The DOI matches its Crossref record; 47 of the 48 references it cites were found in a registry or an index.
A new look at the statistical model identification
Abstract
The history of the development of statistical hypothesis testing in time series analysis is reviewed briefly and it is pointed out that the hypothesis testing procedure is not adequately defined as the procedure for statistical model identification. The classical maximum likelihood estimation procedure is reviewed and a new estimate minimum information theoretical criterion (AIC) estimate (MAICE) which is designed for the purpose of statistical identification is introduced. When there are several competing models the MAICE is defined by the model and the maximum likelihood estimates of the parameters which give the minimum of AIC defined by AIC = (-2)log-(maximum likelihood) + 2(number of independently adjusted parameters within the model). MAICE provides a versatile procedure for statistical model identification which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure. The practical utility of MAICE in time series analysis is demonstrated with some numerical examples.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“Within a set of models for the same data, the Akaike Information Criterion (AIC; Akaike, 1974) and Expected Cross Validation Index (ECVI; Browne & Cudeck, 1993) can be used to compare competing models that need not be nested (smaller is better).”
From Nostalgia-Evoked Inspiration: Mediating Mechanisms and Motivational Implications (2015) · cites this paper as Akaike, 1974
In the citing paper
“In addition to the theoretical grounding, we further evaluated the appropriateness of using the OSMs with linear models (and linear mixed models) by computing the Akaike information criterion (AIC) [120] and the Bayesian information criterion (BIC) [121] of different models for attractiveness and each of the four dependent variables.”
From What is beautiful is still good: the attractiveness halo effect in the era of beauty filters (2024) · cites this paper as [120]
In the citing paper
“AIC measures each model's goodness of fit in proportion to the number of independent variables; when comparing models, the model with the minimal AIC is preferred (Akaike, 1974).”
From Measuring the Evolution of a Scientific Field through Citation Frames (2018) · cites this paper as Akaike, 1974
In the citing paper
“The quality of the statistical model was checked using the entropy maximization principle of the Akaike information criterion (AIC) (Akaike, 1974).”
From Persistent psychopathology and neurocognitive impairment in COVID-19 survivors: effect of inflammatory biomarkers at three-month follow-up (2021) · cites this paper as Akaike, 1974
In the citing paper
“Model fits were compared using the BIC and the AIC [34], which penalise models for the number of free parameters that are estimated to ensure that models with more parameters are not overfitted.”
From Neurocomputational mechanisms underlying subjective valuation of effort costs (2017) · cites this paper as [34]
In the citing paper
“The best model was identified using Akaike's (AIC) and Schwartz's information criteria (SBC or BIC) (45,46).”
From Influence of the COMT genotype on working memory and brain activity changes during development (2011) · cites this paper as [45]
Citations
Cites
Information Theory and an Extension of the Maximum Likelihood Principle
Akaike, H. (1998) · Springer series in statistics · doi:10.1007/978-1-4612-1694-0_15 · Cited by 18,636
Self-citationInformation theory and statistics
(1959) · Journal of the Franklin Institute · doi:10.1016/0016-0032(59)90368-0 · Cited by 7,318
Mathematical Methods of Statistics.
Geary, R. C., & Cramer, H. L. (1947) · The Economic Journal · doi:10.2307/2226151 · Cited by 6,095
Spectral Analysis and its Applications.
Freiberger, W. F., Jenkins, G. M., & Watts, D. G. (1969) · Journal of the American Statistical Association · doi:10.2307/2286100 · Cited by 4,990
(1967) · Project Euclid (Cornell University) · Cited by 4,720
ASH, R. B. (1966) · Cited by 3,990
Fitting autoregressive models for prediction
Akaike, H. (1969) · Annals of the Institute of Statistical Mathematics · doi:10.1007/bf02532251 · Cited by 2,504
Self-citationFitting Autoregreesive Models for Prediction
Akaike, H. (1998) · Springer series in statistics · doi:10.1007/978-1-4612-1694-0_10 · Cited by 2,044
Self-citationThe Statistical Analysis of Time Series.
Anderson, T. W. (1995) · Biometrics · doi:10.2307/2532976 · Cited by 2,037
Mallows, C. L. (1973) · Technometrics · doi:10.2307/1267380 · Cited by 1,978
Research works printed but not linked: 8
- akaike, 1972, Automatic data structure search by the maximum likelihood, Computer in Biomedicine Suppl to Proc 5th Hawaii Int Conf on System Sciences, 99
- akaike, 1972, Use of an information theoretic quantity for statistical model identification, Proc 5th Hawaii Int Conf System Sciences, 249
- whittle, 1962, Gaussian estimation in stationary time series, Bull Int Statist Inst, 39, 105
- whittle, 1954, The statistical analysis of a seiche record, J Marine Res, 13, 76
- box, 1970, Time Series Forecasting and Control
- anderson, 1963, Time Series Analysis, 425
- jones, 1973, Autoregressive spectrum estimation, 3rd Conf Probability and Statistics in Atmospheric Sciences
- akaike, 1970, On a semiautomatic power spectrum estimation procedure, Proc 3rd Hawaii Int Conf System Sciences, 974
Cited by
Sorted by citations
Items 501–600 of 1,289
Chouet, B. A., Dawson, P. B., Ohminato, T., Martini, M., Saccorotti, G., Giudicepietro, F., Luca, G. D., Milana, G., & Scarpa, R. (2003) · Journal of Geophysical Research Atmospheres · doi:10.1029/2002jb001919 · Cited by 418
Cognitive control over learning: Creating, clustering, and generalizing task-set structure.
Collins, A. G. E., & Frank, M. J. (2013) · Psychological Review · doi:10.1037/a0030852 · Cited by 426
Hierarchical Encoding in Visual Working Memory
Brady, T. F., & Alvarez, G. A. (2011) · Psychological Science · doi:10.1177/0956797610397956 · Cited by 420
Seismicity Analysis through Point-process Modeling: A Review
Ogata, Y. (1999) · Pure and Applied Geophysics · doi:10.1007/s000240050275 · Cited by 416
Copper distribution in European topsoils: An assessment based on LUCAS soil survey
Ballabio, C., Panagos, P., Lugato, E., Huang, J., Orgiazzi, A., Jones, A., Fernández‐Ugalde, O., Borrelli, P., & Montanarella, L. (2018) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2018.04.268 · Cited by 416
Neurocomputational mechanisms underlying subjective valuation of effort costs
Chong, T. T.-J., Apps, M., Giehl, K., Sillence, A., Grima, L. L., & Husain, M. (2017) · PLOS Biology · doi:10.1371/journal.pbio.1002598 · Cited by 415
Ultrahigh dimensional feature selection: beyond the linear model.
Fan, J., Samworth, R. J., & Wu, Y. (2009) · PubMed · Cited by 422
Land-use change and carbon sinks: Econometric estimation of the carbon sequestration supply function
Lubowski, R. N., Plantinga, A. J., & Stavins, R. N. (2005) · Journal of Environmental Economics and Management · doi:10.1016/j.jeem.2005.08.001 · Cited by 411
Introduction to Spectral Analysis
Percival, D. B., & Walden, A. T. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139235723.002 · Cited by 413
LSTM based long-term energy consumption prediction with periodicity
Wang, J. Q., Du, Y., & Wang, J. (2020) · Energy · doi:10.1016/j.energy.2020.117197 · Cited by 433
Pedestrian fatality risk as a function of car impact speed
Rosén, E., & Sander, U. (2009) · Accident Analysis & Prevention · doi:10.1016/j.aap.2009.02.002 · Cited by 410
Comparison of Interwar and Postwar Business Cycles: Monetarism Reconsidered
Sims, C. A. (1980) · National Bureau of Economic Research · doi:10.3386/w0430 · Cited by 410
Heart Rate Variability Biofeedback Increases Baroreflex Gain and Peak Expiratory Flow
Lehrer, P. M., Vaschillo, E., Vaschillo, B., Lu, S.-E., Eckberg, D. L., Edelberg, R., Shih, W. J., Lin, Y., Kuusela, T. A., Tahvanainen, K. U. O., et al. (2003) · Psychosomatic Medicine · doi:10.1097/01.psy.0000089200.81962.19 · Cited by 497
Functional data clustering: a survey
Jacques, J., & Preda, C. (2013) · Advances in Data Analysis and Classification · doi:10.1007/s11634-013-0158-y · Cited by 429
Schäfer, R. B., Caquet, T., Siimes, K., Mueller, R., Lagadic, L., & Liess, M. (2007) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2007.04.040 · Cited by 409
Environmental Warming and Feminization of One of the Largest Sea Turtle Populations in the World
Jensen, M. P., Allen, C. D., Eguchi, T., Bell, I. P., LaCasella, E. L., Hilton, W. A., Hof, C. A. M., & Dutton, P. H. (2018) · Current Biology · doi:10.1016/j.cub.2017.11.057 · Cited by 421
The Incidence of Leukemia, Lymphoma and Multiple Myeloma among Atomic Bomb Survivors: 1950–2001
Hsu, W.-L., Preston, D. L., Soda, M., Sugiyama, H., Funamoto, S., Kodama, K., Kimura, A., Kamada, N., Dohy, H., Tomonaga, M., et al. (2013) · Radiation Research · doi:10.1667/rr2892.1 · Cited by 409
Model selection may not be a mandatory step for phylogeny reconstruction
Abadi, S., Azouri, D., Pupko, T., & Mayrose, I. (2019) · Nature Communications · doi:10.1038/s41467-019-08822-w · Cited by 413
Gebremedhin, A. T., Hogan, A. B., Blyth, C. C., Glass, K., & Moore, H. C. (2022) · Scientific Reports · doi:10.1038/s41598-021-04080-3 · Cited by 407
Fadok, V. A., Warner, M. L., Bratton, D. L., & Henson, P. M. (1998) · PubMed · Cited by 406
Givnish, T. J., Barfuss, M. H. J., van Ee, B. W., Riina, R., Schulte, K., Horres, R., Gonsiska, P. A., Jabaily, R. S., Crayn, D. M., Smith, J. A. C., et al. (2013) · Molecular Phylogenetics and Evolution · doi:10.1016/j.ympev.2013.10.010 · Cited by 406
Measuring Syntactic Complexity in L2 Writing Using Fine‐Grained Clausal and Phrasal Indices
KYLE, K., & CROSSLEY, S. A. (2018) · The Modern Language Journal · doi:10.1111/modl.12468 · Cited by 405
Fast approximate energy minimization with label costs
Delong, A., Osokin, A., Isack, H. N., & Boykov, Y. Y. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5539897 · Cited by 414
Glycans Are a Novel Biomarker of Chronological and Biological Ages
Krištić, J., Vučković, F., Menni, C., Klarić, L., Keser, T., Beceheli, I., Pučić-Baković, M., Novokmet, M., Mangino, M., Thaqi, K., et al. (2014) · The Journals of Gerontology: Series A · doi:10.1093/gerona/glt190 · Cited by 400
Comas, I., Homolka, S., Niemann, S., & Gagneux, S. (2009) · PLoS ONE · doi:10.1371/journal.pone.0007815 · Cited by 425
Inferring biological networks by sparse identification of nonlinear dynamics
Mangan, N. M., Brunton, S. L., Proctor, J. L., & Kutz, J. N. (2016) · IEEE Transactions on Molecular, Biological and Multi-Scale Communications · doi:10.1109/tmbmc.2016.2633265 · Cited by 436
Effect of transportation infrastructure on economic growth in India: The VECM approach
Pradhan, R. P., & Bagchi, T. P. (2012) · Research in Transportation Economics · doi:10.1016/j.retrec.2012.05.008 · Cited by 401
Boredom and academic achievement: Testing a model of reciprocal causation.
Pekrun, R. H., Hall, N. C., Goetz, T., & Perry, R. P. (2014) · Journal of Educational Psychology · doi:10.1037/a0036006 · Cited by 442
How Far Are Automatically Chosen Regression Smoothing Parameters From Their Optimum?
Härdle, W. K., Hall, P. M., & Marron, J. S. (1988) · Journal of the American Statistical Association · doi:10.1080/01621459.1988.10478568 · Cited by 400
Joint Species Distribution Modelling
Ovaskainen, O., & Abrego, N. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108591720 · Cited by 458
Albers, J. W., Herman, W. H., Pop‐Busui, R., Feldman, E. L., Martin, C. L., Cleary, P. A., Waberski, B. H., & Lachin, J. M. (2010) · Diabetes Care · doi:10.2337/dc09-1941 · Cited by 407
A Least-squares Approach to Direct Importance Estimation
Kanamori, T., Hido, S., & Sugiyama, M. (2009) · doi:10.5555/1577069.1755831 · Cited by 428
Behaviour Analysis of Multilayer Perceptronswith Multiple Hidden Neurons and Hidden Layers
Panchal, G., Ganatra, A. P., Kosta, Y. P., & Panchal, D. (2011) · International Journal of Computer Theory and Engineering · doi:10.7763/ijcte.2011.v3.328 · Cited by 407
Ecology of butterflies in Europe
(2010) · Choice Reviews Online · doi:10.5860/choice.47-5648 · Cited by 399
A CORRECTED AKAIKE INFORMATION CRITERION FOR VECTOR AUTOREGRESSIVE MODEL SELECTION
Hurvich, C. M., & Tsai, C. (1993) · Journal of Time Series Analysis · doi:10.1111/j.1467-9892.1993.tb00144.x · Cited by 398
Complex-Valued Signal Processing: The Proper Way to Deal With Impropriety
Adalı, T., Schreier, P. J., & Scharf, L. L. (2011) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2011.2162954 · Cited by 398
Bradshaw, C. P., Sawyer, A. L., & O’Brennan, L. M. (2009) · American Journal of Community Psychology · doi:10.1007/s10464-009-9240-1 · Cited by 397
Global operating theatre distribution and pulse oximetry supply: an estimation from reported data
Funk, L. M., Weiser, T. G., Berry, W. R., Lipsitz, S. R., Merry, A. F., Enright, A. C., Wilson, I. H., Dziekan, G., & Gawande, A. A. (2010) · The Lancet · doi:10.1016/s0140-6736(10)60392-3 · Cited by 397
Zebrack, B., Gurney, J. G., Oeffinger, K. C., Whitton, J. A., Packer, R. J., Mertens, A. C., Turk, N., Castleberry, R. P., Dreyer, Z. E., Robison, L. L., et al. (2004) · Journal of Clinical Oncology · doi:10.1200/jco.2004.06.148 · Cited by 410
Gnant, M., Mlineritsch, B., Luschin-Ebengreuth, G., Kainberger, F. M., Kässmann, H., Piswanger-Sölkner, J. C., Seifert, M., Ploner, F., Menzel, C., Dubsky, P. C., et al. (2008) · The Lancet Oncology · doi:10.1016/s1470-2045(08)70204-3 · Cited by 396
Taormina, R., Chau, K., & Sethi, R. (2012) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2012.02.009 · Cited by 402
Clinical effect of driver mutations of JAK2, CALR, or MPL in primary myelofibrosis
Rumi, E., Pietra, D., Pascutto, C., Guglielmelli, P., Martínez‐Trillos, A., Casetti, I. C., Colomer, D., Pieri, L., Pratcorona, M., Rotunno, G., et al. (2014) · Blood · doi:10.1182/blood-2014-05-578435 · Cited by 397
Random forests as a tool for ecohydrological distribution modelling
Peters, J., Bernard De Baets, C., Verhoest, N. E. C., Samson, R., Degroeve, S., Becker, P. D., & Huybrechts, W. (2007) · Ecological Modelling · doi:10.1016/j.ecolmodel.2007.05.011 · Cited by 398
Analyzing information flow in brain networks with nonparametric Granger causality
Dhamala, M., Govindan Rangarajan, C., & Ding, M. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.02.020 · Cited by 392
Bradburn, M. J., Clark, T. G., Love, S., & Altman, D. G. (2003) · British Journal of Cancer · doi:10.1038/sj.bjc.6601120 · Cited by 391
CROSS-VALIDATED LOCAL LINEAR NONPARAMETRIC REGRESSION
Li, Q., & Racine, J. S. (2004) · Cited by 391
A survey of Bayesian Network structure learning
Kitson, N. K., Constantinou, A. C., Guo, Z., Liu, Y., & Chobtham, K. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-022-10351-w · Cited by 393
AIC, BIC and Recent Advances in Model Selection
Chakrabarti, A., & Ghosh, J. K. (2011) · Elsevier eBooks · doi:10.1016/b978-0-444-51862-0.50018-6 · Cited by 390
Chou, I.-C., & Voit, E. O. (2009) · Mathematical Biosciences · doi:10.1016/j.mbs.2009.03.002 · Cited by 399
Arias‐Arévalo, P., Martín‐López, B., & Gómez‐Baggethun, E. (2017) · Ecology and Society · doi:10.5751/es-09812-220443 · Cited by 390
Integrated driving behavior modeling
Toledo, T., Koutsopoulos, H. N., & Ben-Akiva, M. E. (2007) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2007.02.002 · Cited by 389
Fast Approximate Energy Minimization with Label Costs
Delong, A., Osokin, A., Isack, H. N., & Boykov, Y. Y. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-011-0437-z · Cited by 392
Pollard, D. A., Iyer, V. N., Moses, A. M., & Eisen, M. B. (2006) · PLoS Genetics · doi:10.1371/journal.pgen.0020173 · Cited by 389
Beaulieu, J. M., O'Meara, B. C., & Donoghue, M. J. (2013) · Systematic Biology · doi:10.1093/sysbio/syt034 · Cited by 375
Efficient and principled method for detecting communities in networks
Ball, B., Karrer, B., & Newman, M. E. J. (2011) · Physical Review E · doi:10.1103/physreve.84.036103 · Cited by 394
Model Selection Techniques: An Overview
Ding, J., Tarokh, V., & Yang, Y. (2018) · IEEE Signal Processing Magazine · doi:10.1109/msp.2018.2867638 · Cited by 414
Key Concepts in Model Selection: Performance and Generalizability
Forster, M. R. (2000) · Journal of Mathematical Psychology · doi:10.1006/jmps.1999.1284 · Cited by 390
Mittelman, M. S., Roth, D. L., Coon, D. W., & Haley, W. E. (2004) · American Journal of Psychiatry · doi:10.1176/appi.ajp.161.5.850 · Cited by 385
Christensen, A., Atkins, D. C., Berns, S., Wheeler, J., Baucom, D. H., & Simpson, L. E. (2004) · Journal of Consulting and Clinical Psychology · doi:10.1037/0022-006x.72.2.176 · Cited by 380
Maximum likelihood and prediction error methods
Åström, K. J. (1980) · Automatica · doi:10.1016/0005-1098(80)90078-3 · Cited by 384
Korzeniewska, A., Mańczak, M., Kamiński, M., Blinowska, K. J., & Kasicki, S. (2003) · Journal of Neuroscience Methods · doi:10.1016/s0165-0270(03)00052-9 · Cited by 375
Review of Input Variable Selection Methods for Artificial Neural Networks
May, R. J., Dandy, G. C., & Maier, H. R. (2011) · InTech eBooks · doi:10.5772/16004 · Cited by 382
Hierarchical Block Structures and High-Resolution Model Selection in Large Networks
Peixoto, T. P. (2014) · Physical Review X · doi:10.1103/physrevx.4.011047 · Cited by 419
Longitudinal changes in cortical thickness in autism and typical development
Zielinski, B. A., Prigge, M. B. D., Nielsen, J. A., Froehlich, A. L., Abildskov, T. J., Anderson, J. S., Fletcher, P. T., Zygmunt, K. M., Travers, B. G., Lange, N., et al. (2014) · Brain · doi:10.1093/brain/awu083 · Cited by 378
Loneliness, Mental Health, and Substance Use among US Young Adults during COVID-19
Horigian, V. E., Schmidt, R. D., & Feaster, D. J. (2020) · Journal of Psychoactive Drugs · doi:10.1080/02791072.2020.1836435 · Cited by 427
Osu, R., Franklin, D. W., Kato, H., Gomi, H., Domen, K., Yoshioka, T., & Kawato, M. (2002) · Journal of Neurophysiology · doi:10.1152/jn.2002.88.2.991 · Cited by 380
Zebrack, B., Zeltzer, L. K., Whitton, J. A., Mertens, A. C., Odom, L. F., Berkow, R. L., & Robison, L. L. (2002) · PEDIATRICS · doi:10.1542/peds.110.1.42 · Cited by 385
Muroid rodent phylogenetics: 900-species tree reveals increasing diversification rates
Steppan, S. J., & Schenk, J. J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0183070 · Cited by 374
The statistical physics of real-world networks
Cimini, G., Squartini, T., Saracco, F., Garlaschelli, D., Gabrielli, A., & Caldarelli, G. (2018) · Nature Reviews Physics · doi:10.1038/s42254-018-0002-6 · Cited by 391
Xu, L., Groth, K. M., Pearlson, G. D., Schretlen, D. J., & Calhoun, V. D. (2008) · Human Brain Mapping · doi:10.1002/hbm.20540 · Cited by 383
The structure of online social networks mirrors those in the offline world
Dunbar, R., Arnaboldi, V., Conti, M., & Passarella, A. (2015) · Social Networks · doi:10.1016/j.socnet.2015.04.005 · Cited by 397
Brain Development Cooperative Group (2011) · Cerebral Cortex · doi:10.1093/cercor/bhr018 · Cited by 394
multiTree: A computer program for the analysis of multinomial processing tree models
Moshagen, M. (2010) · Behavior Research Methods · doi:10.3758/brm.42.1.42 · Cited by 381
Yonelinas, A. P. (1999) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037//0278-7393.25.6.1415 · Cited by 376
Ballen, C. J., Wieman, C., Salehi, S., Searle, J. B., & Zamudio, K. R. (2017) · CBE—Life Sciences Education · doi:10.1187/cbe.16-12-0344 · Cited by 426
Park, C. L., Edmondson, D., Fenster, J. R., & Blank, T. O. (2008) · Journal of Consulting and Clinical Psychology · doi:10.1037/a0013348 · Cited by 376
Differential Geometry of Curved Exponential Families-Curvatures and Information Loss
Амари, Ш.-И. (1982) · The Annals of Statistics · doi:10.1214/aos/1176345779 · Cited by 375
Survival Trees by Goodness of Split
LeBlanc, M. L., & Crowley, J. J. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10476296 · Cited by 374
How Meaningful Are Bayesian Support Values?
Simmons, M. P., Pickett, K. M., & Miya, M. U. (2004) · Molecular Biology and Evolution · doi:10.1093/molbev/msh014 · Cited by 374
Bradshaw, C. P., Mitchell, M. M., O'Brennan, L. M., & Leaf, P. J. (2010) · Journal of Educational Psychology · doi:10.1037/a0018450 · Cited by 373
HUBBLE RESIDUALS OF NEARBY TYPE Ia SUPERNOVAE ARE CORRELATED WITH HOST GALAXY MASSES
Kelly, P. L., Hicken, M., Burke, D. L., Mandel, K. S., & Kirshner, R. P. (2010) · The Astrophysical Journal · doi:10.1088/0004-637x/715/2/743 · Cited by 373
Regressive Logistic Models for Familial Disease and Other Binary Traits
Bonney, G. E. (1986) · Biometrics · doi:10.2307/2531211 · Cited by 373
Association of histologic disease activity with progression of nonalcoholic fatty liver disease
Kleiner, D. E., Brunt, E. M., Wilson, L. A., Behling, C., Guy, C., Contos, M., Cummings, O., Yeh, M., Gill, R., Chalasani, N., et al. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.12565 · Cited by 372
Akaike, H. (1985) · Springer eBooks · doi:10.1007/978-1-4613-8560-8_1 · Cited by 373
Self-citationWurdack, K. J., & Davis, C. “. (2009) · American Journal of Botany · doi:10.3732/ajb.0800207 · Cited by 368
Divers, J., Mayer‐Davis, E. J., Lawrence, J. M., Isom, S. P., Dabelea, D., Dolan, L. M., Imperatore, G., Marcovina, S. M., Pettitt, D. J., Pihoker, C., et al. (2020) · MMWR Morbidity and Mortality Weekly Report · doi:10.15585/mmwr.mm6906a3 · Cited by 372
Lectures on Algebraic Statistics
Drton, M., Sturmfels, B., & Sullivant, S. (2009) · Birkhäuser Basel eBooks · doi:10.1007/978-3-7643-8905-5 · Cited by 371
Ogata, Y., & Katsura, K. (1993) · Geophysical Journal International · doi:10.1111/j.1365-246x.1993.tb04663.x · Cited by 371
What measure of temperature is the best predictor of mortality?
Barnett, A. G., Tong, S., & Clements, A. (2010) · Environmental Research · doi:10.1016/j.envres.2010.05.006 · Cited by 370
New Dimensions in the Quantitative Classification of Mental Illness
Kotov, R. (2011) · Archives of General Psychiatry · doi:10.1001/archgenpsychiatry.2011.107 · Cited by 381
SimpleTree —An Efficient Open Source Tool to Build Tree Models from TLS Clouds
Hackenberg, J., Spiecker, H., Calders, K., Disney, M. I., & Raumonen, P. (2015) · Forests · doi:10.3390/f6114245 · Cited by 370
Fundamentals of Enzyme Kinetics
Seibert, E., & Tracy, T. S. (2014) · Methods in molecular biology · doi:10.1007/978-1-62703-758-7_2 · Cited by 370
Ruppert, A. S., Dixon, J. G., Salles, G., Wall, A. K. M., Cunningham, D., Poeschel, V., Haïoun, C., Tilly, H., Ghesquières, H., Ziepert, M., et al. (2020) · Blood · doi:10.1182/blood.2019002729 · Cited by 371
Comparison of algorithms for estimation of EMG variables during voluntary isometric contractions
Farina, D., & Merletti, R. (2000) · Journal of Electromyography and Kinesiology · doi:10.1016/s1050-6411(00)00025-0 · Cited by 368
Shuman, W. P., Branch, K. R., May, J. M., Mitsumori, L. M., Lockhart, D. W., Dubinsky, T. J., Warren, B. H., & Caldwell, J. R. (2008) · Radiology · doi:10.1148/radiol.2482072192 · Cited by 368
Model selection in neural networks
Anders, U., & Korn, O. (1999) · Neural Networks · doi:10.1016/s0893-6080(98)00117-8 · Cited by 367
Cardiovascular Risk of Celecoxib in 6 Randomized Placebo-Controlled Trials
Solomon, S. D., Wittes, J. T., Finn, P. V., Fowler, R. A., Viner, J. L., Bertagnolli, M. M., Arber, N., Levin, B., Meinert, C. L., Martin, B. K., et al. (2008) · Circulation · doi:10.1161/circulationaha.108.764530 · Cited by 367
Relationships between structural complexity, coral traits, and reef fish assemblages
Darling, E. S., Graham, N. A. J., Januchowski‐Hartley, F. A., Nash, K. L., Pratchett, M. S., & Wilson, S. K. (2017) · Coral Reefs · doi:10.1007/s00338-017-1539-z · Cited by 397
Novikov, D. S., Kiselev, V. G., & Jespersen, S. N. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27101 · Cited by 362
Model selection tests for nonlinear dynamic models
Rivers, D., & Vuong, Q. (2002) · Econometrics Journal · doi:10.1111/1368-423x.t01-1-00071 · Cited by 366
Metadata
- Authors
- Akaike, H.
- Affiliations
- The Institute of Statistical Mathematics (JP)
- Corresponding author
- Akaike, H.
- Venue
- IEEE Transactions on Automatic Control
- Published
- 1974-12
- Volume
- 19
- Issue
- 6
- Pages
- 716-723
- ISSN
- 0018-9286 (ISSN-L)
- OpenAlex
- W2142635246
- Cited by
- 51,879
- References
- 48
- Citations by year
- 2026: 2363 · 2025: 2646 · 2024: 2436 · 2023: 2545 · 2022: 2756 · 2021: 3085 · 2020: 2796 · 2019: 2643 · 2018: 2462 · 2017: 2474 · 2016: 2272 · 2015: 2369 · 2014: 2357 · 2013: 2274 · 2012: 2097
- FWCI
- 42.49
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Automatic Control (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Physical Sciences › Mathematics › Applied Mathematics › Statistical and numerical algorithms
- Keywords
- time series analysis, hypothesis testing, maximum likelihood estimation, Akaike information criterion, model selection
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
