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Relevant bibliographies by topics / Model selection

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Academic literature on the topic 'Model selection'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 June 2025

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Journal articles on the topic "Model selection"

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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Model selection"

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Selén, Yngve. "Model selection /." Uppsala : Univ. : Dept. of Information Technology, Univ, 2004. http://www.it.uu.se/research/reports/lic/2004-003/.

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Selén, Yngve. "Model Selection." Licentiate thesis, Uppsala universitet, Avdelningen för systemteknik, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-86308.

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Before using a parametric model one has to be sure that it offers a reasonable description of the system to be modeled. If a bad model structure is employed, the obtained model will also be bad, no matter how good is the parameter estimation method. There exist many possible ways of validating candidate models. This thesis focuses on one of the most common ways, i.e., the use of information criteria. First, some common information criteria are presented, and in the later chapters, various extentions and implementations are shown. An important extention, which is advocated in the thesis, is the multi-model (or model averaging) approach to model selection. This multi-model approach consists of forming a weighted sum of several candidate models, which then can be used for inference.

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Evers, Ludger. "Model fitting and model selection for 'mixture of experts' models." Thesis, University of Oxford, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.445776.

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Billah, Baki 1965. "Model selection for time series forecasting models." Monash University, Dept. of Econometrics and Business Statistics, 2001. http://arrow.monash.edu.au/hdl/1959.1/8840.

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Yoshimura, Arihiro. "Essays on Semiparametric Model Selection and Model Averaging." Kyoto University, 2015. http://hdl.handle.net/2433/199059.

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PENG, SISI. "Evaluating Automatic Model Selection." Thesis, Uppsala universitet, Statistiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-154449.

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In this paper, we briefly describe the automatic model selection which is provided by Autometrics in the PcGive program. The modeler only needs to specify the initial model and the significance level at which to reduce the model. Then, the algorithm does the rest. The properties of Autometrics are discussed. We also explain its background concepts and try to see whether the model selected by the Autometrics can perform well. For a given data set, we use Autometrics to find a “new” model, and then compare the “new” model with a previously selected one by another modeler. It is an interesting issue to see whether Autometrics can also find models which fit better to the given data. As an illustration, we choose three examples. It is true that Autometrics is labor saving and always gives us a parsimonious model. It is really an invaluable instrument for social science. But, we still need more examples to strongly support the idea that Autometrics can find a model which fits the data better, just a few examples in this paper is far from enough.

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Bello, Bernardo. "PROCESS MANUFACTURING SELECTION MODEL." Thesis, KTH, Industriell produktion, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-218031.

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Aasberg, Pipirs Freddy, and Patrik Svensson. "Tenancy Model Selection Guidelines." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235716.

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Software as a Service (SaaS) is a subset of cloud services where a vendor provides software as a service to customers. The SaaS application is installed on the SaaS provider’s servers, and is often accessed via the web browser. In the context of SaaS, a customer is called tenant, which often is an organization that is accessing the SaaS application, but it could also be a single individual. A SaaS application can be classified into tenancy models. A tenancy model describes how a tenant’s data is mapped to the storage on the server-side of the SaaS application.By doing a research, the authors have drawn the conclusion that there is a lack of guidance for selecting tenancy models. The purpose of this thesis is to provide guidance for selecting tenancy models. The short-term-goal is to create a tenancy selection guide. The long-term-goal is to provide researchers and students with research material. This thesis provides a guidance model for selection of tenancy models. The model is called Tenancy Model Selection Guidelines (TMSG).TMSG was evaluated by interviewing two professionals from the software industry. The criteria used for evaluating TMSG were Interviewee credibility, Syntactic correctness, Semantic correctness, Usefulness and Model flexibility. In the interviews, both of the interviewees said that TMSG was in need of further refinements. Still they were positive to the achieved result.<br>Software as a Service (SaaS) är en delmängd av molntjänster där en tjänsteleverantör tillgodoser mjukvara som en tjänst åt kunder. SaaS-applikationen installeras på SaaS-leverantörens servrar, och åtkomsten till applikationen sker oftast via webbläsaren. I sammanhanget av SaaS kallas en kund för ten-ant, vilket oftast består av en organisation, eller i vissa fall enbart av en användare. En SaaS-applikation kan delas in i tenancy-modeller. En tenancymodell beskriver hur en tenant:s data är associerad till lagringsutrymmet på SaaS-leverantörens server.Efter att ha gjort en förstudie kunde författarna dra slutsatsen att det råder guidningsbrist för val av tenancy-modeller. Syftet med denna tes är att tillgodose vägledning för val av tenancy-modeller. Kortsiktsmålet är att skapa en guide för val av tenancy-modeller. Långsiktsmålet är att tillgodose forskare och studenter med forskningsmaterial. Denna tes tillgodoser en modell för guidning av val för tenancy-modeller. Namnet på denna guide är textitTenancy Model Selection Guidelines (TMSG).TMSG utvärderades genom intervjuer med två personer som jobbar inom mjukvaru-branschen. Kriterierna som användes vid utvärderingen av TMSG var följande: Trovärdighet hos den intervjuade personen, Syntaktisk korrekthet, Semantisk korrekthet, Användbarhet och Modellens flexibilitet. I båda intervjuerna ansåg de medverkande att TMSG behöver ytterligare finslipning, och de var båda positiva till det uppnådda resultatet.

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Belitz, Christiane. "Model Selection in Generalised Structured Additive Regression Models." Diss., lmu, 2007. http://nbn-resolving.de/urn:nbn:de:bvb:19-78896.

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Sommer, Julia. "Regularized estimation and model selection in compartment models." Diss., Ludwig-Maximilians-Universität München, 2013. http://nbn-resolving.de/urn:nbn:de:bvb:19-157673.

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Dynamic imaging series acquired in medical and biological research are often analyzed with the help of compartment models. Compartment models provide a parametric, nonlinear function of interpretable, kinetic parameters describing how some concentration of interest evolves over time. Aiming to estimate the kinetic parameters, this leads to a nonlinear regression problem. In many applications, the number of compartments needed in the model is not known from biological considerations but should be inferred from the data along with the kinetic parameters. As data from medical and biological experiments are often available in the form of images, the spatial data structure of the images has to be taken into account. This thesis addresses the problem of parameter estimation and model selection in compartment models. Besides a penalized maximum likelihood based approach, several Bayesian approaches-including a hierarchical model with Gaussian Markov random field priors and a model state approach with flexible model dimension-are proposed and evaluated to accomplish this task. Existing methods are extended for parameter estimation and model selection in more complex compartment models. However, in nonlinear regression and, in particular, for more complex compartment models, redundancy issues may arise. This thesis analyzes difficulties arising due to redundancy issues and proposes several approaches to alleviate those redundancy issues by regularizing the parameter space. The potential of the proposed estimation and model selection approaches is evaluated in simulation studies as well as for two in vivo imaging applications: a dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) study on breast cancer and a study on the binding behavior of molecules in living cell nuclei observed in a fluorescence recovery after photobleaching (FRAP) experiment.

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Books on the topic "Model selection"

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missing], [name. Model selection. Beachwood, OH: Institute of Mathematical Statistics, 2003.

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W, Zucchini, ed. Model selection. New York: Wiley, 1986.

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Draper, Norman Richard. Model selection problems. Toronto: University of Toronto, Dept. of Statistics, 1986.

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Burnham, Kenneth P., and David R. Anderson. Model Selection and Inference. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2917-7.

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Picard, Jean, ed. Concentration Inequalities and Model Selection. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-48503-2.

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Burnham, Kenneth P., and David R. Anderson, eds. Model Selection and Multimodel Inference. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/b97636.

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House, Christopher L. An sS model with adverse selection. Cambridge, MA: National Bureau of Economic Research, 2000.

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Grasa, Antonio Aznar. Econometric Model Selection: A New Approach. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-017-1358-0.

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John F. Kennedy School of Government, ed. A "selection model" of political representation. Cambridge, Mass: John F. Kennedy School of Government, Harvard University, 2008.

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Jungeilges, Jochen A. The robustness of model selection rules. Münster: Lit, 1992.

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Book chapters on the topic "Model selection"

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Castillo, Enrique, Angel Cobo, José Manuel Gutiérrez, and Rosa Eva Pruneda. "Model Selection." In Functional Networks with Applications, 133–46. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5601-5_5.

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Raudys, Šarūnas. "Model Selection." In Statistical and Neural Classifiers, 209–66. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0359-2_6.

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Yandell, Brian S. "Model Selection." In Practical Data Analysis for Designed Experiments, 145–58. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4899-3035-4_9.

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Bretthorst, G. Larry. "Model Selection." In Bayesian Spectrum Analysis and Parameter Estimation, 55–67. New York, NY: Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4684-9399-3_5.

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Wang, Yuedong. "Model Selection." In Handbook of Computational Statistics, 469–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21551-3_16.

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Blasco, Agustín. "Model Selection." In Bayesian Data Analysis for Animal Scientists, 213–46. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54274-4_10.

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Fürnkranz, Johannes, Philip K. Chan, Susan Craw, Claude Sammut, William Uther, Adwait Ratnaparkhi, Xin Jin, et al. "Model Selection." In Encyclopedia of Machine Learning, 683. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_551.

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Yamanishi, Kenji. "Model Selection." In Learning with the Minimum Description Length Principle, 91–135. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1790-7_3.

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Emmert-Streib, Frank, Salissou Moutari, and Matthias Dehmer. "Model Selection." In Elements of Data Science, Machine Learning, and Artificial Intelligence Using R, 309–30. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-13339-8_12.

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Dufour, Jean-Marie. "Model Selection." In The New Palgrave Dictionary of Economics, 1–9. London: Palgrave Macmillan UK, 2008. http://dx.doi.org/10.1057/978-1-349-95121-5_1964-1.

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Conference papers on the topic "Model selection"

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Berrezueta, Jose Maria, Santiago José Lituma Webster, Paúl Cárdenas-Delgado, and Priscila Cedillo. "Emotion-Based Interaction Mode Selection Model for University Students." In 2024 IEEE Eighth Ecuador Technical Chapters Meeting (ETCM), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/etcm63562.2024.10746154.

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Perugu, Sirisha, Jessica Holtorf, Vijay Dhar, and John Patrick Cleary. "Unique Model of Neonatal Palliative Care Delivery." In Selection of Abstracts From NCE 2015. American Academy of Pediatrics, 2017. http://dx.doi.org/10.1542/peds.140.1_meetingabstract.19.

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Duller, Christine. "Model selection for logistic regression models." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756152.

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Al-Refai, Mohammed, Walter Cazzola, and Sudipto Ghosh. "A Fuzzy Logic Based Approach for Model-Based Regression Test Selection." In 2017 ACM/IEEE 20th International Conference on Model-Driven Engineering Languages and Systems (MODELS). IEEE, 2017. http://dx.doi.org/10.1109/models.2017.17.

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Blanchard, Ashley, Bianca Calderon, Erin Cahill, Amanda Gonzalez, Dodi Meyer, Margaret C. Krause, and Suzanne Friedman. "Redesigning Primary Care Well Child Visits: A Group Model." In Selection of Abstracts From NCE 2016. American Academy of Pediatrics, 2018. http://dx.doi.org/10.1542/peds.141.1_meetingabstract.41.

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Adibhatla, S., and Z. Gastineau. "Tracking filter selection and control mode selection for model based control." In 30th Joint Propulsion Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1994. http://dx.doi.org/10.2514/6.1994-3204.

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Goldenberg, Alina, Janice Lucille Pelletier, and Sharon Jacob. "Pediatric Allergic Contact Dermatitis—quality Improvement via a Registry Model." In Selection of Abstracts From NCE 2016. American Academy of Pediatrics, 2018. http://dx.doi.org/10.1542/peds.141.1_meetingabstract.00.

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Yan, Xiaoran. "Bayesian model selection of stochastic block models." In 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2016. http://dx.doi.org/10.1109/asonam.2016.7752253.

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Zacekova, Eva, and Zdenek Vana. "Identification and model selection of building models." In 2012 20th Mediterranean Conference on Control & Automation (MED 2012). IEEE, 2012. http://dx.doi.org/10.1109/med.2012.6265853.

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Eguizabal, Alma, Peter J. Schreier, and David Ramirez. "MODEL-ORDER SELECTION IN STATISTICAL SHAPE MODELS." In 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2018. http://dx.doi.org/10.1109/mlsp.2018.8516941.

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Reports on the topic "Model selection"

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Osipov, G. S. Fuzzy elective selection model. Редакция журнала «ОПиПМ», 2019. http://dx.doi.org/10.18411/oppm-2019-26-1.

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Moral, Rafael. Statistical Model Selection and Simplification. Instats Inc., 2024. http://dx.doi.org/10.61700/bg3im1fi4a4dm1478.

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This three-day workshop provides a comprehensive understanding of statistical model selection, comparison, simplification, and averaging using R software, essential for enhancing research methodologies and analytical skills. Participants will gain hands-on experience with advanced techniques such as nested model comparison, out-of-sample predictive performance, variable selection, model averaging, and Bayesian methods, culminating in improved data analysis proficiency and research outcomes.

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Madigan, David, and Adrian E. Raftery. Model Selection and Accounting for Model Uncertainty in Graphical Models Using OCCAM's Window. Fort Belvoir, VA: Defense Technical Information Center, July 1991. http://dx.doi.org/10.21236/ada241408.

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Blume-Kohout, Robin J., and Travis L. Scholten. Characterizing Quantum Devices Using Model Selection. Office of Scientific and Technical Information (OSTI), August 2015. http://dx.doi.org/10.2172/1221861.

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Bai, Z. D., C. R. Rao, and Y. Wu. Model Selection with Data-Oriented Penalty. Fort Belvoir, VA: Defense Technical Information Center, April 1997. http://dx.doi.org/10.21236/ada324872.

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Orozova, Daniela. Appropriate E-test System Selection Model. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, June 2019. http://dx.doi.org/10.7546/crabs.2019.06.14.

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House, Christopher, and John Leahy. An sS Model with Adverse Selection. Cambridge, MA: National Bureau of Economic Research, December 2000. http://dx.doi.org/10.3386/w8030.

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Amit, Yali, and Donald Geman. A Computational Model for Visual Selection. Fort Belvoir, VA: Defense Technical Information Center, April 1998. http://dx.doi.org/10.21236/ada344220.

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Wilhelm, Daniel, and Susanne M. Schennach. A simple parametric model selection test. The IFS, March 2014. http://dx.doi.org/10.1920/wp.cem.2014.1014.

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Wilhelm, Daniel, and Susanne M. Schennach. A simple parametric model selection test. IFS, August 2016. http://dx.doi.org/10.1920/wp.cem.2016.3016.

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