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 601–700 of 1,289
Are People Bayesian? Uncovering Behavioral Strategies
El-Gamal, M. A., & Grether, D. M. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476620 · Cited by 360
Swanson, N. R., & White, H. (1997) · The Review of Economics and Statistics · doi:10.1162/003465397557123 · Cited by 369
Lavers, D. A., Villarini, G., Allan, R. P., Wood, E. F., & Wade, A. J. (2012) · Journal of Geophysical Research Atmospheres · doi:10.1029/2012jd018027 · Cited by 365
Biotic interactions improve prediction of boreal bird distributions at macro‐scales
Heikkinen, R. K., Luoto, M., Virkkala, R., Pearson, R. G., & Körber, J. (2007) · Global Ecology and Biogeography · doi:10.1111/j.1466-8238.2007.00345.x · Cited by 365
Thymic involution and rising disease incidence with age
Palmer, S., Albergante, L., Blackburn, C. C., & Newman, T. J. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1714478115 · Cited by 364
Spatially explicit Bayesian clustering models in population genetics
François, O., & DURAND, E. (2010) · Molecular Ecology Resources · doi:10.1111/j.1755-0998.2010.02868.x · Cited by 378
De-biasing the dynamic mode decomposition for applied Koopman spectral analysis of noisy datasets
Hemati, M. S., Rowley, C. W., Deem, E. A., & Cattafesta, L. N. (2017) · Theoretical and Computational Fluid Dynamics · doi:10.1007/s00162-017-0432-2 · Cited by 363
CASME database: A dataset of spontaneous micro-expressions collected from neutralized faces
Yan, W., Wu, Q., Liu, Y., Wang, S., & Fu, X. (2013) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2013.6553799 · Cited by 363
Time Series Model Specification in the Presence of Outliers
Tsay, R. S. (1986) · Journal of the American Statistical Association · doi:10.1080/01621459.1986.10478250 · Cited by 363
Comparison of different cortical connectivity estimators for high‐resolution EEG recordings
Astolfi, L., Cincotti, F., Mattia, D., Marciani, M. G., Baccalá, L. A., Fallani, F. D. V., Salinari, S., Ursino, M., Zavaglia, M., Ding, L., et al. (2006) · Human Brain Mapping · doi:10.1002/hbm.20263 · Cited by 357
The importance of proper model assumption in bayesian phylogenetics.
Lemmon, A. R., & Moriarty, E. C. (2004) · Systematic Biology · doi:10.1080/10635150490423520 · Cited by 356
Authoritarian Reversals and Democratic Consolidation
Svolik, M. W. (2008) · American Political Science Review · doi:10.1017/s0003055408080143 · Cited by 372
Comparing decision bound and exemplar models of categorization
Maddox, W. T., & Ashby, F. G. (1993) · Perception & Psychophysics · doi:10.3758/bf03211715 · Cited by 373
Empirical profile mixture models for phylogenetic reconstruction
Le, S. Q., Gascuel, O., & Lartillot, N. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn445 · Cited by 362
Wilson, R. J., Gutiérrez, D., Illán, J. G., & Monserrat, V. J. (2007) · Global Change Biology · doi:10.1111/j.1365-2486.2007.01418.x · Cited by 367
Three crocodilian genomes reveal ancestral patterns of evolution among archosaurs
Green, R. E., Braun, E. L., Armstrong, J., Earl, D., Nguyen, N., Hickey, G., Vandewege, M. W., St. John, J. A., Capella-Gutiérrez, S., Castoe, T. A., et al. (2014) · Science · doi:10.1126/science.1254449 · Cited by 359
The Bhattacharyya Metric as an Absolute Similarity Measure for Frequency Coded Data.
Aherne, F. J., Thacker, N. A., & Rockett, P. I. (1998) · Czech digital mathematics library · Cited by 358
Schirrmeister, B. E., de Vos, J. M., Antonelli, A., & Bagheri, H. C. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1209927110 · Cited by 372
Wieling, M. (2018) · Journal of Phonetics · doi:10.1016/j.wocn.2018.03.002 · Cited by 362
Pan, J. J., Ng, W. T., Zong, J. F., Chan, L. L. K., O’Sullivan, B., Lin, S. J., Sze, H. C., Chen, Y. B., Choi, C., Guo, Q. J., et al. (2015) · Cancer · doi:10.1002/cncr.29795 · Cited by 358
GARCH Modelling of Cryptocurrencies
Chu, J., Chan, S., Nadarajah, S., & Osterrieder, J. R. (2017) · Journal of risk and financial management · doi:10.3390/jrfm10040017 · Cited by 386
ON GENERALIZED FRACTIONAL PROCESSES
Gray, H. L., Zhang, N., & Woodward, W. A. (1989) · Journal of Time Series Analysis · doi:10.1111/j.1467-9892.1989.tb00026.x · Cited by 350
A Comparative Review of Dimension Reduction Methods in Approximate Bayesian Computation
Blum, M. G. B., Nunes, M. A., Prangle, D., & Sisson, S. A. (2013) · Statistical Science · doi:10.1214/12-sts406 · Cited by 355
Are Investors Rational? Choices among Index Funds
Elton, E. J., Gruber, M. J., & Busse, J. A. (2004) · The Journal of Finance · doi:10.1111/j.1540-6261.2004.00633.x · Cited by 361
Model Specification in Multivariate Time Series
Tiao, G. C., & Tsay, R. S. (1989) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1989.tb01756.x · Cited by 354
An interference model of visual working memory.
Oberauer, K., & Lin, H.-Y. (2016) · Psychological Review · doi:10.1037/rev0000044 · Cited by 350
3FHL: The Third Catalog of Hard Fermi-LAT Sources
Ajello, M., Atwood, W. B., Baldini, L., Ballet, J., Barbiellini, G., Bastieri, D., Bellazzini, R., Bissaldi, E., Blandford, R. D., Bloom, E. D., et al. (2017) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/aa8221 · Cited by 354
Kudo, M., Chung, H., Haji, S., Osaki, Y., Oka, H., Seki, T., Kasugai, H., Sasaki, Y., & Matsunaga, T. (2004) · Hepatology · doi:10.1002/hep.20486 · Cited by 353
Fadok, V. A., Warner, M. L., Bratton, D. L., & Henson, P. M. (1998) · The Journal of Immunology · doi:10.4049/jimmunol.161.11.6250 · Cited by 353
Sáez-Rodríguez, J., Alexopoulos, L. G., Epperlein, J. P., Samaga, R., Lauffenburger, D. A., Klamt, S., & Sorger, P. K. (2009) · Molecular Systems Biology · doi:10.1038/msb.2009.87 · Cited by 360
An assessment of fixed-capacity models of visual working memory
Rouder, J. N., Morey, R. D., Cowan, N., Zwilling, C. E., Morey, C. C., & Pratte, M. S. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0711295105 · Cited by 349
CONFOUNDING AND EFFECT-MODIFICATION
Miettinen, O. S. (1974) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a112044 · Cited by 352
Geographic Constraints on Social Network Groups
Onnela, J.-P. “., Samuel Arbesman, C., González, M. C., Barabási, A.-Ĺ., & Christakis, N. A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0016939 · Cited by 351
Hsiao, C., Ching, H. S., & Wan, S. K. (2011) · Journal of Applied Econometrics · doi:10.1002/jae.1230 · Cited by 349
Interactions between serotypes of dengue highlight epidemiological impact of cross-immunity
Reich, N. G., Shrestha, S., King, A. A., Rohani, P., Lessler, J., Kalayanarooj, S., Yoon, I.-K., Gibbons, R. V., Burke, D. S., & Cummings, D. A. T. (2013) · Journal of The Royal Society Interface · doi:10.1098/rsif.2013.0414 · Cited by 346
McIntosh, S., Kamei, Y., Adams, B., & Hassan, A. E. (2014) · International Conference on Mining Software Repositories (MSR) · doi:10.1145/2597073.2597076 · Cited by 351
Asymptotic statistical theory of overtraining and cross-validation
Амари, Ш.-И., Murata, N., Müller, K. R., Finke, M., & Yang, H. (1997) · IEEE Transactions on Neural Networks · doi:10.1109/72.623200 · Cited by 351
The Effects of Oncologist Implicit Racial Bias in Racially Discordant Oncology Interactions
Penner, L. A., Dovidio, J. F., Gonzalez, R., Albrecht, T. L., Chapman, R., Foster, T. S., Harper, F. W. K., Hagiwara, N., Hamel, L. M., Shields, A. F., et al. (2016) · Journal of Clinical Oncology · doi:10.1200/jco.2015.66.3658 · Cited by 359
Unsociable Work? Nonstandard Work Schedules, Family Relationships, and Children’s Well‐Being
Strazdins, L. M., Clements, M., Korda, R., Broom, D. H., & D’Souza, R. M. (2006) · Journal of Marriage and the Family · doi:10.1111/j.1741-3737.2006.00260.x · Cited by 350
Multiple resolution segmentation of textured images
Bouman, C. A., & Liu, B. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.67641 · Cited by 352
A survey of Bayesian predictive methods for model assessment, selection and comparison
Vehtari, A., & Ojanen, J. (2012) · Statistics Surveys · doi:10.1214/12-ss102 · Cited by 366
Constrained sparse Galerkin regression
Loiseau, J.-C., & Brunton, S. L. (2018) · Journal of Fluid Mechanics · doi:10.1017/jfm.2017.823 · Cited by 346
Fast hierarchical Bayesian analysis of population structure
Tonkin‐Hill, G. Q., Lees, J. A., Bentley, S. D., Frost, S. D. W., & Corander, J. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz361 · Cited by 345
Utsu, T. (1999) · Pure and Applied Geophysics · doi:10.1007/s000240050276 · Cited by 347
Review of soft sensor methods for regression applications
Souza, F. A. A., Araújo, R. A. M., & Mendes, J. (2015) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2015.12.011 · Cited by 357
Typologies of Compulsive Buying Behavior: A Constrained Clusterwise Regression Approach
DeSarbo, W. S., & Edwards, E. A. (1996) · Journal of Consumer Psychology · doi:10.1207/s15327663jcp0503_02 · Cited by 355
Estimating Granger Causality from Fourier and Wavelet Transforms of Time Series Data
Dhamala, M., Rangarajan, G., & Ding, M. (2008) · Physical Review Letters · doi:10.1103/physrevlett.100.018701 · Cited by 346
Sadegh, M., Ragno, E., & AghaKouchak, A. (2017) · Water Resources Research · doi:10.1002/2016wr020242 · Cited by 365
Hayes, D. L., BOEHMER, J. P., Day, J. D., Gilliam, F. R., Heidenreich, P. A., Seth, M., Jones, P. W., & Saxon, L. A. (2011) · Heart Rhythm · doi:10.1016/j.hrthm.2011.04.015 · Cited by 349
THE USE OF ENTROPY IN HYDROLOGY AND WATER RESOURCES
Singh, V. P. (1997) · Hydrological Processes · doi:10.1002/(sici)1099-1085(199705)11:6 · Cited by 348
Stochastic generation of annual, monthly and daily climate data: A review
Srikanthan, R., & McMahon, T. A. (2001) · Hydrology and earth system sciences · doi:10.5194/hess-5-653-2001 · Cited by 345
Neutrino puzzle: Anomalies, interactions, and cosmological tensions
Kreisch, C. D., Cyr-Racine, F.-Y., & Doré, O. (2020) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.101.123505 · Cited by 345
Thomas, M. K., Nikooienejad, A., Bray, R., Cui, X., Wilson, J., Duffin, K., Milicevic, Z., Haupt, A., & Robins, D. A. (2021) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgaa863 · Cited by 345
Interdisciplinary Application of Nonlinear Time Series MethodsPreprint
Schreiber, T. (1998) · Cited by 348
A Review of Multitaper Spectral Analysis
Babadi, B., & Brown, E. N. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2311996 · Cited by 344
Estimating the Per-Base-Pair Mutation Rate in the Yeast Saccharomyces cerevisiae
Lang, G. I., & Murray, A. W. (2008) · Genetics · doi:10.1534/genetics.107.071506 · Cited by 344
McClure, C. J. W., Ware, H. E., Carlisle, J. D., Kaltenecker, G. S., & Barber, J. R. (2013) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2013.2290 · Cited by 336
Mazoyer, B. M., Zago, L., Jobard, G., Crivello, F., Joliot, M., Perchey, G., Mellet, E., Petit, L., & Tzourio‐Mazoyer, N. (2014) · PLoS ONE · doi:10.1371/journal.pone.0101165 · Cited by 344
Space–time ETAS models and an improved extension
Ogata, Y., & Zhuang, J. (2006) · Tectonophysics · doi:10.1016/j.tecto.2005.10.016 · Cited by 344
Panero, J. L., & Funk, V. A. (2008) · Molecular Phylogenetics and Evolution · doi:10.1016/j.ympev.2008.02.011 · Cited by 344
Earthquake scaling, fault segmentation, and structural maturity
Manighetti, I., Campillo, M., Bouley, S., & Cotton, F. (2006) · Earth and Planetary Science Letters · doi:10.1016/j.epsl.2006.11.004 · Cited by 343
van Middendorp, J. J., Hosman, A. J. F., Donders, A. R. T., Pouw, M. H., Ditunno, J. F., Curt, A. E. P., Geurts, A. C. H., & van de Meent, H. (2011) · The Lancet · doi:10.1016/s0140-6736(10)62276-3 · Cited by 351
Heteroscedastic discriminant analysis and reduced rank HMMs for improved speech recognition
Kumar, N., & Andreou, A. G. (1998) · Speech Communication · doi:10.1016/s0167-6393(98)00061-2 · Cited by 342
Urease Inhibitor NBPT on Ammonia Volatilization and Crop Productivity: A Meta‐Analysis
Silva, A. G. B., Sequeira, C. H., Sermarini, R. A., & Otto, R. (2016) · Agronomy Journal · doi:10.2134/agronj2016.04.0200 · Cited by 342
The diagnostic and prognostic capabilities of plasma biomarkers in Alzheimer's disease
Simrén, J., Leuzy, A., Karikari, T. K., Hye, A., Benedet, A. L., Lantero‐Rodriguez, J., Mattsson, N., Schöll, M., Mecocci, P., Vellas, B. J., et al. (2021) · Alzheimer s & Dementia · doi:10.1002/alz.12283 · Cited by 344
Preservation of the rescaled adjusted range: 1. A reassessment of the Hurst Phenomenon
McLeod, A. I., & Hipel, K. W. (1978) · Water Resources Research · doi:10.1029/wr014i003p00491 · Cited by 341
The Measurement Structure of the Center for Epidemiologic Studies Depression Scale
Sheehan, T. J., Fifield, J., Reisine, S., & Tennen, H. A. (1995) · Journal of Personality Assessment · doi:10.1207/s15327752jpa6403_9 · Cited by 344
Child maltreatment: variation in trends and policies in six developed countries
Gilbert, R. E., Fluke, J., O’Donnell, M., González-Izquierdo, A., Brownell, M. D., Gulliver, P. J., Janson, S., & Sidebotham, P. D. (2011) · The Lancet · doi:10.1016/s0140-6736(11)61087-8 · Cited by 340
Derby, C. A., Crawford, S. L., Pasternak, R. C., Sowers, M., Sternfeld, B., & Matthews, K. A. (2009) · American Journal of Epidemiology · doi:10.1093/aje/kwp043 · Cited by 340
The Estimation of Item Response Models with the lmer Function from the lme4 Package in R
de Boeck, P. A. L., Bakker, M., Zwitser, R. J., Nivard, M. G., Hofman, A. D., Tuerlinckx, F., & Partchev, I. (2011) · Journal of Statistical Software · doi:10.18637/jss.v039.i12 · Cited by 339
Jiang, C., Xiong, L., Wang, D., Liu, P., Guo, S., & Xu, C. (2015) · Journal of Hydrology · doi:10.1016/j.jhydrol.2014.12.060 · Cited by 339
Dixon, W. G., Abrahamowicz, M., Beauchamp, M., Ray, D. W., Bernatsky, S., Suissa, S., & Sylvestre, M. (2012) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2011-200702 · Cited by 339
The Impact of Social Networks on Parents’ Vaccination Decisions
Brunson, E. K. (2013) · PEDIATRICS · doi:10.1542/peds.2012-2452 · Cited by 339
Yang, Z., & Nielsen, R. (2008) · Molecular Biology and Evolution · doi:10.1093/molbev/msm284 · Cited by 331
Prediction of hourly solar radiation using a novel hybrid model of ARMA and TDNN
Wu, J., & Chee, K. C. (2011) · Solar Energy · doi:10.1016/j.solener.2011.01.013 · Cited by 338
Time-series analysis of pressure fluctuations in gas–solid fluidized beds – A review
van Ommen, J. R., Sasic, S., der Schaaf, J. V., Gheorghiu, S., Johnsson, F., & Coppens, M. (2010) · International Journal of Multiphase Flow · doi:10.1016/j.ijmultiphaseflow.2010.12.007 · Cited by 337
Frank, M. J., & Badre, D. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhr114 · Cited by 340
On the key role of droughts in the dynamics of summer fires in Mediterranean Europe
Turco, M., von Hardenberg, J., AghaKouchak, A., Llasat, M. C., Provenzale, A., & Trigo, R. M. (2017) · Scientific Reports · doi:10.1038/s41598-017-00116-9 · Cited by 355
The tool for the automatic analysis of lexical sophistication (TAALES): version 2.0
Kyle, K. D., Crossley, S. A., & Berger, C. M. (2017) · Behavior Research Methods · doi:10.3758/s13428-017-0924-4 · Cited by 349
Recent ecological responses to climate change support predictions of high extinction risk
Maclean, I. M. D., & Wilson, R. J. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1017352108 · Cited by 339
The Importance of Data Partitioning and the Utility of Bayes Factors in Bayesian Phylogenetics
Brown, J. M., & Lemmon, A. R. (2007) · Systematic Biology · doi:10.1080/10635150701546249 · Cited by 337
Vidal, R., Ma, Y., & Sastry, S. (2016) · Interdisciplinary applied mathematics · doi:10.1007/978-0-387-87811-9_2 · Cited by 337
Mousavi, S. M., Langston, C. A., & Horton, S. P. (2016) · Geophysics · doi:10.1190/geo2015-0598.1 · Cited by 335
Consequences of spatial autocorrelation for niche‐based models
Segurado, P., Araújo, M. B., & Kunin, W. E. (2006) · Journal of Applied Ecology · doi:10.1111/j.1365-2664.2006.01162.x · Cited by 335
Eicher, T. S., Papageorgiou, C., & Raftery, A. (2007) · Journal of Applied Econometrics · doi:10.1002/jae.1112 · Cited by 116
Response preparation and inhibition: The role of the cortical sensorimotor beta rhythm
Zhang, Y., Chen, Y., Bressler, S. L., & Ding, M. (2008) · Neuroscience · doi:10.1016/j.neuroscience.2008.06.061 · Cited by 365
Earthquake Likelihood Model Testing
Schorlemmer, D., Gerstenberger, M. C., Wiemer, S., Jackson, D. D., & Rhoades, D. A. (2007) · Seismological Research Letters · doi:10.1785/gssrl.78.1.17 · Cited by 333
Stern, A., Doron‐Faigenboim, A., Erez, E., Martz, E., Bacharach, E., & Pupko, T. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm382 · Cited by 331
A second-generation combined linkage physical map of the human genome.Preprint
Matise, T., Chen, F., Chen, W.-W., de la Vega, F. M., Hansen, M., He, C.-S., Hyland, F. C., Kennedy, G., Kong, X., Murray, S., et al. (2007) · Genome Research · doi:10.1101/gr.7156307 · Cited by 347
Cytomegalovirus infection enhances the immune response to influenza
Furman, D., Jojic, V., Sharma, S., Shen-Orr, S. S., L. Angel, C. J., Onengut-Gumuscu, S., Kidd, B. A., Maecker, H. T., Concannon, P., Dekker, C. L., et al. (2015) · Science Translational Medicine · doi:10.1126/scitranslmed.aaa2293 · Cited by 332
Behavioral components of impulsivity.
Stahl, C., Voss, A., Schmitz, F., Nuszbaum, M., Tüscher, O., Lieb, K., & Klauer, K. C. (2014) · Journal of Experimental Psychology: General · doi:10.1037/a0033981 · Cited by 319
Silberfeld, T., Leigh, J., Verbruggen, H., Cruaud, C., de Reviers, B., & Rousseau, F. (2010) · Molecular Phylogenetics and Evolution · doi:10.1016/j.ympev.2010.04.020 · Cited by 335
Stock Return Predictability: A Bayesian Model Selection Perspective
Cremers, M. (2002) · Review of Financial Studies · doi:10.1093/rfs/15.4.1223 · Cited by 332
A time-varying effect model for intensive longitudinal data.
Tan, X. M., Shiyko, M. P., Li, R., Li, Y., & Dierker, L. (2011) · Psychological Methods · doi:10.1037/a0025814 · Cited by 331
Aghanim, N., Ashdown, M., Aumont, J., Baccigalupi, C., Ballardini, M., Banday, A. J., Barreiro, R. B., Bartolo, N., Basak, S., Benabed, K., et al. (2016) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201629022 · Cited by 331
Measuring progressions: Assessment structures underlying a learning progression
Wilson, M. R. (2009) · Journal of Research in Science Teaching · doi:10.1002/tea.20318 · Cited by 343
Optimal Choice of AR and MA Parts in Autoregressive Moving Average Models
Kashyap, R. L. (1982) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1982.4767213 · Cited by 329
Quaternion-MUSIC for vector-sensor array processing
Miron, S., Bihan, N. L., & Mars, J. I. (2006) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2006.870630 · Cited by 329
Carbonell‐Caballero, J., Alonso, R., Ibáñez, V., Terol, J., Talón, M., & Dopazo, J. (2015) · Molecular Biology and Evolution · doi:10.1093/molbev/msv082 · Cited by 328
Determination of EEG Activity Propagation: Pair-Wise Versus Multichannel Estimate
Kuś, R., Kamiński, M., & Blinowska, K. J. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.827929 · Cited by 327
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
