Log in

Relevant bibliographies by topics / Bayesian Information Criterion / Journal articles

To see the other types of publications on this topic, follow the link: Bayesian Information Criterion.

Journal articles on the topic 'Bayesian Information Criterion'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 June 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Bayesian Information Criterion.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Nguefack-Tsague, Georges, and Ingo Bulla. "A Focused Bayesian Information Criterion." Advances in Statistics 2014 (October 14, 2014): 1–8. http://dx.doi.org/10.1155/2014/504325.

Full text

Abstract:

Myriads of model selection criteria (Bayesian and frequentist) have been proposed in the literature aiming at selecting a single model regardless of its intended use. An honorable exception in the frequentist perspective is the “focused information criterion” (FIC) aiming at selecting a model based on the parameter of interest (focus). This paper takes the same view in the Bayesian context; that is, a model may be good for one estimand but bad for another. The proposed method exploits the Bayesian model averaging (BMA) machinery to obtain a new criterion, the focused Bayesian model averaging (FoBMA), for which the best model is the one whose estimate is closest to the BMA estimate. In particular, for two models, this criterion reduces to the classical Bayesian model selection scheme of choosing the model with the highest posterior probability. The new method is applied in linear regression, logistic regression, and survival analysis. This criterion is specially important in epidemiological studies in which the objective is often to determine a risk factor (focus) for a disease, adjusting for potential confounding factors.

APA, Harvard, Vancouver, ISO, and other styles

2

Bayarri, M. J., James O. Berger, Woncheol Jang, Surajit Ray, Luis R. Pericchi, and Ingmar Visser. "Prior-based Bayesian information criterion." Statistical Theory and Related Fields 3, no. 1 (2019): 2–13. http://dx.doi.org/10.1080/24754269.2019.1582126.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Zhou, Shouhao. "Posterior Averaging Information Criterion." Entropy 25, no. 3 (2023): 468. http://dx.doi.org/10.3390/e25030468.

Full text

Abstract:

We propose a new model selection method, named the posterior averaging information criterion, for Bayesian model assessment to minimize the risk of predicting independent future observations. The theoretical foundation is built on the Kullback–Leibler divergence to quantify the similarity between the proposed candidate model and the underlying true model. From a Bayesian perspective, our method evaluates the candidate models over the entire posterior distribution in terms of predicting a future independent observation. Without assuming that the true distribution is contained in the candidate models, the new criterion is developed by correcting the asymptotic bias of the posterior mean of the in-sample log-likelihood against out-of-sample log-likelihood, and can be generally applied even for Bayesian models with degenerate non-informative priors. Simulations in both normal and binomial settings demonstrate superior small sample performance.

APA, Harvard, Vancouver, ISO, and other styles

4

Li, Wentian, and Dale R. Nyholt. "Marker Selection by Akaike Information Criterion and Bayesian Information Criterion." Genetic Epidemiology 21, S1 (2001): S272—S277. http://dx.doi.org/10.1002/gepi.2001.21.s1.s272.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Hirose, Kei, Shuichi Kawano, and Sadanori Konishi. "BAYESIAN FACTOR ANALYSIS AND INFORMATION CRITERION." Bulletin of informatics and cybernetics 40 (December 2008): 75–87. http://dx.doi.org/10.5109/18995.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Wang, L. "Wilcoxon-type generalized Bayesian information criterion." Biometrika 96, no. 1 (2009): 163–73. http://dx.doi.org/10.1093/biomet/asn060.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Calderón Rivera, Dayana Soret, Claudia Fernanda Navarrete López, and José Luis Díaz Arévalo. "Ajustes de distribuciones probabilísticas para la variable temperatura media multianual para el departamento de Boyacá (Colombia)." Ingeniería y Región 14, no. 2 (2016): 113. http://dx.doi.org/10.25054/22161325.698.

Full text

Abstract:

En este trabajo se presenta un estudio acerca de la selección de la mejor distribución probabilística para la variable media multianual de la temperatura en el departamento de Boyacá (Colombia), como base para futuras estimaciones y proyecciones de la variable en condiciones de incertidumbre. Se seleccionaron las distribuciones Normal, Gamma, Weibull y LogNormal para ajustar los datos; y para encontrar cual distribución ajusta mejor los datos se utilizaron los criterios de información basados en la máxima verosimilitud de Akaike (Akaike Information Criterion) y Bayesiano (Bayesian Information Criteron). Se muestran los resultados tanto en forma tabular como gráfica, así como un plano de las funciones de distribución probabilísticas más representativas en el área de estudio. Como resultado se obtiene que en general la distribución que mejor se ajusta es la Weibull.

APA, Harvard, Vancouver, ISO, and other styles

8

Volinsky, Chris T., and Adrian E. Raftery. "Bayesian Information Criterion for Censored Survival Models." Biometrics 56, no. 1 (2000): 256–62. http://dx.doi.org/10.1111/j.0006-341x.2000.00256.x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Drton, Mathias, and Martyn Plummer. "A Bayesian information criterion for singular models." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 79, no. 2 (2017): 323–80. http://dx.doi.org/10.1111/rssb.12187.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Lan, Wei, Hansheng Wang, and Chih-Ling Tsai. "A Bayesian information criterion for portfolio selection." Computational Statistics & Data Analysis 56, no. 1 (2012): 88–99. http://dx.doi.org/10.1016/j.csda.2011.06.012.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Ando, Tomohiro. "Bayesian Model Averaging and Bayesian Predictive Information Criterion for Model Selection." JOURNAL OF THE JAPAN STATISTICAL SOCIETY 38, no. 2 (2008): 243–57. http://dx.doi.org/10.14490/jjss.38.243.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Zhao, Yuan, Xiaochuan Ke, Lei Huang, and Yuhang Xiao. "On Cycle-Period Estimation: A Bayesian Information Criterion." IEEE Transactions on Vehicular Technology 70, no. 4 (2021): 3949–54. http://dx.doi.org/10.1109/tvt.2021.3065380.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Albahli, Saleh, and Ghulam Nabi Ahmad Hassan Yar. "Defect Prediction Using Akaike and Bayesian Information Criterion." Computer Systems Science and Engineering 41, no. 3 (2022): 1117–27. http://dx.doi.org/10.32604/csse.2022.021750.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Zhu, Hongtu, Joseph G. Ibrahim, and Qingxia Chen. "Bayesian case-deletion model complexity and information criterion." Statistics and Its Interface 7, no. 4 (2014): 531–42. http://dx.doi.org/10.4310/sii.2014.v7.n4.a9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Chen, Jiahua, and Zeny Feng. "A discussion of ‘prior-based Bayesian information criterion’." Statistical Theory and Related Fields 3, no. 1 (2019): 14–16. http://dx.doi.org/10.1080/24754269.2019.1583628.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Liu, Sifan, and Dongchu Sun. "Discussion of ‘Prior-based Bayesian Information Criterion (PBIC)’." Statistical Theory and Related Fields 3, no. 1 (2019): 24–25. http://dx.doi.org/10.1080/24754269.2019.1611142.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Clarke, Bertrand S. "Discussion of ‘Prior-based Bayesian Information Criterion (PBIC)’." Statistical Theory and Related Fields 3, no. 1 (2019): 26–29. http://dx.doi.org/10.1080/24754269.2019.1611143.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Hannig, Jan. "Discussion of ‘Prior-based Bayesian information criterion (PBIC)’." Statistical Theory and Related Fields 3, no. 1 (2019): 30–31. http://dx.doi.org/10.1080/24754269.2019.1611144.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Mehrjou, Arash, Reshad Hosseini, and Babak Nadjar Araabi. "Improved Bayesian information criterion for mixture model selection." Pattern Recognition Letters 69 (January 2016): 22–27. http://dx.doi.org/10.1016/j.patrec.2015.10.004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Friel, N., J. P. McKeone, C. J. Oates, and A. N. Pettitt. "Investigation of the widely applicable Bayesian information criterion." Statistics and Computing 27, no. 3 (2016): 833–44. http://dx.doi.org/10.1007/s11222-016-9657-y.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Hu, Jianwei, Hong Qin, Ting Yan, and Yunpeng Zhao. "Corrected Bayesian Information Criterion for Stochastic Block Models." Journal of the American Statistical Association 115, no. 532 (2019): 1771–83. http://dx.doi.org/10.1080/01621459.2019.1637744.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Jones, Richard H. "Bayesian information criterion for longitudinal and clustered data." Statistics in Medicine 30, no. 25 (2011): 3050–56. http://dx.doi.org/10.1002/sim.4323.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Neath, Andrew A., and Joseph E. Cavanaugh. "The Bayesian information criterion: background, derivation, and applications." Wiley Interdisciplinary Reviews: Computational Statistics 4, no. 2 (2011): 199–203. http://dx.doi.org/10.1002/wics.199.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Sclove, Stanley L. "Using Model Selection Criteria to Choose the Number of Principal Components." Journal of Statistical Theory and Applications 20, no. 3 (2021): 450–61. http://dx.doi.org/10.1007/s44199-021-00002-4.

Full text

Abstract:

AbstractThe use of information criteria, especially AIC (Akaike’s information criterion) and BIC (Bayesian information criterion), for choosing an adequate number of principal components is illustrated.

APA, Harvard, Vancouver, ISO, and other styles

25

Peiro, Ignacio Garcia. "How to Model in Zoology: Basic Concepts and Explanations." International Journal of Zoology and Animal Biology 7, no. 1 (2024): 1–2. http://dx.doi.org/10.23880/izab-16000556.

Full text

Abstract:

The information criteria used for biologists-zoologists when comparing statistical models are summarized. The three criteria summarizes the quality (Akaike Information Criterion, AIC), complexity (Bayesian Information Criterion, BIC) and finally the reliability, fiability or accuracy (Deviance Information Criterion, DIC) of a model.

APA, Harvard, Vancouver, ISO, and other styles

26

Choe, John Y., Yen-Chi Chen, and Nick Terry. "Information criterion for approximation of unnormalized densities." PLOS ONE 20, no. 3 (2025): e0317430. https://doi.org/10.1371/journal.pone.0317430.

Full text

Abstract:

This paper considers the problem of approximating an unknown density when it can be evaluated up to a normalizing constant at a finite number of points. This density approximation problem is ubiquitous in statistics, such as approximating a posterior density for Bayesian inference and estimating an optimal density for importance sampling. We consider a parametric approximation approach and cast it as a model selection problem to find the best model in pre-specified distribution families (e.g., select the best number of Gaussian mixture components and their parameters). This problem cannot be addressed with traditional approaches that maximize the (marginal) likelihood of a model, for example, using the Akaike information criterion (AIC) or Bayesian information criterion (BIC). We instead aim to minimize the cross-entropy that gauges the deviation of a parametric model from the target density. We propose a novel information criterion called the cross-entropy information criterion (CIC) and prove that the CIC is an asymptotically unbiased estimator of the cross-entropy (up to a multiplicative constant) under some regularity conditions. We propose an iterative method to approximate the target density by minimizing the CIC. We demonstrate how the proposed method selects a parametric model that well approximates the target density through multiple numerical studies in the Supporting Information.

APA, Harvard, Vancouver, ISO, and other styles

27

Zou, Tianji, Kai Liu, Wenbo Wu, Ke Wang, and Congmin Lv. "Uncertainly Analysis of Prior Distribution in Accelerated Degradation Testing Bayesian Evaluation Method Based on Deviance Information Criterion." Symmetry 16, no. 2 (2024): 160. http://dx.doi.org/10.3390/sym16020160.

Full text

Abstract:

The accelerated degradation testing (ADT) Bayesian evaluation method comprehensively utilizes product degradation data under accelerated stress levels collected over a short period of time and multiple sources of prior information, such as historical information, similar product information, simulation information, etc., to conduct life and reliability evaluation. Through the prior distribution, prior information affects the ADT Bayesian evaluation results ultimately. However, different evaluators may obtain different prior distributions based on the same prior information due to varying experiences or rules, which may lead to differences in the ADT Bayesian evaluation results. Therefore, it is necessary to analyze and study the impact of prior distribution uncertainty on the ADT Bayesian evaluation results while also finding criteria to judge the quality of prior distributions. This paper focuses on the ADT Bayesian evaluation method based on the Wiener process and the Arrhenius relation, studying the influence of different prior distributions on the robustness of ADT Bayesian evaluation results. Additionally, based on the deviance information criterion (DIC), a criterion for selecting prior distributions in the ADT Bayesian evaluation method is proposed. Through carrying out uncertainty analysis of prior distribution in the ADT Bayesian evaluation method, a theoretical system and framework for analyzing prior uncertainty in ADT Bayesian evaluation based on DIC are established, providing a better foundation for the practical application of the ADT Bayesian evaluation method in engineering.

APA, Harvard, Vancouver, ISO, and other styles

28

Ke, Xiaochuan, Yuan Zhao, and Lei Huang. "On Accurate Source Enumeration: A New Bayesian Information Criterion." IEEE Transactions on Signal Processing 69 (2021): 1012–27. http://dx.doi.org/10.1109/tsp.2021.3052052.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Shao, Jun, and Sheng Zhang. "A discussion of ‘prior-based Bayesian information criterion (PBIC)’." Statistical Theory and Related Fields 3, no. 1 (2019): 19–21. http://dx.doi.org/10.1080/24754269.2019.1583086.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Jiang, Jiming, and Thuan Nguyen. "A discussion of prior-based Bayesian information criterion (PBIC)." Statistical Theory and Related Fields 3, no. 1 (2019): 17–18. http://dx.doi.org/10.1080/24754269.2019.1583631.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Shriner, Daniel, and Nengjun Yi. "Deviance information criterion (DIC) in Bayesian multiple QTL mapping." Computational Statistics & Data Analysis 53, no. 5 (2009): 1850–60. http://dx.doi.org/10.1016/j.csda.2008.01.016.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Shaik, Thanveer, Xiaohui Tao, Lin Li, et al. "Clustered FedStack: Intermediate Global Models with Bayesian Information Criterion." Pattern Recognition Letters 177 (January 2024): 121–27. http://dx.doi.org/10.1016/j.patrec.2023.12.004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Yulistiani, Selma, and Suliadi Suliadi. "Deteksi Pencilan pada Model ARIMA dengan Bayesian Information Criterion (BIC) Termodifikasi." STATISTIKA: Journal of Theoretical Statistics and Its Applications 19, no. 1 (2019): 29–37. http://dx.doi.org/10.29313/jstat.v19i1.4740.

Full text

Abstract:

Time series data may be affected by special events or circumstances such as promotions, natural disasters, etc. These events can lead to inconsistent observations in the series called outliers. Because outliers can make invalid conclusions, it is important to carry out procedures in detecting outlier effects. In outlier detection there is one type of outlier, namely additive outlier (AO). The process of detecting additive outliers in the ARIMA model can be said as a model selection problem, where the candidate model assumes additive outliers at a certain time. In the selection of models there are criteria that must be considered in order to produce the best model. The good criteria for models selection can use the Bayesian Information Criterion (BIC) derived by Schwarz (1978). Galeano and Pena (2011) proposed a modified Bayesian Information Criterion for model selection and detect potential outliers. The modified Bayesian Information Criterion for outlier detection will be applied to the data OutStanding Loan PT.Pegadaian Cimahi year 2013-2017. So that the best model is obtained that the model with adding 2 potential outliers with the ARIMA model (1.0,0), that outliers at observations 48, and 58 because it has a minimum BICUP value of 1064.95650.

APA, Harvard, Vancouver, ISO, and other styles

34

Wang, Yanjun, and Qun Liu. "Comparison of Akaike information criterion (AIC) and Bayesian information criterion (BIC) in selection of stock–recruitment relationships." Fisheries Research 77, no. 2 (2006): 220–25. http://dx.doi.org/10.1016/j.fishres.2005.08.011.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Veerkamp, Wim J. J., and Martijn P. F. Berger. "Some New Item Selection Criteria for Adaptive Testing." Journal of Educational and Behavioral Statistics 22, no. 2 (1997): 203–26. http://dx.doi.org/10.3102/10769986022002203.

Full text

Abstract:

In this study some alternative item selection criteria for adaptive testing are proposed. These criteria take into account the uncertainty of the ability estimates. A general weighted information criterion of which the usual maximum information criterion and the proposed alternative criteria are special cases is suggested. A small simulation study was conducted to compare the different criteria. The results showed that the likelihood weighted information criterion is a good alternative to the maximum information criterion. Another good alternative is a maximum information criterion with the maximum likelihood estimator of ability replaced by the Bayesian expected a posteriori estimator.

APA, Harvard, Vancouver, ISO, and other styles

36

Ando, T. "Bayesian predictive information criterion for the evaluation of hierarchical Bayesian and empirical Bayes models." Biometrika 94, no. 2 (2007): 443–58. http://dx.doi.org/10.1093/biomet/asm017.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

R.M., Harindranath, and Jayanth Jacob. "Bayesian structural equation modelling tutorial for novice management researchers." Management Research Review 41, no. 11 (2018): 1254–70. http://dx.doi.org/10.1108/mrr-11-2017-0377.

Full text

Abstract:

Purpose This paper aims to popularize the Bayesian methods among novice management researchers. The paper interprets the results of Bayesian method of confirmatory factor analysis (CFA), structural equation modelling (SEM), mediation and moderation analysis, with the intention that the novice researchers will apply this method in their research. The paper made an attempt in discussing various complex mathematical concepts such as Markov Chain Monte Carlo, Bayes factor, Bayesian information criterion and deviance information criterion (DIC), etc. in a lucid manner. Design/methodology/approach Data collected from 172 pharmaceutical sales representatives were used. The study will help the management researchers to perform Bayesian CFA, Bayesian SEM, Bayesian moderation analysis and Bayesian mediation analysis using SPSS AMOS software. Findings The interpretation of the results of Bayesian CFA, Bayesian SEM and Bayesian mediation analysis were discussed. Practical implications The management scholars are non-statisticians and are not much aware of the benefits offered by Bayesian methods. Hitherto, the management scholars use predominantly traditional SEM in validating their models empirically, and this study will give an exposure to “Bayesian statistics” that has practical advantages. Originality/value This is one paper, which discusses the following four concepts: Bayesian method of CFA, SEM, mediation and moderation analysis.

APA, Harvard, Vancouver, ISO, and other styles

38

Gao, Zhanzhongyu, Xun Xiao, Yi-Ping Fang, Jing Rao, and Huadong Mo. "A Selective Review on Information Criteria in Multiple Change Point Detection." Entropy 26, no. 1 (2024): 50. http://dx.doi.org/10.3390/e26010050.

Full text

Abstract:

Change points indicate significant shifts in the statistical properties in data streams at some time points. Detecting change points efficiently and effectively are essential for us to understand the underlying data-generating mechanism in modern data streams with versatile parameter-varying patterns. However, it becomes a highly challenging problem to locate multiple change points in the noisy data. Although the Bayesian information criterion has been proven to be an effective way of selecting multiple change points in an asymptotical sense, its finite sample performance could be deficient. In this article, we have reviewed a list of information criterion-based methods for multiple change point detection, including Akaike information criterion, Bayesian information criterion, minimum description length, and their variants, with the emphasis on their practical applications. Simulation studies are conducted to investigate the actual performance of different information criteria in detecting multiple change points with possible model mis-specification for the practitioners. A case study on the SCADA signals of wind turbines is conducted to demonstrate the actual change point detection power of different information criteria. Finally, some key challenges in the development and application of multiple change point detection are presented for future research work.

APA, Harvard, Vancouver, ISO, and other styles

39

Hussein, Amjad, and Safaa K. Kadhem. "Spatial mixture modeling for analyzing a rainfall pattern: A case study in Ireland." Open Engineering 12, no. 1 (2022): 204–14. http://dx.doi.org/10.1515/eng-2022-0024.

Full text

Abstract:

Abstract This study investigates the spatial heterogeneity in the maximum monthly rainfall amounts reported by stations in Ireland from January 2018 to December 2020. The heterogeneity is modeled by the Bayesian normal mixture model with different ranks. The selection of the best model or the degree of heterogeneity is implemented using four criteria which are the modified Akaike information criterion, the modified Bayesian information criterion, the deviance information criterion, and the widely applicable information criterion. The estimation and model selection process is implemented using the Gibbs sampling. The results show that the maximum monthly rainfall amounts are accommodated in two and three components. The goodness of fit for the selected models is checked using the graphical plots including the probability density function and cumulative distribution function. This article also contributes via the spatial determination of return level or rainfall amounts at risk with different return periods using the prediction intervals constructed from the posterior predictive distribution.

APA, Harvard, Vancouver, ISO, and other styles

40

Wang, Hang, and David Miller. "Improved Parsimonious Topic Modeling Based on the Bayesian Information Criterion." Entropy 22, no. 3 (2020): 326. http://dx.doi.org/10.3390/e22030326.

Full text

Abstract:

In a previous work, a parsimonious topic model (PTM) was proposed for text corpora. In that work, unlike LDA, the modeling determined a subset of salient words for each topic, with topic-specific probabilities, with the rest of the words in the dictionary explained by a universal shared model. Further, in LDA all topics are in principle present in every document. In contrast, PTM gives sparse topic representation, determining the (small) subset of relevant topics for each document. A customized Bayesian information criterion (BIC) was derived, balancing model complexity and goodness of fit, with the BIC minimized to jointly determine the entire model—the topic-specific words, document-specific topics, all model parameter values, and the total number of topics—in a wholly unsupervised fashion. In the present work, several important modeling and algorithm (parameter learning) extensions of PTM are proposed. First, we modify the BIC objective function using a lossless coding scheme with low modeling cost for describing words that are non-salient for all topics—such words are essentially identified as wholly noisy/uninformative. This approach increases the PTM’s model sparsity, which also allows model selection of more topics and with lower BIC cost than the original PTM. Second, in the original PTM model learning strategy, word switches were updated sequentially, which is myopic and susceptible to finding poor locally optimal solutions. Here, instead, we jointly optimize all the switches that correspond to the same word (across topics). This approach jointly optimizes many more parameters at each step than the original PTM, which in principle should be less susceptible to finding poor local minima. Results on several document data sets show that our proposed method outperformed the original PTM model with respect to multiple performance measures, and gave a sparser topic model representation than the original PTM.

APA, Harvard, Vancouver, ISO, and other styles

41

Tamura, Y., T. Sato, M. Ooe, and M. Ishiguro. "A procedure for tidal analysis with a Bayesian information criterion." Geophysical Journal International 104, no. 3 (2007): 507–16. http://dx.doi.org/10.1111/j.1365-246x.1991.tb05697.x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Lu, Zhihua, and Abdelhak M. Zoubir. "Generalized Bayesian Information Criterion for Source Enumeration in Array Processing." IEEE Transactions on Signal Processing 61, no. 6 (2013): 1470–80. http://dx.doi.org/10.1109/tsp.2012.2232661.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

WEAKLIEM, DAVID L. "A Critique of the Bayesian Information Criterion for Model Selection." Sociological Methods & Research 27, no. 3 (1999): 359–97. http://dx.doi.org/10.1177/0049124199027003002.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Hannart, Alexis, and Philippe Naveau. "An Improved Bayesian Information Criterion for Multiple Change-Point Models." Technometrics 54, no. 3 (2012): 256–68. http://dx.doi.org/10.1080/00401706.2012.694780.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

WADA, Yasuhiko, Akira TAKEBE, Shigeo MATSUMOTO, and Nobuhisa KASHIWAGI. "Model Selection for Sire Evaluation by Akaike's Bayesian Information Criterion." Nihon Chikusan Gakkaiho 64, no. 4 (1993): 371–78. http://dx.doi.org/10.2508/chikusan.64.371.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Lee, Eun Ryung, Hohsuk Noh, and Byeong U. Park. "Model Selection via Bayesian Information Criterion for Quantile Regression Models." Journal of the American Statistical Association 109, no. 505 (2014): 216–29. http://dx.doi.org/10.1080/01621459.2013.836975.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Lin, Da, Xin Xu, and Fangling Pu. "Bayesian Information Criterion Based Feature Filtering for the Fusion of Multiple Features in High-Spatial-Resolution Satellite Scene Classification." Journal of Sensors 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/142612.

Full text

Abstract:

This paper presents a novel classification method for high-spatial-resolution satellite scene classification introducing Bayesian information criterion (BIC)-based feature filtering process to further eliminate opaque and redundant information between multiple features. Firstly, two diverse and complementary feature descriptors are extracted to characterize the satellite scene. Then, sparse canonical correlation analysis (SCCA) with penalty function is employed to fuse the extracted feature descriptors and remove the ambiguities and redundancies between them simultaneously. After that, a two-phase Bayesian information criterion (BIC)-based feature filtering process is designed to further filter out redundant information. In the first phase, we gradually impose a constraint via an iterative process to set a constraint on the loadings for averting sparse correlation descending below to a lower confidence limit of the approximated canonical correlation. In the second phase, Bayesian information criterion (BIC) is utilized to conduct the feature filtering which sets the smallest loading in absolute value to zero in each iteration for all features. Lastly, a support vector machine with pyramid match kernel is applied to obtain the final result. Experimental results on high-spatial-resolution satellite scenes demonstrate that the suggested approach achieves satisfactory performance in classification accuracy.

APA, Harvard, Vancouver, ISO, and other styles

48

Kasali, Jimoh, and Adediwura Alaba Adeyemi. "Model-Data Fit using Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and The Sample-Size-Adjusted BIC." Square : Journal of Mathematics and Mathematics Education 4, no. 1 (2022): 43–51. http://dx.doi.org/10.21580/square.2022.4.1.11297.

Full text

Abstract:

The study determined if the 1PL, 2PL, 3PL and 4PL item response theory models best fit the data from the 2016 NECO Mathematics objective tests. Ex-post facto design was adopted for the study. The population for the study comprised 1,022,474 candidates who enrolled and sat for June/July SSCE 2016 NECO Mathematics Examination. The sample comprised 276,338 candidates who sat for the examination in three purposively Geo political zones in Nigeria (i.e., S/West, S/East and N/West). The research instruments used for the study were Optical Marks Record Sheets for the National Examination Council (NECO) June/July 2016 SSCE Mathematics objectives items. The responses of the tests were scored dichotomously. Data collected were analyzed using 2loglikelihood chi-square. The results of the likelihood ratio test revealed that 2PL fitted the data better than 1PL was statistically significant (χ2 (59) = 820636.1, p 0.05); the 2PL model fitted the data better than the 1PL model; 3PL model fitted the data better than the 2PL model and the result showed that the 4PL model fitted the data better than the 3PL model and the Likelihood ratio test that 4PL model fitted the data better than 3PL model was statistically significant, (χ2(60)=216159.2, p0.05). The study concluded that four-parameter logistic model fitted the 2016 NECO Mathematics test items.Keywords: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), one parameter model, two parameter model, three parameter model, four parameter model.

APA, Harvard, Vancouver, ISO, and other styles

49

Badshah, Waqar, and Mehmet Bulut. "Model Selection Procedures in Bounds Test of Cointegration: Theoretical Comparison and Empirical Evidence." Economies 8, no. 2 (2020): 49. http://dx.doi.org/10.3390/economies8020049.

Full text

Abstract:

Only unstructured single-path model selection techniques, i.e., Information Criteria, are used by Bounds test of cointegration for model selection. The aim of this paper was twofold; one was to evaluate the performance of these five routinely used information criteria {Akaike Information Criterion (AIC), Akaike Information Criterion Corrected (AICC), Schwarz/Bayesian Information Criterion (SIC/BIC), Schwarz/Bayesian Information Criterion Corrected (SICC/BICC), and Hannan and Quinn Information Criterion (HQC)} and three structured approaches (Forward Selection, Backward Elimination, and Stepwise) by assessing their size and power properties at different sample sizes based on Monte Carlo simulations, and second was the assessment of the same based on real economic data. The second aim was achieved by the evaluation of the long-run relationship between three pairs of macroeconomic variables, i.e., Energy Consumption and GDP, Oil Price and GDP, and Broad Money and GDP for BRICS (Brazil, Russia, India, China and South Africa) countries using Bounds cointegration test. It was found that information criteria and structured procedures have the same powers for a sample size of 50 or greater. However, BICC and Stepwise are better at small sample sizes. In the light of simulation and real data results, a modified Bounds test with Stepwise model selection procedure may be used as it is strongly theoretically supported and avoids noise in the model selection process.

APA, Harvard, Vancouver, ISO, and other styles

50

Fenga, Livio. "Bootstrap Order Determination for ARMA Models: A Comparison between Different Model Selection Criteria." Journal of Probability and Statistics 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/1235979.

Full text

Abstract:

The present paper deals with the order selection of models of the class for autoregressive moving average. A novel method—previously designed to enhance the selection capabilities of the Akaike Information Criterion and successfully tested—is now extended to the other three popular selectors commonly used by both theoretical statisticians and practitioners. They are the final prediction error, the Bayesian information criterion, and the Hannan-Quinn information criterion which are employed in conjunction with a semiparametric bootstrap scheme of the type sieve.

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!