Log in

Relevant bibliographies by topics / Model selection / Journal articles

Journal articles on the topic 'Model selection'

To see the other types of publications on this topic, follow the link: Model selection.

Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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 'Model selection.'

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

Lei, Y., and S. Y Zhang. "Comparison and selection of growth models using the Schnute model." Journal of Forest Science 52, No. 4 (January 9, 2012): 188–96. http://dx.doi.org/10.17221/4501-jfs.

Full text

Abstract:

Forestmodellers have long faced the problem of selecting an appropriate mathematical model to describe tree ontogenetic or size-shape empirical relationships for tree species. A common practice is to develop many models (or a model pool) that include different functional forms, and then to select the most appropriate one for a given data set. However, this process may impose subjective restrictions on the functional form. In this process, little attention is paid to the features (e.g. asymptote and inflection point rather than asymptote and nonasymptote) of different functional forms, and to the intrinsic curve of a given data set. In order to find a better way of comparing and selecting the growth models, this paper describes and analyses the characteristics of the Schnute model. This model has both flexibility and versatility that have not been used in forestry. In this study, the Schnute model was applied to different data sets of selected forest species to determine their functional forms. The results indicate that the model shows some desirable properties for the examined data sets, and allows for discerning the different intrinsic curve shapes such as sigmoid, concave and other curve shapes. Since no suitable functional form for a given data set is usually known prior to the comparison of candidate models, it is recommended that the Schnute model be used as the first step to determine an appropriate functional form of the data set under investigation in order to avoid using a functional form a priori.

APA, Harvard, Vancouver, ISO, and other styles

2

Solanki, Dr Ashvinkumar H. "Portfolio Selection Process through Markowitz Model." Indian Journal of Applied Research 4, no. 8 (October 1, 2011): 356–58. http://dx.doi.org/10.15373/2249555x/august2014/90.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Hoeting, Jennifer A., Richard A. Davis, Andrew A. Merton, and Sandra E. Thompson. "Model Selection For Geostatistical Models." Ecological Applications 16, no. 1 (February 2006): 87–98. http://dx.doi.org/10.1890/04-0576.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Kapetanios, George. "Model Selection in Threshold Models." Journal of Time Series Analysis 22, no. 6 (November 2001): 733–54. http://dx.doi.org/10.1111/1467-9892.00251.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Parr, William, H. Linhart, and W. Zucchini. "Model Selection." Journal of the American Statistical Association 84, no. 406 (June 1989): 620. http://dx.doi.org/10.2307/2289962.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Blom, G., H. Linhart, and W. Zucchini. "Model Selection." Biometrics 45, no. 1 (March 1989): 340. http://dx.doi.org/10.2307/2532060.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Brockett, Patrick L., H. Linhart, and W. Zucchini. "Model Selection." Journal of Marketing Research 25, no. 2 (May 1988): 214. http://dx.doi.org/10.2307/3172654.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Viennet, G., F. Comte, and Y. Baraud. "model selection." Annals of Statistics 29, no. 3 (June 2001): 839–75. http://dx.doi.org/10.1214/aos/1009210692.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Littell, Ramon C. "Model Selection." Technometrics 30, no. 1 (February 1988): 115–16. http://dx.doi.org/10.1080/00401706.1988.10488331.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Nelson, Lloyd S. "Model Selection." Journal of Quality Technology 20, no. 3 (July 1988): 218. http://dx.doi.org/10.1080/00224065.1988.11979111.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Gibson, Martin G., H. Linhart, and W. Zucchini. "Model Selection." Statistician 37, no. 4/5 (1988): 486. http://dx.doi.org/10.2307/2348786.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Scott, Marian, H. Linhart, and W. Zucchini. "Model Selection." Journal of the Royal Statistical Society. Series A (Statistics in Society) 151, no. 2 (1988): 375. http://dx.doi.org/10.2307/2982782.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Ryu, Hang Keun. "Subjective model selection rules versus passive model selection rules." Economic Modelling 28, no. 1-2 (January 2011): 459–72. http://dx.doi.org/10.1016/j.econmod.2010.08.002.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Kawano, Shuichi, Ibuki Hoshina, Kaito Shimamura, and Sadanori Konishi. "PREDICTIVE MODEL SELECTION CRITERIA FOR BAYESIAN LASSO REGRESSION." Journal of the Japanese Society of Computational Statistics 28, no. 1 (2015): 67–82. http://dx.doi.org/10.5183/jjscs.1501001_220.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Gopalakrishnan, M. Muthu. "Optimal Portfolio Selection Using Sharpe’s Single Index Model." Indian Journal of Applied Research 4, no. 1 (October 1, 2011): 286–88. http://dx.doi.org/10.15373/2249555x/jan2014/83.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Muralidharan, K. "A Teaching Note for Model Selection and Validation." Mathematical Journal of Interdisciplinary Sciences 1, no. 2 (March 2, 2013): 55–62. http://dx.doi.org/10.15415/mjis.2013.12012.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Rajeevan, A. K., P. V. Shouri, and Usha Nair. "A Reliability Based Model for Wind Turbine Selection." International Journal of Renewable Energy Development 2, no. 2 (June 17, 2013): 69–74. http://dx.doi.org/10.14710/ijred.2.2.69-74.

Full text

Abstract:

A wind turbine generator output at a specific site depends on many factors, particularly cut- in, rated and cut-out wind speed parameters. Hence power output varies from turbine to turbine. The objective of this paper is to develop a mathematical relationship between reliability and wind power generation. The analytical computation of monthly wind power is obtained from weibull statistical model using cubic mean cube root of wind speed. Reliability calculation is based on failure probability analysis. There are many different types of wind turbinescommercially available in the market. From reliability point of view, to get optimum reliability in power generation, it is desirable to select a wind turbine generator which is best suited for a site. The mathematical relationship developed in this paper can be used for site-matching turbine selection in reliability point of view.

APA, Harvard, Vancouver, ISO, and other styles

18

Chen, Qiong-Ying, Long Chen, Jian-Nan Su, Ming-Jian Fu, and Guang-Yong Chen. "Model selection for RBF-ARX models." Applied Soft Computing 121 (May 2022): 108723. http://dx.doi.org/10.1016/j.asoc.2022.108723.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Bedrick, Edward J., and Winston K. Crandall. "MODEL SELECTION CRITERIA FOR LOGLINEAR MODELS." Australian & New Zealand Journal of Statistics 52, no. 4 (December 2010): 439–49. http://dx.doi.org/10.1111/j.1467-842x.2010.00593.x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Lv, Jinchi, and Jun S. Liu. "Model selection principles in misspecified models." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 76, no. 1 (July 3, 2013): 141–67. http://dx.doi.org/10.1111/rssb.12023.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Eğri˙oğlu, Erol, and Süleyman Günay. "Bayesian model selection in ARFIMA models." Expert Systems with Applications 37, no. 12 (December 2010): 8359–64. http://dx.doi.org/10.1016/j.eswa.2010.05.047.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Alhamzawi, Rahim. "Model selection in quantile regression models." Journal of Applied Statistics 42, no. 2 (September 25, 2014): 445–58. http://dx.doi.org/10.1080/02664763.2014.959905.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Müller, Samuel, J. L. Scealy, and A. H. Welsh. "Model Selection in Linear Mixed Models." Statistical Science 28, no. 2 (May 2013): 135–67. http://dx.doi.org/10.1214/12-sts410.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Mamun, Abdulla, and Sudhir Paul. "Model Selection in Generalized Linear Models." Symmetry 15, no. 10 (October 11, 2023): 1905. http://dx.doi.org/10.3390/sym15101905.

Full text

Abstract:

The problem of model selection in regression analysis through the use of forward selection, backward elimination, and stepwise selection has been well explored in the literature. The main assumption in this, of course, is that the data are normally distributed and the main tool used here is either a t test or an F test. However, the properties of these model selection procedures are not well-known. The purpose of this paper is to study the properties of these procedures within generalized linear regression models, considering the normal linear regression model as a special case. The main tool that is being used is the score test. However, the F test and other large sample tests, such as the likelihood ratio and the Wald test, the AIC, and the BIC, are included for the comparison. A systematic study, through simulations, of the properties of this procedure was conducted, in terms of level and power, for symmetric and asymmetric distributions, such as normal, Poisson, and binomial regression models. Extensions for skewed distributions, over-dispersed Poisson (the negative binomial), and over-dispersed binomial (the beta-binomial) regression models, are also given and evaluated. The methods are applied to analyze two health datasets.

APA, Harvard, Vancouver, ISO, and other styles

25

Katsanevakis, Stelios. "Modelling fish growth: Model selection, multi-model inference and model selection uncertainty." Fisheries Research 81, no. 2-3 (November 2006): 229–35. http://dx.doi.org/10.1016/j.fishres.2006.07.002.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Ginestet, Cedric E. "Model Selection and Model Averaging." Journal of the Royal Statistical Society: Series A (Statistics in Society) 172, no. 4 (October 2009): 937. http://dx.doi.org/10.1111/j.1467-985x.2009.00614_5.x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Vonta, Ilia. "Model selection and model averaging." Journal of Applied Statistics 37, no. 8 (August 2010): 1419–20. http://dx.doi.org/10.1080/02664760902899774.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Lin, Li-Chung, Po-Hsien Huang, and Li-Jen Weng. "Selecting Path Models in SEM: A Comparison of Model Selection Criteria." Structural Equation Modeling: A Multidisciplinary Journal 24, no. 6 (August 28, 2017): 855–69. http://dx.doi.org/10.1080/10705511.2017.1363652.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Shen, Xiaotong, and Jianming Ye. "Adaptive Model Selection." Journal of the American Statistical Association 97, no. 457 (March 2002): 210–21. http://dx.doi.org/10.1198/016214502753479356.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

MUKHERJEE, PIA, and DAVID PARKINSON. "COSMOLOGICAL MODEL SELECTION." International Journal of Modern Physics A 23, no. 06 (March 10, 2008): 787–802. http://dx.doi.org/10.1142/s0217751x08039736.

Full text

Abstract:

We give an overview of the recent progress in the field of cosmological model selection. Model selection statistics, such as those based on information theory and on Bayesian statistics are introduced and discussed. In the Bayesian framework, the marginalised model likelihood, or evidence, is the primary model selection statistic. We describe different methods of computing the evidence, and focus in particular on Nested Sampling. We describe the results of applying model selection methods to new cosmological data such as the CMB measurements by WMAP.

APA, Harvard, Vancouver, ISO, and other styles

31

Hoegh, Andrew, Dipayan Maiti, and Scotland Leman. "Multiset Model Selection." Journal of Computational and Graphical Statistics 27, no. 2 (April 3, 2018): 436–48. http://dx.doi.org/10.1080/10618600.2017.1379408.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Amato, Umberto, Anestis Antoniadis, and Italia De Feis. "Additive model selection." Statistical Methods & Applications 25, no. 4 (March 12, 2016): 519–64. http://dx.doi.org/10.1007/s10260-016-0357-8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Giudici, Paolo, and Emanuela Raffinetti. "Lorenz Model Selection." Journal of Classification 37, no. 3 (January 8, 2020): 754–68. http://dx.doi.org/10.1007/s00357-019-09358-w.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Stoica, P., and Y. Selen. "Model-order selection." IEEE Signal Processing Magazine 21, no. 4 (July 2004): 36–47. http://dx.doi.org/10.1109/msp.2004.1311138.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Shao, Jun. "Bootstrap Model Selection." Journal of the American Statistical Association 91, no. 434 (June 1996): 655–65. http://dx.doi.org/10.1080/01621459.1996.10476934.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Laud, Purushottam W., and Joseph G. Ibrahim. "Predictive Model Selection." Journal of the Royal Statistical Society: Series B (Methodological) 57, no. 1 (January 1995): 247–62. http://dx.doi.org/10.1111/j.2517-6161.1995.tb02028.x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Birgé, Lucien, and Pascal Massart. "Gaussian model selection." Journal of the European Mathematical Society 3, no. 3 (August 1, 2001): 203–68. http://dx.doi.org/10.1007/s100970100031.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Meade, Nigel, and Towhidul Islam. "Technological Forecasting—Model Selection, Model Stability, and Combining Models." Management Science 44, no. 8 (August 1998): 1115–30. http://dx.doi.org/10.1287/mnsc.44.8.1115.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Zhang, Yongli, and Yuhong Yang. "Cross-validation for selecting a model selection procedure." Journal of Econometrics 187, no. 1 (July 2015): 95–112. http://dx.doi.org/10.1016/j.jeconom.2015.02.006.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

S. Daugherty, Mary, Thadavillil Jithendranathan, and David O. Vang. "Portfolio selection using the multiple attribute decision making model." Investment Management and Financial Innovations 18, no. 2 (May 27, 2021): 155–65. http://dx.doi.org/10.21511/imfi.18(2).2021.13.

Full text

Abstract:

This paper uses a Multiple Attribute Decision Making (MADM) model to improve the out-of-sample performance of a naïve asset allocation model. Under certain conditions, the naïve model has out-performed other portfolio optimization models, but it also has been shown to increase the tail risk. The MADM model uses a set of attributes to rank the assets and is flexible with the attributes that can be used in the ranking process. The MADM model assigns weights to each attribute and uses these weights to rank assets in terms of their desirability for inclusion in a portfolio. Using the MADM model, assets are ranked based on the attributes, and unlike the naïve model, only the top 50 percent of assets are included in the portfolio at any point in time. This model is tested using both developed and emerging market stock indices. In the case of developed markets, the MADM model had 24.04 percent higher return and 53.66 percent less kurtosis than the naïve model. In the case of emerging markets, the MADM model return is 90.16 percent higher than the naïve model, but with almost similar kurtosis.

APA, Harvard, Vancouver, ISO, and other styles

41

Alzober, Waled, and Abdul Razak Yaakub. "Integrated Model for Selection the Prequalification Criteria of Contractor." Lecture Notes on Software Engineering 2, no. 3 (2014): 233–37. http://dx.doi.org/10.7763/lnse.2014.v2.128.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Sarfaraz, A. "A Fuzzy Conceptual Design Selection Model Considering Conflict Resolution." International Journal of Engineering and Technology 4, no. 1 (2012): 38–45. http://dx.doi.org/10.7763/ijet.2012.v4.315.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Ariyo, Oludare, Emmanuel Lesaffre, Geert Verbeke, Martijn Huisman, Martijn Heymans, and Jos Twisk. "Bayesian model selection for multilevel mediation models." Statistica Neerlandica 76, no. 2 (November 7, 2021): 219–35. http://dx.doi.org/10.1111/stan.12256.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Rivers, Douglas, and Quang Vuong. "Model selection tests for nonlinear dynamic models." Econometrics Journal 5, no. 1 (June 1, 2002): 1–39. http://dx.doi.org/10.1111/1368-423x.t01-1-00071.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Claeskens, Gerda, Christophe Croux, and Johan Van Kerckhoven. "PREDICTION-FOCUSED MODEL SELECTION FOR AUTOREGRESSIVE MODELS." Australian & New Zealand Journal of Statistics 49, no. 4 (December 12, 2007): 359–79. http://dx.doi.org/10.1111/j.1467-842x.2007.00487.x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Pan, Wei, and Chap T. Le. "Bootstrap model selection in generalized linear models." Journal of Agricultural, Biological, and Environmental Statistics 6, no. 1 (March 2001): 49–61. http://dx.doi.org/10.1198/108571101300325139.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Yan, Xiaoran, Cosma Shalizi, Jacob E. Jensen, Florent Krzakala, Cristopher Moore, Lenka Zdeborová, Pan Zhang, and Yaojia Zhu. "Model selection for degree-corrected block models." Journal of Statistical Mechanics: Theory and Experiment 2014, no. 5 (May 16, 2014): P05007. http://dx.doi.org/10.1088/1742-5468/2014/05/p05007.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Cantelmo, C., and L. Piroddi. "Adaptive model selection for polynomial NARX models." IET Control Theory & Applications 4, no. 12 (December 1, 2010): 2693–706. http://dx.doi.org/10.1049/iet-cta.2009.0581.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Song, Joon Jin, and Victor De Oliveira. "Bayesian model selection in spatial lattice models." Statistical Methodology 9, no. 1-2 (January 2012): 228–38. http://dx.doi.org/10.1016/j.stamet.2011.01.003.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Ni, Xiao, Hao Helen Zhang, and Daowen Zhang. "Automatic model selection for partially linear models." Journal of Multivariate Analysis 100, no. 9 (October 2009): 2100–2111. http://dx.doi.org/10.1016/j.jmva.2009.06.009.

Full text

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!