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Articoli di riviste sul tema "Model selection"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 25 luglio 2025

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1

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

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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 t

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2

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

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3

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

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4

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

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5

Grace, James. "A 'Weight of Evidence' approach to evaluating structural equation models." One Ecosystem 5 (March 13, 2020): e50452. https://doi.org/10.3897/oneeco.5.e50452.

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It is possible that model selection has been the most researched and most discussed topic in the history of both statistics and structural equation modeling (SEM). The reason for this is because selecting one model for interpretive use from amongst many possible models is both essential and difficult. The published protocols and advice for model evaluation and selection in SEM studies are complex and difficult to integrate with current approaches used in biology. Opposition to the use of <i>p</i>-values and decision thresholds has been voiced by the statistics community, yet certain phases of

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6

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

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7

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

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8

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

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9

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

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10

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

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11

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

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12

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

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13

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.

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14

Mistry, Het. "Mastering Model Selection for AI/ML Models." European Journal of Computer Science and Information Technology 13, no. 14 (2025): 55–67. https://doi.org/10.37745/ejcsit.2013/vol13n145567.

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This article presents a comprehensive framework for mastering model selection in artificial intelligence and machine learning applications across diverse domains. The article addresses the fundamental challenge of selecting models that optimally balance complexity with generalization capability, navigating the classic bias-variance tradeoff that underpins predictive performance. Beginning with theoretical foundations of regularization approaches and complexity measures, the article proceeds through data-driven selection strategies, including cross-validation techniques and advanced hyperparame

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15

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

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16

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.

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17

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

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18

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 (2013): 141–67. http://dx.doi.org/10.1111/rssb.12023.

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19

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

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20

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

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21

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

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22

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

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Abstract (sommario):

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

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23

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.

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24

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

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25

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

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26

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 (2013): 69–74. http://dx.doi.org/10.14710/ijred.2.2.69-74.

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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 rel

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27

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 (2017): 855–69. http://dx.doi.org/10.1080/10705511.2017.1363652.

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28

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

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29

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

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30

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

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31

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

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32

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

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33

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

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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.

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34

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

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35

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

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36

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

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37

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

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38

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

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39

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

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40

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

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41

Pavlidis, Pavlos, and Nikolaos Alachiotis. "A survey of methods and tools to detect recent and strong positive selection." Journal of Biological Research-Thessaloniki 24, no. 1 (2017): 7. https://doi.org/10.1186/s40709-017-0064-0.

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Abstract (sommario):

Positive selection occurs when an allele is favored by natural selection. The frequency of the favored allele increases in the population and due to genetic hitchhiking the neighboring linked variation diminishes, creating so-called selective sweeps. Detecting traces of positive selection in genomes is achieved by searching for signatures introduced by selective sweeps, such as regions of reduced variation, a specific shift of the site frequency spectrum, and particular LD patterns in the region. A variety of methods and tools can be used for detecting sweeps, ranging from simple implementatio

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42

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

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43

Huber, Florian, and Volker Steinhage. "Conditional Feature Selection: Evaluating Model Averaging When Selecting Features with Shapley Values." Geomatics 4, no. 3 (2024): 286–310. http://dx.doi.org/10.3390/geomatics4030016.

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In the field of geomatics, artificial intelligence (AI) and especially machine learning (ML) are rapidly transforming the field of geomatics with respect to collecting, managing, and analyzing spatial data. Feature selection as a building block in ML is crucial because it directly impacts the performance and predictive power of a model by selecting the most critical variables and eliminating the redundant and irrelevant ones. Random forests have now been used for decades and allow for building models with high accuracy. However, finding the most expressive features from the dataset by selectin

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44

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 (2021): 155–65. http://dx.doi.org/10.21511/imfi.18(2).2021.13.

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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, a

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45

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.

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46

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.

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47

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 (2021): 219–35. http://dx.doi.org/10.1111/stan.12256.

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48

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

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49

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

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50

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

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