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 901–1,000 of 1,289
Adult glioma incidence trends in the United States, 1977–2000
Hess, K. R., Broglio, K. R., & Bondy, M. L. (2004) · Cancer · doi:10.1002/cncr.20621 · Cited by 270
Denoising of Hyperspectral Images Using the PARAFAC Model and Statistical Performance Analysis
Liu, X., Bourennane, S., & Fossati, C. (2012) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2012.2187063 · Cited by 270
A simple equilibrium model for predicting shoreline change
Davidson, M., Splinter, K. D., & Turner, I. L. (2012) · Coastal Engineering · doi:10.1016/j.coastaleng.2012.11.002 · Cited by 270
Extensive arsenic contamination in high-pH unconfined aquifers in the Indus Valley
Podgorski, J. E., Eqani, S. A. M. A. S., Khanam, T., Ullah, R., Shen, H., & Berg, M. D. V. (2017) · Science Advances · doi:10.1126/sciadv.1700935 · Cited by 276
An empirical evaluation of the overconfidence hypothesis
Chuang, W.-I., & Lee, B. (2006) · Journal of Banking & Finance · doi:10.1016/j.jbankfin.2005.08.007 · Cited by 271
On the frequency of heavy rainfall for the Midwest of the United States
Villarini, G., Smith, J. A., Baeck, M. L., Vitolo, R., Stephenson, D. B., & Krajewski, W. F. (2011) · Journal of Hydrology · doi:10.1016/j.jhydrol.2011.01.027 · Cited by 268
An automatic Portmanteau test for serial correlation
Escanciano, J. C., & Lobato, I. N. (2009) · Journal of Econometrics · doi:10.1016/j.jeconom.2009.03.001 · Cited by 269
Liu, J., Pearlson, G. D., Windemuth, A., Ruaño, G., Perrone‐Bizzozero, N. I., & Calhoun, V. D. (2007) · Human Brain Mapping · doi:10.1002/hbm.20508 · Cited by 271
Alshehry, Z. H., Mundra, P. A., Barlow, C. K., Mellett, N. A., Wong, G., McConville, M. J., Simes, J. R., Tonkin, A., SULLIVAN, D. R., Barnes, E. H., et al. (2016) · Circulation · doi:10.1161/circulationaha.116.023233 · Cited by 270
Gómez-Valent, A., & Amendola, L. (2018) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2018/04/051 · Cited by 288
Salerno, F., Viviano, G., & Tartari, G. (2018) · Water Research · doi:10.1016/j.watres.2018.07.058 · Cited by 269
Systems biology: experimental design
Kreutz, C., & Timmer, J. (2009) · The FEBS Journal · doi:10.1111/j.1742-4658.2008.06843.x · Cited by 257
Li, Y., & Giles, D. E. A. (2014) · International Journal of Finance & Economics · doi:10.1002/ijfe.1506 · Cited by 268
Assessing bimodality to detect the presence of a dual cognitive process
Freeman, J. B., & Dale, R. A. C. (2012) · Behavior Research Methods · doi:10.3758/s13428-012-0225-x · Cited by 268
Learning Bayesian network structures by searching for the best ordering with genetic algorithms
Larrañaga, P., Kuijpers, C. M. H., Murga, R. H., & Yurramendi, Y. (1996) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/3468.508827 · Cited by 268
Laruelle, M. A., Wallace, E. A., Seibyl, J. P., Baldwin, R. M., Zea‐Ponce, Y., Zoghbi, S. S., Neumeyer, J. L., Charney, D. S., Hoffer, P. B., & Innis, R. B. (1994) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1038/jcbfm.1994.131 · Cited by 268
Nabiximols as an Agonist Replacement Therapy During Cannabis Withdrawal: A Randomized Clinical Trial
Allsop, D. J., Copeland, J., Lintzeris, N., Dunlop, A. J., Montebello, M., Sadler, C., Rivas, G. R., Holland, R. M., Muhleisen, P., Norberg, M. M., et al. (2014) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2013.3947 · Cited by 266
Environmental effects on the identified natural frequencies of the Dowling Hall Footbridge
Moser, P., & Moaveni, B. (2011) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2011.03.005 · Cited by 267
Forecasting Daily Patient Volumes in the Emergency Department
Jones, S. S., Thomas, A., Evans, R. S., Welch, S. J., Haug, P. J., & Snow, G. L. (2008) · Academic Emergency Medicine · doi:10.1111/j.1553-2712.2007.00032.x · Cited by 289
Lee, Y.-S., & Tong, L. (2010) · Knowledge-Based Systems · doi:10.1016/j.knosys.2010.07.006 · Cited by 267
Newtonized Orthogonal Matching Pursuit: Frequency Estimation Over the Continuum
Mamandipoor, B., Ramasamy, D., & Madhow, U. (2016) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2016.2580523 · Cited by 267
Sparse reduced-order modelling: sensor-based dynamics to full-state estimation
Loiseau, J.-C., Noack, B. R., & Brunton, S. L. (2018) · Journal of Fluid Mechanics · doi:10.1017/jfm.2018.147 · Cited by 265
Drought timing and local climate determine the sensitivity of eastern temperate forests to drought
D’Orangeville, L., Maxwell, J. T., Kneeshaw, D., Pederson, N., Duchesne, L., Logan, T., Houle, D., Arseneault, D., Beier, C. M., Bishop, D. A., et al. (2018) · Global Change Biology · doi:10.1111/gcb.14096 · Cited by 266
Reconstructing the deep-branching relationships of the papilionoid legumes
Cardoso, D., Pennington, R. T., de Queiroz, L. P., Boatwright, J. S., Wyk, B.-E. V., Wojciechowski, M. F., & Lavin, M. (2013) · South African Journal of Botany · doi:10.1016/j.sajb.2013.05.001 · Cited by 266
van Mierlo, P., Papadopoulou, M., Carrette, E., Boon, P., Vandenberghe, S., Vonck, K. E. J., & Marinazzo, D. (2014) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2014.06.004 · Cited by 262
Detection of precursory relative quiescence before great earthquakes through a statistical model
Ogata, Y. (1992) · Journal of Geophysical Research Atmospheres · doi:10.1029/92jb00708 · Cited by 266
Bigelow, K., Boggs, C. H., & He, X. (1999) · Fisheries Oceanography · doi:10.1046/j.1365-2419.1999.00105.x · Cited by 266
Recursive unsupervised learning of finite mixture models
Živković, Z., & van der Heijden, F. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.1273970 · Cited by 266
Graph-based analysis of brain connectivity in schizophrenia
Olejarczyk, E., & Jernajczyk, W. (2017) · PLoS ONE · doi:10.1371/journal.pone.0188629 · Cited by 267
Detection of nonlinear dynamics in short, noisy time series
Barahona, M., & Poon, C. (1996) · Nature · doi:10.1038/381215a0 · Cited by 266
How human–AI feedback loops alter human perceptual, emotional and social judgements
Glickman, M., & Sharot, T. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-02077-2 · Cited by 289
Lines, E. R., Zavala, M. A., Purves, D. W., & Coomes, D. A. (2012) · Global Ecology and Biogeography · doi:10.1111/j.1466-8238.2011.00746.x · Cited by 265
Non-stationary approach to at-site flood frequency modelling I. Maximum likelihood estimation
Strupczewski, W. G., Singh, V. P., & Feluch, W. (2001) · Journal of Hydrology · doi:10.1016/s0022-1694(01)00397-3 · Cited by 268
ARIMA forecasting of ambient air pollutants (O3, NO, NO2 and CO)
Kumar, U., & Jain, V. K. (2009) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-009-0361-8 · Cited by 265
Marquand, A. F., Wolfers, T., Mennes, M., Buitelaar, J. K., & Beckmann, C. F. (2016) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2016.04.002 · Cited by 267
Terlouw, T., AlSkaif, T. A., Bauer, C., & van Sark, W. G. J. H. M. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.01.227 · Cited by 278
HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity
Niso, G., Bruña, R., Pereda, E., Gutiérrez, R., Bajo, R., Maestú, F., & Pozo Guerrero, F. D. (2013) · UPM Digital Archive (Technical University of Madrid) · Cited by 268
A Smoothness Priors–State Space Modeling of Time Series with Trend and Seasonality
Kitagawa, G., & Gersch, W. (1984) · Journal of the American Statistical Association · doi:10.1080/01621459.1984.10478060 · Cited by 265
Unit roots in real GNP: Do we know, and do we care?
Christiano, L. J., & Eichenbaum, M. (1990) · Carnegie-Rochester Conference Series on Public Policy · doi:10.1016/0167-2231(90)90021-c · Cited by 265
Signal processing techniques applied to human sleep EEG signals—A review
Motamedi‐Fakhr, S., Moshrefi‐Torbati, M., Hill, M., Hill, C. M., & White, P. R. (2014) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2013.12.003 · Cited by 267
Hari, D. M., Leung, A. M., Lee, J., Sim, M., Vuong, B., Chiu, C. G., & Bilchik, A. J. (2013) · Journal of the American College of Surgeons · doi:10.1016/j.jamcollsurg.2013.04.018 · Cited by 264
Conditional Heteroscedastic Time Series Models
Tsay, R. S. (1987) · Journal of the American Statistical Association · doi:10.1080/01621459.1987.10478471 · Cited by 264
Multivariate Granger causality and generalized variance
Barrett, A. B., Barnett, L., & Seth, A. K. (2010) · Physical Review E · doi:10.1103/physreve.81.041907 · Cited by 272
The effect of tRNA levels on decoding times of mRNA codons
Dana, A., & Tuller, T. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku646 · Cited by 258
Biofeedback treatment for asthma.
Lehrer, P. M., Vaschillo, E., Vaschillo, B., Lu, S.-E., Scardella, A., Siddique, M., & Habib, R. H. (2004) · Chest · doi:10.1378/chest.126.2.352 · Cited by 264
New Heuristic Methods for Joint Species Delimitation and Species Tree Inference
O’Meara, B. C. (2009) · Systematic Biology · doi:10.1093/sysbio/syp077 · Cited by 264
Statistical procedures for developing earthquake damage fragility curves
Lallemant, D., Kiremidjian, A. S., & Burton, H. V. (2015) · Earthquake Engineering & Structural Dynamics · doi:10.1002/eqe.2522 · Cited by 265
Lin, X., Wang, X., & Hajli, N. (2019) · International Journal of Electronic Commerce · doi:10.1080/10864415.2019.1619907 · Cited by 313
Commonsense Knowledge Mining from Pretrained Models
Davison, J., Feldman, J., & Rush, A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1109 · Cited by 264
Phylogeny of the genus Pythium and description of new genera
Uzuhashi, S., Kakishima, M., & Tojo, M. (2010) · Mycoscience · doi:10.1007/s10267-010-0046-7 · Cited by 270
Environmental time series analysis and forecasting with the Captain toolbox
Taylor, C. J., Pedregal, D. J., Young, P., & Tych, W. (2006) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2006.03.002 · Cited by 263
Specification of spatial models: A simulation study on weights matrices
Stakhovych, S., & Bijmolt, T. H. A. (2009) · Papers of the Regional Science Association · doi:10.1111/j.1435-5957.2008.00213.x · Cited by 263
A Unified Probabilistic Framework for Name Disambiguation in Digital Library
Tang, J., Fong, A. C. M., Wang, B., & Zhang, J. (2011) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2011.13 · Cited by 259
Recovery from the Middle East respiratory syndrome is associated with antibody and T cell responses
Zhao, J., Alshukairi, A. N., Baharoon, S. A., Ahmed, W., Bokhari, A. A., Nehdi, A., Layqah, L. A., Alghamdi, M., Algethamy, M., Dada, A. M., et al. (2017) · Science Immunology · doi:10.1126/sciimmunol.aan5393 · Cited by 262
Spatial variation in the fine-structure constant - new results from VLT/UVES
King, J. A., Webb, J. K., Murphy, M. T., Flambaum, V. V., Carswell, R. F., Bainbridge, M. B., Wilczynska, M. R., & Koch, F. E. (2012) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2012.20852.x · Cited by 261
A smoothness priors time-varying AR coefficient modeling of nonstationary covariance time series
Kitagawa, G., & Gersch, W. (1985) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1985.1103788 · Cited by 263
On model selection criteria in multimodel analysis
Ye, M., Meyer, P. D., & Neuman, S. P. (2008) · Water Resources Research · doi:10.1029/2008wr006803 · Cited by 273
Identification of Two Distinct Hybrid State Intermediates on the Ribosome
Munro, J. B., Altman, R. B., O’Connor, N., & Blanchard, S. C. (2007) · Molecular Cell · doi:10.1016/j.molcel.2007.01.022 · Cited by 262
Drought Forecasting Using a Hybrid Stochastic and Neural Network Model
Mishra, A. K., Desai, V. R., & Singh, V. P. (2007) · Journal of Hydrologic Engineering · doi:10.1061/(asce)1084-0699(2007)12:6(626 · Cited by 264
Salvaterra, R., Campana, S., Vergani, S. D., Covino, S., D’Avanzo, P., Fugazza, D. P., Ghirlanda, G., Ghisellini, G., Melandri, A., Nava, L., et al. (2012) · The Astrophysical Journal · doi:10.1088/0004-637x/749/1/68 · Cited by 262
A new modified Weibull distribution
Almalki, S. J., & Yuan, J. (2012) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2012.10.018 · Cited by 262
Whitlock, J. L., Muehlenkamp, J. J., & Eckenrode, J. J. (2008) · Journal of Clinical Child & Adolescent Psychology · doi:10.1080/15374410802359734 · Cited by 262
Feng, T., Sun, L., & Zhang, Y. (2009) · Energy Policy · doi:10.1016/j.enpol.2009.08.008 · Cited by 262
The global relationship between forest productivity and biomass
Keeling, H. C., & Phillips, O. L. (2007) · Global Ecology and Biogeography · doi:10.1111/j.1466-8238.2007.00314.x · Cited by 259
Zhang, B., Zhang, B., Liang, E., Fan, Y.-Z., Wu, X.-F., Pe’er, A., Maxham, A., Gao, H., & Dong, Y.-M. (2011) · The Astrophysical Journal · doi:10.1088/0004-637x/730/2/141 · Cited by 261
Avian influenza H5N1 viral and bird migration networks in Asia
Tian, H., Zhou, S., Dong, L., Boeckel, T. P. V., Cui, Y., Newman, S. H., Takekawa, J. Y., Prosser, D. J., Xiao, X., Wu, Y., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1405216112 · Cited by 266
Concurrent engineering and its consequences
Koufteros, X. A., Vonderembse, M. A., & Doll, W. J. (2000) · Journal of Operations Management · doi:10.1016/s0272-6963(00)00048-6 · Cited by 261
Forecasting tourism demand with multisource big data
Li, H., Hu, M., & Li, G. (2020) · Annals of Tourism Research · doi:10.1016/j.annals.2020.102912 · Cited by 287
Haan, M. N., Miller, J. W., Aiello, A. E., Whitmer, R. A., Jagust, W. J., Mungas, D. M., Allen, L. H., & Green, R. (2007) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/85.2.511 · Cited by 260
Dialogic reading and morphology training in Chinese children: Effects on language and literacy.
Chow, B. W., McBride-Chang, C., Cheung, H., & Chow, C. S.-L. (2008) · Developmental Psychology · doi:10.1037/0012-1649.44.1.233 · Cited by 276
Time Trade-Off Derived EQ-5D Weights for Australia
Viney, R. C., Norman, R., King, M. T., Cronin, P., Street, D. J., Knox, S. A., & Ratcliffe, J. (2011) · Value in Health · doi:10.1016/j.jval.2011.04.009 · Cited by 260
Sensitivity analysis for volcanic source modeling quality assessment and model selection
Cannavò, F. (2012) · Computers & Geosciences · doi:10.1016/j.cageo.2012.03.008 · Cited by 260
Data‐based mechanistic modelling and the rainfall‐flow non‐linearity
Young, P. C., & Beven, K. J. (1994) · Environmetrics · doi:10.1002/env.3170050311 · Cited by 260
Assessment of the quality of meta-analysis in agronomy
Philibert, A., Loyce, C., & Makowski, D. (2011) · Agriculture Ecosystems & Environment · doi:10.1016/j.agee.2011.12.003 · Cited by 272
The effects of crude oil shocks on stock market shifts behaviour: A regime switching approach
Aloui, C., & Jammazi, R. (2009) · Energy Economics · doi:10.1016/j.eneco.2009.03.009 · Cited by 260
A Particle Swarm Optimization to Identifying the ARMAX Model for Short-Term Load Forecasting
Huang, C.-M., Huang, C.-J., & Wang, M. (2005) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2005.846106 · Cited by 260
Pröschold, T. (2001) · Protist · doi:10.1078/1434-4610-00068 · Cited by 261
The Chemistry and Kinetics of Polyethylene Pyrolysis: A Process to Produce Fuels and Chemicals
Zhao, D., Wang, X., Miller, J. B., & Huber, G. W. (2020) · ChemSusChem · doi:10.1002/cssc.201903434 · Cited by 259
Shin, S. H., McDonald, S. E., & Conley, D. (2018) · Addictive Behaviors · doi:10.1016/j.addbeh.2017.11.020 · Cited by 252
Sibling correlations and segregation analysis of age‐related maculopathy: The beaver dam eye study
Heiba, I. M., Elston, R. C., Klein, B. E. K., & Klein, R. (1994) · Genetic Epidemiology · doi:10.1002/gepi.1370110106 · Cited by 259
Statistical monitoring of multivariable dynamic processes with state‐space models
Negiz, A., & Çlinar, A. (1997) · AIChE Journal · doi:10.1002/aic.690430810 · Cited by 259
An investigation of model selection criteria for neural network time series forecasting
Qi, M., & Zhang, G. P. (2001) · European Journal of Operational Research · doi:10.1016/s0377-2217(00)00171-5 · Cited by 258
Li, Z., & Sillanpää, M. J. (2012) · Theoretical and Applied Genetics · doi:10.1007/s00122-012-1892-9 · Cited by 253
Granger causality and information flow in multivariate processes
Blinowska, K. J., Kuś, R., & Kamiński, M. (2004) · Physical Review E · doi:10.1103/physreve.70.050902 · Cited by 258
Linares, J. C., Camarero, J. J., & Carreira, J. A. (2010) · Journal of Ecology · doi:10.1111/j.1365-2745.2010.01645.x · Cited by 258
CFHTLenS revisited: assessing concordance with Planck including astrophysical systematics
Joudaki, S., Blake, C., Heymans, C., Choi, A., Harnois-Déraps, J., Hildebrandt, H., Joachimi, B., Johnson, A., Mead, A., Parkinson, D., et al. (2016) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stw2665 · Cited by 258
Distinct Subtypes of Apathy Revealed by the Apathy Motivation Index
Ang, Y., Lockwood, P. L., Apps, M. A. J., Muhammed, K., & Masud Husain, B. (2017) · PLoS ONE · doi:10.1371/journal.pone.0169938 · Cited by 256
Ghaneh, P., Kleeff, J. H., Halloran, C. M., Raraty, M. G. T., Jackson, R., Melling, J., Jones, O., Palmer, D. H., Cox, T. F., Smith, C., et al. (2017) · Annals of Surgery · doi:10.1097/sla.0000000000002557 · Cited by 258
Fitting and comparing models of phyletic evolution: random walks and beyond
Hunt, G. (2006) · Paleobiology · doi:10.1666/05070.1 · Cited by 258
Lei, X., Kusunose, K., Rao, M. V. M. S., Nishizawa, O. I., & Satoh, T. (2000) · Journal of Geophysical Research Atmospheres · doi:10.1029/1999jb900385 · Cited by 257
HiCNorm: removing biases in Hi-C data via Poisson regression
Hu, M., Deng, K., Selvaraj, S., Qin, Z., Ren, B., & Liu, J. S. (2012) · Bioinform. · doi:10.1093/bioinformatics/bts570 · Cited by 250
Hinde, K., Skibiel, A. L., Foster, A. B., Rosso, L. A. D., Mendoza, S. P., & Capitanio, J. P. (2014) · Behavioral Ecology · doi:10.1093/beheco/aru186 · Cited by 254
Predators or prey? Spatio-temporal discrimination of human-derived risk by brown bears
Ordiz, A., Støen, O., Delibes, M., & Swenson, J. E. (2011) · Oecologia · doi:10.1007/s00442-011-1920-5 · Cited by 257
Making use of population information in evolutionary artificial neural networks
Yao, X., & Liu, Y. (1998) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/3477.678637 · Cited by 257
NHPP software reliability and cost models with testing coverage
Pham, H., & Zhang, X. (2002) · European Journal of Operational Research · doi:10.1016/s0377-2217(02)00181-9 · Cited by 257
Finding approximate POMDP solutions through belief compression
Roy, N., Gordon, G., & Thrun, S. (2005) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1496 · Cited by 254
Physics‐Constrained Machine Learning of Evapotranspiration
Zhao, W. L., Gentine, P., Reichstein, M., Zhang, Y., Zhou, S., Wen, Y., Lin, C., Li, X., & Qiu, G. Y. (2019) · Geophysical Research Letters · doi:10.1029/2019gl085291 · Cited by 315
Heritability of age at menarche in girls from the Fels Longitudinal Study
Towne, B., Czerwinski, S. A., Demerath, E. W., Blangero, J., Roche, A. F., & Siervogel, R. M. (2005) · American Journal of Physical Anthropology · doi:10.1002/ajpa.20106 · Cited by 252
Criticality between Cortical States
Fontenele, A. J., de Vasconcelos, N. A. P., Feliciano, T., Aguiar, L. A. A., Soares-Cunha, C., Coimbra, B., Dalla Porta, L., Ribeiro, S., Rodrigues, A. J., Sousa, N., et al. (2019) · Physical Review Letters · doi:10.1103/physrevlett.122.208101 · Cited by 237
Development and Psychometric Properties of the Sussex-Oxford Compassion Scales (SOCS)
Gu, J., Baer, R., Cavanagh, K., Kuyken, W., & Strauss, C. (2020) · Assessment · doi:10.1177/1073191119860911 · Cited by 238
“…used to indicate model-data fit: the comparative fit index (CFI; Bentler, 1990), root mean square error of approximation (RMSEA; Steiger, 1990), nonnormed fit index (NNFI; Bentler & Bonett, 1980), standardized root mean square residual (SRMR), and Akaike information criterion (AIC; Akaike, 1974).”
Method
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
