Statistical Inference

A scaling normalization method for differential expression analysis of RNA-seq data

Robinson, M. D., & Oshlack, A. (2010) · Genome Biology · doi:10.1186/gb-2010-11-3-r25 · Cited by 7,876

Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples

Austin, P. C. (2009) · Statistics in Medicine · doi:10.1002/sim.3697 · Cited by 6,758

What's wrong with Bonferroni adjustments

Perneger, T. V. (1998) · BMJ · doi:10.1136/bmj.316.7139.1236 · Cited by 5,900

Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies

Austin, P. C., & Stuart, E. A. (2015) · Statistics in Medicine · doi:10.1002/sim.6607 · Cited by 4,495

A kernel two-sample test

(2012) · MPG.PuRe (Max Planck Society) · doi:10.5555/2188385.2188410 · Cited by 3,603

An effect size primer: A guide for clinicians and researchers.

Ferguson, C. J. (2009) · Professional Psychology: Research and Practice · doi:10.1037/a0015808 · Cited by 3,467

The polyvagal perspective.

Porges, S. W. (2007) · Biological Psychology · doi:10.1016/j.biopsycho.2006.06.009 · Cited by 3,415

Bias in cross-sectional analyses of longitudinal mediation

Maxwell, S. E., & Cole, D. A. (2007) · Psychological Methods · doi:10.1037/1082-989x.12.1.23 · Cited by 3,401

A Nonparametric “Trim and Fill” Method of Accounting for Publication Bias in Meta-Analysis

Duval, S., & Tweedie, R. (2000) · Journal of the American Statistical Association · doi:10.1080/01621459.2000.10473905 · Cited by 2,955

A likelihood approach to estimating phylogeny from discrete morphological character data

Lewis, P. O. (2001) · Systematic Biology · doi:10.1080/106351501753462876 · Cited by 2,929

Taking Preferences Seriously: A Liberal Theory of International Politics

Moravcsik, A. (1997) · International Organization · doi:10.1162/002081897550447 · Cited by 2,814

Detection of nonneutral substitution rates on mammalian phylogenies

Pollard, K. S., Hubisz, M. J., Rosenbloom, K. R., & Siepel, A. (2010) · Genome Research · doi:10.1101/gr.097857.109 · Cited by 2,353

Comparing correlated correlation coefficients

Meng, X.-L., Rosenthal, R., & Rubin, D. B. (1992) · Psychological Bulletin · doi:10.1037/0033-2909.111.1.172 · Cited by 2,472

fitdistrplus: An R Package for Fitting Distributions

Delignette-Muller, M. L., & Dutang, C. (2015) · Journal of Statistical Software · doi:10.18637/jss.v064.i04 · Cited by 2,383

Hierarchical structure and the prediction of missing links in networks

Clauset, A., Moore, C., & Newman, M. E. J. (2008) · Nature · doi:10.1038/nature06830 · Cited by 2,171

A Kernel Method for the Two-Sample-Problem

Gretton, A., Borgwardt, K. M., Rasch, M., Schölkopf, B., & Smola, A. J. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0069 · Cited by 2,167

Using Bayesian model averaging to calibrate forecast ensembles.

Raftery, A. E., Gneiting, T., Balabdaoui, F., & Polakowski, M. (2005) · Monthly Weather Review · doi:10.1175/mwr2906.1 · Cited by 1,915

Tutorial on maximum likelihood estimation

Myung, I. J. (2003) · Journal of Mathematical Psychology · doi:10.1016/s0022-2496(02)00028-7 · Cited by 1,802

Neural correlations, population coding and computation

Averbeck, B. B., Latham, P. E., & Pouget, A. (2006) · Nature reviews. Neuroscience · doi:10.1038/nrn1888 · Cited by 1,743

Integrating structured biological data by Kernel Maximum Mean Discrepancy

Borgwardt, K. M., Gretton, A., Rasch, M. J., Kriegel, H.-P., Schölkopf, B., & Smola, A. J. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl242 · Cited by 1,674

Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation

Wong, T.-T. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.03.009 · Cited by 1,562

Sifting the evidence---what's wrong with significance tests? Another comment on the role of statistical methods

Sterne, J. A. C. (2001) · BMJ · doi:10.1136/bmj.322.7280.226 · Cited by 1,466

Standardizing catch and effort data: a review of recent approaches

Maunder, M. N., & Punt, A. E. (2004) · Fisheries Research · doi:10.1016/j.fishres.2004.08.002 · Cited by 1,336

A Kenward-Roger Approximation and Parametric Bootstrap Methods for Tests in Linear Mixed Models - The R Package pbkrtest

Halekoh, U., & Højsgaard, S. (2014) · Journal of Statistical Software · doi:10.18637/jss.v059.i09 · Cited by 1,324

A meta-analysis of worldwide studies demonstrates an association between schizophrenia and tobacco smoking behaviors

de Leon, J., & Diaz, F. J. (2005) · Schizophrenia Research · doi:10.1016/j.schres.2005.02.010 · Cited by 1,276

Estimating average annual per cent change in trend analysis

Clegg, L. X., Hankey, B. F., Tiwari, R., Feuer, E. J., & Edwards, B. K. (2009) · Statistics in Medicine · doi:10.1002/sim.3733 · Cited by 1,256

Risk and Rationality in Adolescent Decision Making: Implications for Theory, Practice, and Public Policy

Reyna, V. F., & Farley, F. (2006) · Psychological Science in the Public Interest · doi:10.1111/j.1529-1006.2006.00026.x · Cited by 1,244

A new look at the big five factor structure through exploratory structural equation modeling

Marsh, H. W., Lüdtke, O., Muthén, B., Asparouhov, T., Morin, A. J. S., Trautwein, U., & Nagengast, B. (2010) · Psychological Assessment · doi:10.1037/a0019227 · Cited by 899

Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose

Morstatter, F., Pfeffer, J., Liu, H., & Carley, K. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v7i1.14401 · Cited by 863

Comparison of logistic regression versus propensity score when the number of events is low and there are multiple confounders

Cepeda, M. S. (2003) · American Journal of Epidemiology · doi:10.1093/aje/kwg115 · Cited by 859

Propensity score techniques and the assessment of measured covariate balance to test causal associations in psychological research.

Harder, V. S., Stuart, E. A., & Anthony, J. C. (2010) · Psychological Methods · doi:10.1037/a0019623 · Cited by 802

Ubiquitous Log Odds: A Common Representation of Probability and Frequency Distortion in Perception, Action, and Cognition

Zhang, H., & Maloney, L. T. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00001 · Cited by 679

Principles of Statistical Inference

Cox, D. R. (2006) · doi:10.1017/cbo9780511813559 · Cited by 670

Violating the normality assumption may be the lesser of two evils

Knief, U., & Forstmeier, W. (2021) · Behavior Research Methods · doi:10.3758/s13428-021-01587-5 · Cited by 654

The fallacy of placing confidence in confidence intervals

Morey, R. D., Hoekstra, R., Rouder, J. N., Lee, M. D., & Wagenmakers, E.-J. (2016) · Psychonomic Bulletin & Review · doi:10.3758/s13423-015-0947-8 · Cited by 577

Belief and recollection of autobiographical memories

Rubin, D. C., Schrauf, R. W., & Greenberg, D. L. (2003) · Memory & Cognition · doi:10.3758/bf03196443 · Cited by 612

A maximum pseudo-likelihood approach for estimating species trees under the coalescent model

Liu, L., Yu, L., & Edwards, S. V. (2010) · BMC Evolutionary Biology · doi:10.1186/1471-2148-10-302 · Cited by 607

Sources of Individual Differences in Reading Comprehension and Reading Fluency.

Jenkins, J. R., Fuchs, L. S., van den Broek, P., Espin, C., & Deno, S. L. (2003) · Journal of Educational Psychology · doi:10.1037/0022-0663.95.4.719 · Cited by 580

Permutation Tests for Studying Classifier Performance

Ojala, M., & Garriga, G. C. (2009) · 2009 Ninth IEEE International Conference on Data Mining · doi:10.1109/icdm.2009.108 · Cited by 555

Dealing with uncertainty in model updating for damage assessment: a review

Simoen, E., De Roeck, G., & Lombaert, G. (2015) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2014.11.001 · Cited by 540

Review of modeling methods for HVAC systems

Afram, A., & Janabi-Sharifi, F. (2014) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2014.03.055 · Cited by 479

Elements of Multivariate Time Series Analysis.

DSS, & Reinsel, G. C. (1997) · Journal of the American Statistical Association · doi:10.2307/2965460 · Cited by 474

Post Hoc Power Analysis: An Idea Whose Time Has Passed?

Levine, M., & Ensom, M. H. H. (2001) · Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy · doi:10.1592/phco.21.5.405.34503 · Cited by 473

Dynamics of subjective contour formation in the early visual cortex

Lee, T. S., & Nguyen, M. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.4.1907 · Cited by 428

Effect Size, Statistical Power, and Sample Size Requirements for the Bootstrap Likelihood Ratio Test in Latent Class Analysis

Dziak, J. J., Lanza, S. T., & Tan, X. (2014) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1080/10705511.2014.919819 · Cited by 441

When to adjust alpha during multiple testing: a consideration of disjunction, conjunction, and individual testing

Rubin, M. (2021) · Synthese · doi:10.1007/s11229-021-03276-4 · Cited by 371

SOBER: statistical model-based bug localization

Liu, C., Yan, X., Fei, L., Han, J., & Midkiff, S. P. (2005) · Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering · doi:10.1145/1081706.1081753 · Cited by 357

Estimating particulate matter-mortality dose-response curves and threshold levels: an analysis of daily time-series for the 20 largest US cities

Daniels, M. J. (2000) · American Journal of Epidemiology · doi:10.1093/aje/152.5.397 · Cited by 348

AERA Editorial Policies regarding Statistical Significance Testing: Three Suggested Reforms

Thompson, B. (1996) · Educational Researcher · doi:10.2307/1176337 · Cited by 339

The effects of estrogen on osteoprotegerin, RANKL, and estrogen receptor expression in human osteoblasts.

Bord, S., Ireland, D. C., Beavan, S. R., & Compston, J. E. (2003) · Bone · doi:10.1016/s8756-3282(02)00953-5 · Cited by 333

Depressed Dopamine Activity in Caudate and Preliminary Evidence of Limbic Involvement in Adults With Attention-Deficit/Hyperactivity Disorder

Volkow, N. D., Wang, G.-J., Newcorn, J., Telang, F., Solanto, M. V., Fowler, J. S., Logan, J., Ma, Y., Schulz, K., Pradhan, K., et al. (2007) · Archives of General Psychiatry · doi:10.1001/archpsyc.64.8.932 · Cited by 331

Effects of Aerobic Exercise Versus Stress Management Treatment in Fibromyalgia

Wigers, S. H., Stiles, T. C., & Vogel, P. A. (1996) · Scandinavian Journal of Rheumatology · doi:10.3109/03009749609069212 · Cited by 328

Assessment of the Anthelmintic Efficacy of Albendazole in School Children in Seven Countries Where Soil-Transmitted Helminths Are Endemic

Vercruysse, J., Behnke, J. M., Albonico, M., Ame, S. M., Angebault, C., Bethony, J. M., Engels, D., Guillard, B., Hoa, N. T. V., Kang, G., et al. (2011) · PLoS Neglected Tropical Diseases · doi:10.1371/journal.pntd.0000948 · Cited by 301

Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing

Kohavi, R., Tang, D., & Xu, Y. (2020) · doi:10.1017/9781108653985 · Cited by 297

A tutorial introduction to the minimum description length principle

Grunwald, P. (2004) · arXiv · doi:10.48550/arxiv.math/0406077 · Cited by 293

RNA sequencing reveals two major classes of gene expression levels in metazoan cells

Hebenstreit, D., Fang, M., Gu, M., Charoensawan, V., van Oudenaarden, A., & Teichmann, S. A. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.28 · Cited by 289

A re‐evaluation of fixed effect(s) meta‐analysis

Rice, K., Higgins, J. P. T., & Lumley, T. (2018) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/rssa.12275 · Cited by 287

Targeted and Persistent 8-Oxoguanine Base Damage at Telomeres Promotes Telomere Loss and Crisis

Fouquerel, E., Barnes, R. P., Uttam, S., Watkins, S. C., Bruchez, M. P., & Opresko, P. L. (2019) · Molecular Cell · doi:10.1016/j.molcel.2019.04.024 · Cited by 275

Teaching about Approximate Confidence Regions Based on Maximum Likelihood Estimation

Meeker, W. Q., & Escobar, L. A. (1995) · The American Statistician · doi:10.1080/00031305.1995.10476112 · Cited by 279

A stochastic model of mortality, fertility, and human capital investment

Kalemli-Ozcan, S. (2003) · Journal of Development Economics · doi:10.1016/s0304-3878(02)00089-5 · Cited by 278

A novel measure of effect size for mediation analysis

Lachowicz, M. J., Preacher, K. J., & Kelley, K. (2018) · Psychological Methods · doi:10.1037/met0000165 · Cited by 271

Assessing the goodness of fit of logistic regression models in large samples: a modification of the Hosmer-Lemeshow test

Nattino, G., Pennell, M. L., & Lemeshow, S. (2020) · Biometrics · doi:10.1111/biom.13249 · Cited by 229

Monitoring Early Reading Development in First Grade: Word Identification Fluency versus Nonsense Word Fluency

Fuchs, L. S., Fuchs, D., & Compton, D. L. (2004) · Exceptional Children · doi:10.1177/001440290407100101 · Cited by 225

Identifiability of Parameters in MCMC Bayesian Inference of Phylogeny

Rannala, B. (2002) · Systematic Biology · doi:10.1080/10635150290102429 · Cited by 190

An estimate of the science-wise false discovery rate and application to the top medical literature

Jager, L. R., & Leek, J. T. (2014) · Biostatistics · doi:10.1093/biostatistics/kxt007 · Cited by 179

Human telomere biology: pitfalls of moving from the laboratory to epidemiology

Aviv, A., Valdes, A. M., & Spector, T. D. (2006) · International Journal of Epidemiology · doi:10.1093/ije/dyl169 · Cited by 172

Sampling methods for solving Bayesian model updating problems: A tutorial

Lye, A., Cicirello, A., & Patelli, E. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.107760 · Cited by 169

From Laplace to Supernova SN 1987A: Bayesian Inference in Astrophysics

Loredo, T. J. (1990) · Maximum Entropy and Bayesian Methods · doi:10.1007/978-94-009-0683-9_6 · Cited by 168

Mathematics Screening and Progress Monitoring at First Grade: Implications for Responsiveness to Intervention

Fuchs, L. S., Fuchs, D., Compton, D. L., Bryant, J. D., Hamlett, C. L., & Seethaler, P. M. (2007) · Exceptional Children · doi:10.1177/001440290707300303 · Cited by 163

Does lego training stimulate pupils’ ability to solve logical problems?

Lindh, J., & Holgersson, T. (2007) · Computers & Education · doi:10.1016/j.compedu.2005.12.008 · Cited by 159

Independent contrasts and PGLS regression estimators are equivalent

Blomberg, S. P., Lefevre, J. G., Wells, J. A., & Waterhouse, M. (2012) · Systematic Biology · doi:10.1093/sysbio/syr118 · Cited by 158

Publication Bias in Meta-Analysis: Confidence Intervals for Rosenthal’s Fail-Safe Number

Fragkos, K. C., Tsagris, M., & Frangos, C. C. (2014) · International Scholarly Research Notices · doi:10.1155/2014/825383 · Cited by 159

Trajectories of brain loss in aging and the development of cognitive impairment

Carlson, N. E., Moore, M. M., Dame, A., Howieson, D., Silbert, L. C., Quinn, J. F., & Kaye, J. A. (2008) · Neurology · doi:10.1212/01.wnl.0000280577.43413.d9 · Cited by 144

Multilevel factorial experiments for developing behavioral interventions: power, sample size, and resource considerations

Dziak, J. J., Nahum-Shani, I., & Collins, L. M. (2012) · Psychological Methods · doi:10.1037/a0026972 · Cited by 142

Analysing supply chain integration through a systematic literature review: a normative perspective

Mustafa Kamal, M., & Irani, Z. (2014) · Supply Chain Management: An International Journal · doi:10.1108/scm-12-2013-0491 · Cited by 139

Historical HbA1c values may explain the type 2 diabetes legacy effect: UKPDS 88

Lind, M., Imberg, H., Coleman, R. L., Nerman, O., & Holman, R. R. (2021) · Diabetes Care · doi:10.2337/dc20-2439 · Cited by 128

Limitations of GCTA as a solution to the missing heritability problem.

Krishna Kumar, S., Feldman, M. W., Rehkopf, D. H., & Tuljapurkar, S. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1520109113 · Cited by 128

User Verification Leveraging Gait Recognition for Smartphone Enabled Mobile Healthcare Systems

Ren, Y., Chen, Y., Chuah, M. C., & Yang, J. (2015) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2014.2365185 · Cited by 123

An Efficient Bandit Algorithm for Realtime Multivariate Optimization

Hill, D. N., Nassif, H., Liu, Y., Iyer, A., & Vishwanathan, S. V. N. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098184 · Cited by 113

Just a game after all: Violent video game exposure and time spent playing effects on hostile feelings, depression, and visuospatial cognition

Valadez, J. J., & Ferguson, C. J. (2012) · Computers in Human Behavior · doi:10.1016/j.chb.2011.11.006 · Cited by 111

Imaginary People Representing Real Numbers: Generating Personas from Online Social Media Data

An, J., Kwak, H., Jung, S., Salminen, J., Admad, M., & Jansen, B. (2018) · ACM Transactions on the Web · doi:10.1145/3265986 · Cited by 104

Dietary exposure to cadmium at close to the current provisional tolerable weekly intake does not affect renal function among female Japanese farmers

Horiguchi, H., Oguma, E., Sasaki, S., Miyamoto, K., Ikeda, Y., Machida, M., & Kayama, F. (2004) · Environmental Research · doi:10.1016/s0013-9351(03)00142-7 · Cited by 98

Conclusions via unique predictions obtained despite unidentifiability—new definitions and a general method

Cedersund, G. (2012) · The FEBS Journal · doi:10.1111/j.1742-4658.2012.08725.x · Cited by 81

Exact Power of Conditional and Unconditional Tests: Going Beyond the 2 × 2 Contingency Table

Mehta, C. R., & Hilton, J. F. (1993) · The American Statistician · doi:10.2307/2685184 · Cited by 87

Effectiveness of Mindfulness-Based Therapies in Reducing Symptoms of Depression: A Meta-Analysis

McCarney, R. W., Schulz, J., & Grey, A. R. (2012) · European Journal of Psychotherapy & Counselling · doi:10.1080/13642537.2012.713186 · Cited by 85

Key factors influencing canine heartworm, Dirofilaria immitis, in the United States

Brown, H. E., Harrington, L. C., Kaufman, P. E., McKay, T., Bowman, D. D., Nelson, C. T., Wang, D., & Lund, R. (2012) · Parasites & Vectors · doi:10.1186/1756-3305-5-245 · Cited by 73

Power analysis software for educational researchers

Peng, C.-Y. J., Long, H., & Abaci, S. (2012) · The Journal of Experimental Education · doi:10.1080/00220973.2011.647115 · Cited by 59

Synthesis of osteoprotegerin and RANKL by megakaryocytes is modulated by oestrogen

Bord, S., Frith, E., Ireland, D. C., Scott, M. A., Craig, J. I. O., & Compston, J. E. (2004) · British Journal of Haematology · doi:10.1111/j.1365-2141.2004.05024.x · Cited by 56

Recreational beach use values with multiple activities

Pascoe, S. (2019) · Ecological Economics · doi:10.1016/j.ecolecon.2019.02.018 · Cited by 55

Challenges of DHS and MIS to capture the entire pattern of malaria parasite risk and intervention effects in countries with different ecological zones: the case of Cameroon

Massoda Tonye, S. G., Kouambeng, C., Wounang, R., & Vounatsou, P. (2018) · Malaria Journal · doi:10.1186/s12936-018-2284-7 · Cited by 54

Choice of test for association in small sample unordered r × c tables

Lydersen, S., Pradhan, V., Senchaudhuri, P., & Laake, P. (2007) · Statistics in Medicine · doi:10.1002/sim.2839 · Cited by 48

Cook’s distance for generalized linear mixed models

Pinho, L. G. B., Nobre, J. S., & Singer, J. M. (2015) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2014.08.008 · Cited by 34

Parameter estimation in nonlinear chemical and biological processes with unmeasured variables from small data sets

Jang, S. S., & Gopaluni, R. B. (2011) · Chemical Engineering Science · doi:10.1016/j.ces.2011.03.029 · Cited by 25

A measure of survey mode differences

Homola, J., Jackson, N., & Gill, J. (2016) · Electoral Studies · doi:10.1016/j.electstud.2016.06.010 · Cited by 21

Statistical subspace-based damage detection with estimated reference

Viefhues, E., Döhler, M., Hille, F., & Mevel, L. (2022) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.108241 · Cited by 16

Reliability of Scales With Second-Order Structure: Evaluation of Coefficient Alpha’s Population Slippage Using Latent Variable Modeling

Raykov, T., Goldammer, P., Marcoulides, G. A., Li, T., & Menold, N. (2018) · Educational and Psychological Measurement · doi:10.1177/0013164417719948 · Cited by 13

Is cosmological tuning fine or coarse?

Díaz-Pachón, D. A., Hössjer, O., & Marks II, R. J. (2021) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2021/07/020 · Cited by 10

Quantum computing and supervised machine learning

Oneto, L., Ridella, S., & Anguita, D. (2017) · Quantum Inspired Computational Intelligence · doi:10.1016/b978-0-12-804409-4.00002-4 · Cited by 11

Chronic Physical Health Problems Moderate Changes in Depression and Substance Use Among Dual Diagnosed Individuals During and After Treatment

Cui, R., Tate, S. R., Cummins, K., Skidmore, J. R., & Brown, S. A. (2015) · Substance Use & Misuse · doi:10.3109/10826084.2014.962052 · Cited by 5