Relevant bibliographies by topics / AICc model selection

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'AICc model selection'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "AICc model selection"

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Lee, Shyan-Yuan, and Chih-Ling Tsai. "Model selection for causal models: The global procedure with AICC and AICU." Global Finance Journal 9, no. 2 (1998): 205–23. http://dx.doi.org/10.1016/s1044-0283(98)90004-7.

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Song, Guofeng, Xiaogang Dong, Jiafeng Wu, and You-Gan Wang. "Blockwise AICc for Model Selection in Generalized Linear Models." Environmental Modeling & Assessment 22, no. 6 (2017): 523–33. http://dx.doi.org/10.1007/s10666-017-9552-8.

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Pillay, Khuneswari Gopal, Sya Sya Syahira Muhammad Fitri Avtar, and Mohd Asrul Affendi Abdullah. "Interaction Effects on Prediction of Children Weight at School Entry Using Model Averaging." International Journal of Engineering & Technology 7, no. 4.30 (2018): 205. http://dx.doi.org/10.14419/ijet.v7i4.30.22121.

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Model selection introduce uncertainty to the model building process, therefore model averaging was introduced as an alternative to overcome the problem of underestimate of standards error in model selection. This research also focused on using selection criteria between Corrected Akaike's Information Criteria (AICC) and Bayesian Information Criteria (BIC) as weight for model averaging when involving interaction effects. Mean squared error of prediction (MSE(P)) was used in order to determine the best model for model averaging. Gateshead Millennium Study (GMS) data on children weight used to il
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Zhang, Lanju, Charles Tan, and David LeBlond. "AICc for Calibration Model Selection in Assay Validation." Statistics in Biopharmaceutical Research 9, no. 4 (2017): 379–83. http://dx.doi.org/10.1080/19466315.2017.1388835.

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Olofsen, Erik, and Albert Dahan. "Using Akaike's information theoretic criterion in mixed-effects modeling of pharmacokinetic data: a simulation study." F1000Research 2 (May 28, 2014): 71. http://dx.doi.org/10.12688/f1000research.2-71.v2.

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Akaike's information theoretic criterion for model discrimination (AIC) is often stated to "overfit", i.e., it selects models with a higher dimension than the dimension of the model that generated the data. However, with experimental pharmacokinetic data it may not be possible to identify the correct model, because of the complexity of the processes governing drug disposition. Instead of trying to find the correct model, a more useful objective might be to minimize the prediction error of drug concentrations in subjects with unknown disposition characteristics. In that case, the AIC might be t
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Olofsen, Erik, and Albert Dahan. "Using Akaike's information theoretic criterion in mixed-effects modeling of pharmacokinetic data: a simulation study." F1000Research 2 (July 27, 2015): 71. http://dx.doi.org/10.12688/f1000research.2-71.v3.

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Akaike's information theoretic criterion for model discrimination (AIC) is often stated to "overfit", i.e., it selects models with a higher dimension than the dimension of the model that generated the data. However, with experimental pharmacokinetic data it may not be possible to identify the correct model, because of the complexity of the processes governing drug disposition. Instead of trying to find the correct model, a more useful objective might be to minimize the prediction error of drug concentrations in subjects with unknown disposition characteristics. In that case, the AIC might be t
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Jasieniuk, Marie, Mark L. Taper, Nicole C. Wagner, Robert N. Stougaard, Monica Brelsford, and Bruce D. Maxwell. "Selection of a Barley Yield Model Using Information–Theoretic Criteria." Weed Science 56, no. 4 (2008): 628–36. http://dx.doi.org/10.1614/ws-07-177.1.

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Empirical models of crop–weed competition are integral components of bioeconomic models, which depend on predictions of the impact of weeds on crop yields to make cost-effective weed management recommendations. Selection of the best empirical model for a specific crop–weed system is not straightforward, however. We used information–theoretic criteria to identify the model that best describes barley yield based on data from barley–wild oat competition experiments conducted at three locations in Montana over 2 yr. Each experiment consisted of a complete addition series arranged as a randomized c
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Olofsen, Erik, and Albert Dahan. "Using Akaike’s information theoretic criterion in population analysis: a simulation study." F1000Research 2 (March 4, 2013): 71. http://dx.doi.org/10.12688/f1000research.2-71.v1.

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Akaike’s information-theoretic criterion for model discrimination (AIC) is often stated to “overfit”, i.e., it selects models with a higher dimension than the dimension of the model that generated the data. However, when no fixed-dimensional correct model exists, for example for pharmacokinetic data, AIC, or its bias-corrected version (AICc) might be the selection criterion of choice if the objective is to minimize prediction error. The present simulation study was designed to assess the behavior of AICc when applying it to the analysis of population data, for various degrees of interindividua
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Susko, Edward, and Andrew J. Roger. "On the Use of Information Criteria for Model Selection in Phylogenetics." Molecular Biology and Evolution 37, no. 2 (2019): 549–62. http://dx.doi.org/10.1093/molbev/msz228.

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Abstract The information criteria Akaike information criterion (AIC), AICc, and Bayesian information criterion (BIC) are widely used for model selection in phylogenetics, however, their theoretical justification and performance have not been carefully examined in this setting. Here, we investigate these methods under simple and complex phylogenetic models. We show that AIC can give a biased estimate of its intended target, the expected predictive log likelihood (EPLnL) or, equivalently, expected Kullback–Leibler divergence between the estimated model and the true distribution for the data. Rea
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Agyeman, A. "Estimating the Returns to Schooling: A Comparison of Fixed Effects and Selection Effects Models for Twins." Ghana Journal of Science 61, no. 1 (2020): 15–30. http://dx.doi.org/10.4314/gjs.v61i1.2.

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Strong empirical links exist between the number of years spent schooling and earnings. How­ever, the relationship may be masked due to the effect of unobserved factors that influence both wages and schooling. Two of the main econometric models, namely fixed-effects and se­lection-effects, used to analyse returns to schooling were compared using monozygotic and di­zygotic twins’ datasets in Ghana. The efficiency of the models was assessed based on the stan­dard errors associated with the return to schooling estimates. Goodness of fit measures was used as a basis for comparison of the performanc
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Dissertations / Theses on the topic "AICc model selection"

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Arledge, Christopher S. "Cosmological Model Selection and Akaike’s Criterion." Ohio University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1430478203.

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Carter, Knute Derek. "Best-subset model selection based on multitudinal assessments of likelihood improvements." Diss., University of Iowa, 2013. https://ir.uiowa.edu/etd/5726.

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Given a set of potential explanatory variables, one model selection approach is to select the best model, according to some criterion, from among the collection of models defined by all possible subsets of the explanatory variables. A popular procedure that has been used in this setting is to select the model that results in the smallest value of the Akaike information criterion (AIC). One drawback in using the AIC is that it can lead to the frequent selection of overspecified models. This can be problematic if the researcher wishes to assert, with some level of certainty, the necessity of any
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Ge, Wentao. "Bootstrap-adjusted Quasi-likelihood Information Criteria for Mixed Model Selection." Bowling Green State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu156207676645628.

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De, Aguinaga José Guillermo. "Uncertainty Assessment of Hydrogeological Models Based on Information Theory." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-71814.

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There is a great deal of uncertainty in hydrogeological modeling. Overparametrized models increase uncertainty since the information of the observations is distributed through all of the parameters. The present study proposes a new option to reduce this uncertainty. A way to achieve this goal is to select a model which provides good performance with as few calibrated parameters as possible (parsimonious model) and to calibrate it using many sources of information. Akaike’s Information Criterion (AIC), proposed by Hirotugu Akaike in 1973, is a statistic-probabilistic criterion based on the Info
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Lehmann, Rüdiger, and Michael Lösler. "Multiple Outlier Detection: Hypothesis Tests versus Model Selection by Information Criteria." Hochschule für Technik und Wirtschaft Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:520-qucosa-225770.

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The detection of multiple outliers can be interpreted as a model selection problem. Models that can be selected are the null model, which indicates an outlier free set of observations, or a class of alternative models, which contain a set of additional bias parameters. A common way to select the right model is by using a statistical hypothesis test. In geodesy data snooping is most popular. Another approach arises from information theory. Here, the Akaike information criterion (AIC) is used to select an appropriate model for a given set of observations. The AIC is based on the Kullback-Leibler
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Lehmann, Rüdiger, and Michael Lösler. "Multiple Outlier Detection: Hypothesis Tests versus Model Selection by Information Criteria." Hochschule für Technik und Wirtschaft Dresden, 2016. https://htw-dresden.qucosa.de/id/qucosa%3A23307.

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The detection of multiple outliers can be interpreted as a model selection problem. Models that can be selected are the null model, which indicates an outlier free set of observations, or a class of alternative models, which contain a set of additional bias parameters. A common way to select the right model is by using a statistical hypothesis test. In geodesy data snooping is most popular. Another approach arises from information theory. Here, the Akaike information criterion (AIC) is used to select an appropriate model for a given set of observations. The AIC is based on the Kullback-Leibler
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Wang, Jun. "Selecting the Best Linear Mixed Model Using Predictive Approaches." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1697.pdf.

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Sparks, JonDavid. "Model selection criteria in the presence of missing data based on the Kullback-Leibler discrepancy." Diss., University of Iowa, 2009. https://ir.uiowa.edu/etd/439.

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An important challenge in statistical modeling involves determining an appropriate structural form for a model to be used in making inferences and predictions. Missing data is a very common occurrence in most research settings and can easily complicate the model selection problem. Many useful procedures have been developed to estimate parameters and standard errors in the presence of missing data;however, few methods exist for determining the actual structural form of a modelwhen the data is incomplete. In this dissertation, we propose model selection criteria based on the Kullback-Leiber disc
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Harrell, Leigh Michelle. "Accuracy of Global Fit Indices as Indictors of Multidimensionality in Multidimensional Rasch Analysis." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/29656.

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Most research on confirmatory factor analysis using global fit indices (AIC, BIC, AICc, and CAIC) has been in the structural equation modeling framework. Little research has been done concerning application of these indices to item response models, especially within the framework of multidimensional Rasch analysis. The results of two simulations studies that investigated how sample size, between-dimension correlation, and test length affect the accuracy of these indices in model recovery using a multidimensional Rasch analysis are described in this dissertation. The first study analyzed dichot
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Gomez, Elisa Valderas. "Performance of the Kenward-Project when the Covariance Structure is Selected Using AIC and BIC." Diss., CLICK HERE for online access, 2004. http://contentdm.lib.byu.edu/ETD/image/etd431.pdf.

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Book chapters on the topic "AICc model selection"

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Khoi, Bui Huy. "Factors Influencing on University Reputation: Model Selection by AIC." In Data Science for Financial Econometrics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48853-6_13.

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Hurvich, Clifford M., and Chih-Ling Tsai. "Autoregressive Model Selection in Small Samples Using a Bias-Corrected Version of AIC." In Proceedings of the First US/Japan Conference on the Frontiers of Statistical Modeling: An Informational Approach. Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-0866-9_9.

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Salihu, Lukman, Adekunbi E. Adedayo, Babajide Jelili Olalekan, et al. "Evaluation of Selected Reaeration Models for Water Quality Management." In Advances in Environmental Engineering and Green Technologies. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1871-7.ch012.

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In this chapter, a new proposed model was compared with selected standard models and evaluated statistically (model of selection criterion [MSC] and Akaike information criterion [AIC]). Suspended concentration and calculated reaeration rate were used to predict concentration of EPs removable by the aeration and self-purification of the stream. The study revealed that MSC for the new proposed model were 0.75, - 0.44, - 0.32, - 0.45, and - 0.45 respectively. AIC for both dry and wet seasons were 11.85, 42.17, 41.37, 42.17, and 42.25 for the new proposed model, respectively. It was concluded the proposed model performed better than some of the standard models.
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"Appendix E: Model Selection Criterion: AIC and BIC." In The Basics of Financial Econometrics. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118856406.app5.

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Chakrabarti, Arijit, and Jayanta K. Ghosh. "AIC, BIC and Recent Advances in Model Selection." In Philosophy of Statistics. Elsevier, 2011. http://dx.doi.org/10.1016/b978-0-444-51862-0.50018-6.

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Lukman, Salihu, Isaiah Adesola Oke, and Afolabi M. Asani. "A Comprehensive Update and Performance Evaluation of Friction Factor Formulae." In Encyclopedia of Information Science and Technology, Fifth Edition. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3479-3.ch085.

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In this paper, explicit friction factor formulae (Fff), which is a function of Reynolds number (Re) and relative roughness (Rr) were updated and evaluated. Fff were obtained from archive and conduct performance evaluation. Performance evaluation of the Fff were conducted using relative error; model of selection (MSC) and the Akaike Information Criterion (AIC) using Colebrook-White's friction factors as the reference Ff. The study revealed that there are 47 Fff in use. The growth of the Fff can be grouped into four subgroups based on the pattern and into three subgroups based on number of authors and into three subgroups, based on the accuracy. The growth rates were combinations of linear and exponential based on the pattern. The study revealed that Fff provided the lowest relative error of less than 1.00%, the highest MSC of greater than 6.64 and the lowest AIC of less than -34324.17. The study concluded that the recent and third generation Fff are the best and using Microsoft Excel Solver for calculating Ff in the pipe flow systems is a good tool for engineers.
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Climescu-Haulica, Adriana, and Michelle Quirk. "Nonlinear Stochastic Differential Equations Method for Reverse Engineering of Gene Regulatory Network." In Handbook of Research on Computational Methodologies in Gene Regulatory Networks. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-685-3.ch009.

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In this chapter, we present a method to infer the structure of the gene regulatory network that takes in account both the kinetic molecular interactions and the randomness of data. The dynamics of the gene expression level are fitted via a nonlinear stochastic differential equation (SDE) model. The drift term of the equation contains the transcription rate related to the architecture of the local regulatory network. The statistical analysis of data combines maximum likelihood principle with Akaike Information Criteria (AIC) through a forward selection Strategy to yield a set of specific regulators and their contribution. Tested with expression data concerning the cell cycle for S. Cerevisiae and embryogenesis for the D. melanogaster, this method provides a framework for the reverse engineering of various gene regulatory networks.
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Conference papers on the topic "AICc model selection"

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Karimi, Mahmood. "Finite Sample AIC for Autoregressive Model Order Selection." In 2007 IEEE International Conference on Signal Processing and Communications. IEEE, 2007. http://dx.doi.org/10.1109/icspc.2007.4728545.

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Kamel, Ahmed H., Ali S. Shaqlaih, and Arslan Rozyyev. "A Comparative Analysis and Evaluation of Model Selection Criteria." In ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/imece2017-70152.

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The ongoing research for model choice and selection has generated a plethora of approaches. With such a wealth of methods, it can be difficult for a researcher to know what model selection approach is the proper way to proceed to select the appropriate model for prediction. The authors present an evaluation of various model selection criteria from decision-theoretic perspective using experimental data to define and recommend a criterion to select the best model. In this analysis, six of the most common selection criteria, nineteen friction factor correlations, and eight sets of experimental da
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Oprisescu, Serban, Monica Dumitrescu, and Vasile Buzuloiu. "On Model Selection for an Urban Area, by the AIC Criterion." In 2007 International Symposium on Signals, Circuits and Systems. IEEE, 2007. http://dx.doi.org/10.1109/isscs.2007.4292785.

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Rodríguez, Carlos C. "The ABC of Model Selection: AIC, BIC and the New CIC." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 25th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2005. http://dx.doi.org/10.1063/1.2149782.

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Asogawa, S., and N. Akamatsu. "An efficient algorithm for clustering data and best model selection using AIC." In Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94). IEEE, 1994. http://dx.doi.org/10.1109/icnn.1994.374222.

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Abdel Rafea, Mahmoud, and Cristhian Criado. "Autonomous Inflow Control Device Pilot Application for Extra Heavy Oil Field." In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/omae2018-77223.

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Autonomous Inflow Control Device (AICD) completion was successfully designed and applied in a horizontal well drilled in a deep reservoir in an extra heavy oil field located in South America where the average total depth of the targeted reservoir is around ten thousands feet and the in-situ viscosity is 600 cps while API Gravity is ranged between 8.5–9.5. Due to geological and petro-physical features in this area which turns into permeability variations and thick transition zone across the reservoir, a horizontal well of 2500 feet length was drilled and completed with a standalone screen along
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Mariia, Matskevichus, and Gladilin Peter. "Model Selection Approach for Time Series Forecasting." In 2019 IEEE 13th International Conference on Application of Information and Communication Technologies (AICT). IEEE, 2019. http://dx.doi.org/10.1109/aict47866.2019.8981768.

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Geng, Li-zhong, and Hui-bo Jia. "Smart Intrusion Detection Method Using Negative Selection Algorithm Based on Maximum Entropy Model." In 2009 International Conference on Artificial Intelligence and Computational Intelligence. IEEE, 2009. http://dx.doi.org/10.1109/aici.2009.92.

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Mohd Hezri Marzaki, Mazidah Tajjudin, Ramli Adnan, Mohd Hezri Fazalul Rahiman, and Mohd Hafiz A. Jalil. "Comparison of different model structure selection using R2, MDL and AIC criterion." In 2013 IEEE 4th Control and System Graduate Research Colloquium (ICSGRC). IEEE, 2013. http://dx.doi.org/10.1109/icsgrc.2013.6653280.

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Alguliyev, Rasim, Ramiz Aliguliyev, and Nijat Isazade. "A sentence selection model and HLO algorithm for extractive text summarization." In 2016 IEEE 10th International Conference on Application of Information and Communication Technologies (AICT). IEEE, 2016. http://dx.doi.org/10.1109/icaict.2016.7991686.

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