang, L., Lv, J., Wang, J., Hou, W., Xie, X., & Zhang, H. (2016) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2016.01.016 · Cited by 164

Updated results on neutrino mass and mass hierarchy from cosmology with Planck 2018 likelihoods

Choudhury, S. R., & Hannestad, S. (2020) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2020/07/037 · Cited by 165

Denosumab, raloxifene, romosozumab and teriparatide to prevent osteoporotic fragility fractures: a systematic review and economic evaluation

Davis, S., Simpson, E. L., Hamilton, J., James, M. M., Rawdin, A., Wong, R., Goka, E., Gittoes, N. J. L., & Selby, P. L. (2020) · Health Technology Assessment · doi:10.3310/hta24290 · Cited by 170

A Bayesian Approach to Multilevel Structural Equation Modeling With Continuous and Dichotomous Outcomes

Depaoli, S. A., & Clifton, J. P. (2015) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2014.937849 · Cited by 183

Leveraging Missing Ratings to Improve Online Recommendation Systems

Ying, Y., Feinberg, F. M., & Wedel, M. (2006) · Journal of Marketing Research · doi:10.1509/jmkr.43.3.355 · Cited by 162

Inferring Population Decline and Expansion From Microsatellite Data: A Simulation-Based Evaluation of the Msvar Method

Girod, C., Vitalis, R., Leblois, R., & Fréville, H. (2011) · Genetics · doi:10.1534/genetics.110.121764 · Cited by 162

Dynamic reassortments and genetic heterogeneity of the human-infecting influenza A (H7N9) virus

Cui, L., Liu, D., Shi, W., Pan, J., Qi, X., Li, X., Guo, X., Zhou, M., Li, W., Li, J., et al. (2014) · Nature Communications · doi:10.1038/ncomms4142 · Cited by 161

Eutrophication risk assessment using Bayesian calibration of process-based models: Application to a mesotrophic lake

Arhonditsis, G. B., Qian, S. S., Stow, C. A., Lamon, E. C., & Reckhow, K. H. (2007) · Ecological Modelling · doi:10.1016/j.ecolmodel.2007.05.020 · Cited by 161

Estimation of immigration rate using integrated population models

Abadi, F., Giménez, O., Ullrich, B., Arlettaz, R., & Schaub, M. (2010) · Journal of Applied Ecology · doi:10.1111/j.1365-2664.2010.01789.x · Cited by 160

Landslide susceptibility mapping along road corridors in the Indian Himalayas using Bayesian logistic regression models

Das, I., Stein, A., Kerle, N., & Dadhwal, V. K. (2012) · Geomorphology · doi:10.1016/j.geomorph.2012.08.004 · Cited by 160

Tritrophic phenological match–mismatch in space and time

Burgess, M. D., Smith, K. W., Evans, K. L., Leech, D. I., Pearce‐Higgins, J. W., Branston, C. J., Briggs, K., Clark, J. R., du Feu, C. R., Lewthwaite, K., et al. (2018) · Nature Ecology & Evolution · doi:10.1038/s41559-018-0543-1 · Cited by 172

Bayesian Animal Survival Estimation

Brooks, S. P., Catchpole, E. A., & Morgan, B. J. T. (2000) · Statistical Science · doi:10.1214/ss/1009213003 · Cited by 160

Modeling the effects of environmental disturbance on wildlife communities: avian responses to prescribed fire

Russell, R. E., Royle, J. A., Saab, V. A., Lehmkuhl, J. F., Block, W. M., & Sauer, J. R. (2009) · Ecological Applications · doi:10.1890/08-0910.1 · Cited by 159

Bayesian analysis for social data: A step-by-step protocol and interpretation

Vuong, Q., La, V., Nguyen, M., Ho, M., Tran, T., & Ho, T. M. (2020) · MethodsX · doi:10.1016/j.mex.2020.100924 · Cited by 159

Changes in glacier equilibrium-line altitude in the western Alps from 1984 to 2010: evaluation by remote sensing and modeling of the morpho-topographic and climate controls

Rabatel, A., Letréguilly, A., Dedieu, J.-P., & Eckert, N. (2013) · The Cryosphere · doi:10.5194/tc-7-1455-2013 · Cited by 158

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Liu, A., & Wu, D. C. (2019) · Annals of Tourism Research · doi:10.1016/j.annals.2019.04.005 · Cited by 158

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