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Artículos de revistas sobre el tema "Model selection"

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Publicado: 4 de junio de 2021
Última modificación: 8 de junio de 2024

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1

Lei, Y. y S. Y Zhang. "Comparison and selection of growth models using the Schnute model". Journal of Forest Science 52, No. 4 (9 de enero de 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, n.º 8 (1 de octubre de 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 y Sandra E. Thompson. "Model Selection For Geostatistical Models". Ecological Applications 16, n.º 1 (febrero de 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, n.º 6 (noviembre de 2001): 733–54. http://dx.doi.org/10.1111/1467-9892.00251.

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5

Parr, William, H. Linhart y W. Zucchini. "Model Selection." Journal of the American Statistical Association 84, n.º 406 (junio de 1989): 620. http://dx.doi.org/10.2307/2289962.

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6

Blom, G., H. Linhart y W. Zucchini. "Model Selection." Biometrics 45, n.º 1 (marzo de 1989): 340. http://dx.doi.org/10.2307/2532060.

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7

Brockett, Patrick L., H. Linhart y W. Zucchini. "Model Selection". Journal of Marketing Research 25, n.º 2 (mayo de 1988): 214. http://dx.doi.org/10.2307/3172654.

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8

Viennet, G., F. Comte y Y. Baraud. "model selection". Annals of Statistics 29, n.º 3 (junio de 2001): 839–75. http://dx.doi.org/10.1214/aos/1009210692.

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9

Littell, Ramon C. "Model Selection". Technometrics 30, n.º 1 (febrero de 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, n.º 3 (julio de 1988): 218. http://dx.doi.org/10.1080/00224065.1988.11979111.

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11

Gibson, Martin G., H. Linhart y W. Zucchini. "Model Selection." Statistician 37, n.º 4/5 (1988): 486. http://dx.doi.org/10.2307/2348786.

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12

Scott, Marian, H. Linhart y W. Zucchini. "Model Selection." Journal of the Royal Statistical Society. Series A (Statistics in Society) 151, n.º 2 (1988): 375. http://dx.doi.org/10.2307/2982782.

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13

Ryu, Hang Keun. "Subjective model selection rules versus passive model selection rules". Economic Modelling 28, n.º 1-2 (enero de 2011): 459–72. http://dx.doi.org/10.1016/j.econmod.2010.08.002.

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14

Kawano, Shuichi, Ibuki Hoshina, Kaito Shimamura y Sadanori Konishi. "PREDICTIVE MODEL SELECTION CRITERIA FOR BAYESIAN LASSO REGRESSION". Journal of the Japanese Society of Computational Statistics 28, n.º 1 (2015): 67–82. http://dx.doi.org/10.5183/jjscs.1501001_220.

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15

Gopalakrishnan, M. Muthu. "Optimal Portfolio Selection Using Sharpe’s Single Index Model". Indian Journal of Applied Research 4, n.º 1 (1 de octubre de 2011): 286–88. http://dx.doi.org/10.15373/2249555x/jan2014/83.

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16

Muralidharan, K. "A Teaching Note for Model Selection and Validation". Mathematical Journal of Interdisciplinary Sciences 1, n.º 2 (2 de marzo de 2013): 55–62. http://dx.doi.org/10.15415/mjis.2013.12012.

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17

Rajeevan, A. K., P. V. Shouri y Usha Nair. "A Reliability Based Model for Wind Turbine Selection". International Journal of Renewable Energy Development 2, n.º 2 (17 de junio de 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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18

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

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19

Bedrick, Edward J. y Winston K. Crandall. "MODEL SELECTION CRITERIA FOR LOGLINEAR MODELS". Australian & New Zealand Journal of Statistics 52, n.º 4 (diciembre de 2010): 439–49. http://dx.doi.org/10.1111/j.1467-842x.2010.00593.x.

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20

Lv, Jinchi y Jun S. Liu. "Model selection principles in misspecified models". Journal of the Royal Statistical Society: Series B (Statistical Methodology) 76, n.º 1 (3 de julio de 2013): 141–67. http://dx.doi.org/10.1111/rssb.12023.

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21

Eğri˙oğlu, Erol y Süleyman Günay. "Bayesian model selection in ARFIMA models". Expert Systems with Applications 37, n.º 12 (diciembre de 2010): 8359–64. http://dx.doi.org/10.1016/j.eswa.2010.05.047.

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22

Alhamzawi, Rahim. "Model selection in quantile regression models". Journal of Applied Statistics 42, n.º 2 (25 de septiembre de 2014): 445–58. http://dx.doi.org/10.1080/02664763.2014.959905.

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23

Müller, Samuel, J. L. Scealy y A. H. Welsh. "Model Selection in Linear Mixed Models". Statistical Science 28, n.º 2 (mayo de 2013): 135–67. http://dx.doi.org/10.1214/12-sts410.

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24

Mamun, Abdulla y Sudhir Paul. "Model Selection in Generalized Linear Models". Symmetry 15, n.º 10 (11 de octubre de 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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25

Katsanevakis, Stelios. "Modelling fish growth: Model selection, multi-model inference and model selection uncertainty". Fisheries Research 81, n.º 2-3 (noviembre de 2006): 229–35. http://dx.doi.org/10.1016/j.fishres.2006.07.002.

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26

Ginestet, Cedric E. "Model Selection and Model Averaging". Journal of the Royal Statistical Society: Series A (Statistics in Society) 172, n.º 4 (octubre de 2009): 937. http://dx.doi.org/10.1111/j.1467-985x.2009.00614_5.x.

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27

Vonta, Ilia. "Model selection and model averaging". Journal of Applied Statistics 37, n.º 8 (agosto de 2010): 1419–20. http://dx.doi.org/10.1080/02664760902899774.

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28

Lin, Li-Chung, Po-Hsien Huang y Li-Jen Weng. "Selecting Path Models in SEM: A Comparison of Model Selection Criteria". Structural Equation Modeling: A Multidisciplinary Journal 24, n.º 6 (28 de agosto de 2017): 855–69. http://dx.doi.org/10.1080/10705511.2017.1363652.

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29

Shen, Xiaotong y Jianming Ye. "Adaptive Model Selection". Journal of the American Statistical Association 97, n.º 457 (marzo de 2002): 210–21. http://dx.doi.org/10.1198/016214502753479356.

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30

MUKHERJEE, PIA y DAVID PARKINSON. "COSMOLOGICAL MODEL SELECTION". International Journal of Modern Physics A 23, n.º 06 (10 de marzo de 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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31

Hoegh, Andrew, Dipayan Maiti y Scotland Leman. "Multiset Model Selection". Journal of Computational and Graphical Statistics 27, n.º 2 (3 de abril de 2018): 436–48. http://dx.doi.org/10.1080/10618600.2017.1379408.

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32

Amato, Umberto, Anestis Antoniadis y Italia De Feis. "Additive model selection". Statistical Methods & Applications 25, n.º 4 (12 de marzo de 2016): 519–64. http://dx.doi.org/10.1007/s10260-016-0357-8.

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33

Giudici, Paolo y Emanuela Raffinetti. "Lorenz Model Selection". Journal of Classification 37, n.º 3 (8 de enero de 2020): 754–68. http://dx.doi.org/10.1007/s00357-019-09358-w.

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34

Stoica, P. y Y. Selen. "Model-order selection". IEEE Signal Processing Magazine 21, n.º 4 (julio de 2004): 36–47. http://dx.doi.org/10.1109/msp.2004.1311138.

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35

Shao, Jun. "Bootstrap Model Selection". Journal of the American Statistical Association 91, n.º 434 (junio de 1996): 655–65. http://dx.doi.org/10.1080/01621459.1996.10476934.

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36

Laud, Purushottam W. y Joseph G. Ibrahim. "Predictive Model Selection". Journal of the Royal Statistical Society: Series B (Methodological) 57, n.º 1 (enero de 1995): 247–62. http://dx.doi.org/10.1111/j.2517-6161.1995.tb02028.x.

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37

Birgé, Lucien y Pascal Massart. "Gaussian model selection". Journal of the European Mathematical Society 3, n.º 3 (1 de agosto de 2001): 203–68. http://dx.doi.org/10.1007/s100970100031.

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38

Meade, Nigel y Towhidul Islam. "Technological Forecasting—Model Selection, Model Stability, and Combining Models". Management Science 44, n.º 8 (agosto de 1998): 1115–30. http://dx.doi.org/10.1287/mnsc.44.8.1115.

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39

Zhang, Yongli y Yuhong Yang. "Cross-validation for selecting a model selection procedure". Journal of Econometrics 187, n.º 1 (julio de 2015): 95–112. http://dx.doi.org/10.1016/j.jeconom.2015.02.006.

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40

S. Daugherty, Mary, Thadavillil Jithendranathan y David O. Vang. "Portfolio selection using the multiple attribute decision making model". Investment Management and Financial Innovations 18, n.º 2 (27 de mayo de 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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41

Alzober, Waled y Abdul Razak Yaakub. "Integrated Model for Selection the Prequalification Criteria of Contractor". Lecture Notes on Software Engineering 2, n.º 3 (2014): 233–37. http://dx.doi.org/10.7763/lnse.2014.v2.128.

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42

Sarfaraz, A. "A Fuzzy Conceptual Design Selection Model Considering Conflict Resolution". International Journal of Engineering and Technology 4, n.º 1 (2012): 38–45. http://dx.doi.org/10.7763/ijet.2012.v4.315.

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43

Ariyo, Oludare, Emmanuel Lesaffre, Geert Verbeke, Martijn Huisman, Martijn Heymans y Jos Twisk. "Bayesian model selection for multilevel mediation models". Statistica Neerlandica 76, n.º 2 (7 de noviembre de 2021): 219–35. http://dx.doi.org/10.1111/stan.12256.

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44

Rivers, Douglas y Quang Vuong. "Model selection tests for nonlinear dynamic models". Econometrics Journal 5, n.º 1 (1 de junio de 2002): 1–39. http://dx.doi.org/10.1111/1368-423x.t01-1-00071.

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45

Claeskens, Gerda, Christophe Croux y Johan Van Kerckhoven. "PREDICTION-FOCUSED MODEL SELECTION FOR AUTOREGRESSIVE MODELS". Australian & New Zealand Journal of Statistics 49, n.º 4 (12 de diciembre de 2007): 359–79. http://dx.doi.org/10.1111/j.1467-842x.2007.00487.x.

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46

Pan, Wei y Chap T. Le. "Bootstrap model selection in generalized linear models". Journal of Agricultural, Biological, and Environmental Statistics 6, n.º 1 (marzo de 2001): 49–61. http://dx.doi.org/10.1198/108571101300325139.

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47

Yan, Xiaoran, Cosma Shalizi, Jacob E. Jensen, Florent Krzakala, Cristopher Moore, Lenka Zdeborová, Pan Zhang y Yaojia Zhu. "Model selection for degree-corrected block models". Journal of Statistical Mechanics: Theory and Experiment 2014, n.º 5 (16 de mayo de 2014): P05007. http://dx.doi.org/10.1088/1742-5468/2014/05/p05007.

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48

Cantelmo, C. y L. Piroddi. "Adaptive model selection for polynomial NARX models". IET Control Theory & Applications 4, n.º 12 (1 de diciembre de 2010): 2693–706. http://dx.doi.org/10.1049/iet-cta.2009.0581.

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49

Song, Joon Jin y Victor De Oliveira. "Bayesian model selection in spatial lattice models". Statistical Methodology 9, n.º 1-2 (enero de 2012): 228–38. http://dx.doi.org/10.1016/j.stamet.2011.01.003.

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50

Ni, Xiao, Hao Helen Zhang y Daowen Zhang. "Automatic model selection for partially linear models". Journal of Multivariate Analysis 100, n.º 9 (octubre de 2009): 2100–2111. http://dx.doi.org/10.1016/j.jmva.2009.06.009.

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