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Artículos de revistas sobre el tema "Model selection"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 9 de junio de 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 (January 9, 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 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.

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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.

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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 (February 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 (November 2001): 733–54. http://dx.doi.org/10.1111/1467-9892.00251.

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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.

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6

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

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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.

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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.

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9

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

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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.

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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.

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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.

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13

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 model evaluation have been historically tied to reliance on <i>p</i>-values. In this paper, I outline an approach to model evaluation, comparison and selection based on a weight-of-evidence paradigm. The details and proposed sequence of steps are illustrated using a real-world example. At the end of the paper, I briefly discuss the current state of knowledge and a possible direction for future studies.

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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 (April 15, 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 hyperparameter optimization methods. The article incorporates robust evaluation metrics for both classification and regression tasks, emphasizing the importance of multi-metric assessment in capturing various performance dimensions. The article extends beyond initial model selection to address the critical yet often overlooked dimension of post-deployment maintenance, including concept drift detection and retraining strategies that ensure sustained model performance over time. The article demonstrates the practical application of these principles in high-stakes environments with domain-specific constraints. The article's integrated framework offers decision support for strategy selection based on data characteristics, with implementation guidance across common machine learning platforms. By synthesizing theoretical insights with practical considerations, this article provides researchers and practitioners with a structured approach to model selection throughout the complete machine learning lifecycle, ultimately enhancing the reliability and sustainability of AI applications in production environments.

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15

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.

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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 (December 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 (July 3, 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 (December 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 (September 25, 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 (May 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 (October 11, 2023): 1905. http://dx.doi.org/10.3390/sym15101905.

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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.

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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 (October 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 (March 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 (June 17, 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 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.

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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 (August 28, 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 (November 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 (October 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 (August 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 (August 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 (March 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 (March 10, 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 (April 3, 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 (March 12, 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 (January 8, 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 (July 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 (June 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 (January 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 (August 1, 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 (April 8, 2017): 7. https://doi.org/10.1186/s40709-017-0064-0.

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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 implementations that compute summary statistics such as Tajima's D, to more advanced statistical approaches that use combinations of statistics, maximum likelihood, machine learning etc. In this survey, we present and discuss summary statistics and software tools, and classify them based on the selective sweep signature they detect, i.e., SFS-based vs. LD-based, as well as their capacity to analyze whole genomes or just subgenomic regions. Additionally, we summarize the results of comparisons among four open-source software releases (SweeD, SweepFinder, SweepFinder2, and OmegaPlus) regarding sensitivity, specificity, and execution times. In equilibrium neutral models or mild bottlenecks, both SFS- and LD-based methods are able to detect selective sweeps accurately. Methods and tools that rely on LD exhibit higher true positive rates than SFS-based ones under the model of a single sweep or recurrent hitchhiking. However, their false positive rate is elevated when a misspecified demographic model is used to represent the null hypothesis. When the correct (or similar to the correct) demographic model is used instead, the false positive rates are considerably reduced. The accuracy of detecting the true target of selection is decreased in bottleneck scenarios. In terms of execution time, LD-based methods are typically faster than SFS-based methods, due to the nature of required arithmetic.

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42

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.

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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 (August 8, 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 selecting the most important features within random forests is still a challenging question. The often-used internal Gini importances of random forests are based on the amount of training examples that are divided by a feature but fail to acknowledge the magnitude of change in the target variable, leading to suboptimal selections. Shapley values are an established and unified framework for feature attribution, i.e., specifying how much each feature in a trained ML model contributes to the predictions for a given instance. Previous studies highlight the effectiveness of Shapley values for feature selection in real-world applications, while other research emphasizes certain theoretical limitations. This study provides an application-driven discussion of Shapley values for feature selection by first proposing four necessary conditions for a successful feature selection with Shapley values that are extracted from a multitude of critical research in the field. Given these valuable conditions, Shapley value feature selection is nevertheless a model averaging procedure by definition, where unimportant features can alter the final selection. Therefore, we additionally present Conditional Feature Selection (CFS) as a novel algorithm for performing feature selection that mitigates this problem and use it to evaluate the impact of model averaging in several real-world examples, covering the use of ML in geomatics. The results of this study show Shapley values as a good measure for feature selection when compared with Gini feature importances on four real-world examples, improving the RMSE by 5% when averaged over selections of all possible subset sizes. An even better selection can be achieved by CFS, improving on the Gini selection by approximately 7.5% in terms of RMSE. For random forests, Shapley value calculation can be performed in polynomial time, offering an advantage over the exponential runtime of CFS, building a trade-off to the lost accuracy in feature selection due to model averaging.

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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 (May 27, 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, 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.

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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 (November 7, 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 (June 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 (December 12, 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 (March 2001): 49–61. http://dx.doi.org/10.1198/108571101300325139.

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