Academic literature on the topic 'Model selection curves'
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Journal articles on the topic "Model selection curves"
Müller, Samuel, and Alan H. Welsh. "On Model Selection Curves." International Statistical Review 78, no. 2 (May 5, 2010): 240–56. http://dx.doi.org/10.1111/j.1751-5823.2010.00108.x.
Full textGonzales Martínez, Rolando. "The Wage Curve, Once More with Feeling: Bayesian Model Averaging of Heckit Models." Econometric Research in Finance 3, no. 2 (October 15, 2018): 79–92. http://dx.doi.org/10.33119/erfin.2018.3.2.1.
Full textEmmert-Streib, Frank, and Matthias Dehmer. "Evaluation of Regression Models: Model Assessment, Model Selection and Generalization Error." Machine Learning and Knowledge Extraction 1, no. 1 (March 22, 2019): 521–51. http://dx.doi.org/10.3390/make1010032.
Full textYoung, Peg, and J. Keith Ord. "Model selection and estimation for technological growth curves." International Journal of Forecasting 5, no. 4 (January 1989): 501–13. http://dx.doi.org/10.1016/0169-2070(89)90005-8.
Full textFryer, R. J., A. F. Zuur, and N. Graham. "Using mixed models to combine smooth size-selection and catch-comparison curves over hauls." Canadian Journal of Fisheries and Aquatic Sciences 60, no. 4 (April 1, 2003): 448–59. http://dx.doi.org/10.1139/f03-029.
Full textWang, Ziming, Zexi Yang, Xiuzhen Wang, Qiang Yue, Zhendong Xia, and Hong Xiao. "Residence Time Distribution (RTD) Applications in Continuous Casting Tundish: A Review and New Perspectives." Metals 12, no. 8 (August 17, 2022): 1366. http://dx.doi.org/10.3390/met12081366.
Full textKULASEKERA, K. B., and JAVIER OLAYA. "VARIABLE SELECTION IN NONPARAMETRIC REGRESSION MODEL." International Journal of Reliability, Quality and Safety Engineering 11, no. 02 (June 2004): 141–61. http://dx.doi.org/10.1142/s0218539304001415.
Full textRalston, Stephen. "Size Selection of Snappers (Lutjanidae) by Hook and Line Gear." Canadian Journal of Fisheries and Aquatic Sciences 47, no. 4 (April 1, 1990): 696–700. http://dx.doi.org/10.1139/f90-078.
Full textKrenek, Sascha, Thomas U. Berendonk, and Thomas Petzoldt. "Thermal performance curves of Paramecium caudatum: A model selection approach." European Journal of Protistology 47, no. 2 (May 2011): 124–37. http://dx.doi.org/10.1016/j.ejop.2010.12.001.
Full textWinters, G. H., and J. P. Wheeler. "Direct and Indirect Estimation of Gillnet Selection Curves of Atlantic Herring (Clupea harengus harengus)." Canadian Journal of Fisheries and Aquatic Sciences 47, no. 3 (March 1, 1990): 460–70. http://dx.doi.org/10.1139/f90-050.
Full textDissertations / Theses on the topic "Model selection curves"
MONTEIRO, ANDRE MONTEIRO DALMEIDA. "NON-PARAMETRIC ESTIMATIONS OF INTEREST RATE CURVES : MODEL SELECTION CRITERION: MODEL SELECTION CRITERIONPERFORMANCE DETERMINANT FACTORS AND BID-ASK S." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2002. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=2684@1.
Full textEsta tese investiga a estimação de curvas de juros sob o ponto de vista de métodos não-paramétricos. O texto está dividido em dois blocos. O primeiro investiga a questão do critério utilizado para selecionar o método de melhor desempenho na tarefa de interpolar a curva de juros brasileira em uma dada amostra. Foi proposto um critério de seleção de método baseado em estratégias de re-amostragem do tipo leave-k-out cross validation, onde K k £ £ 1 e K é função do número de contratos observados a cada curva da amostra. Especificidades do problema reduzem o esforço computacional requerido, tornando o critério factível. A amostra tem freqüência diária: janeiro de 1997 a fevereiro de 2001. O critério proposto apontou o spline cúbico natural -utilizado com método de ajuste perfeito aos dados - como o método de melhor desempenho. Considerando a precisão de negociação, este spline mostrou-se não viesado. A análise quantitativa de seu desempenho identificou, contudo, heterocedasticidades nos erros simulados. A partir da especificação da variância condicional destes erros e de algumas hipóteses, foi proposto um esquema de intervalo de segurança para a estimação de taxas de juros pelo spline cúbico natural, empregado como método de ajuste perfeito aos dados. O backtest sugere que o esquema proposto é consistente, acomodando bem as hipóteses e aproximações envolvidas. O segundo bloco investiga a estimação da curva de juros norte-americana construída a partir dos contratos de swaps de taxas de juros dólar-Libor pela Máquina de Vetores Suporte (MVS), parte do corpo da Teoria do Aprendizado Estatístico. A pesquisa em MVS tem obtido importantes avanços teóricos, embora ainda sejam escassas as implementações em problemas reais de regressão. A MVS possui características atrativas para a modelagem de curva de juros: é capaz de introduzir já na estimação informações a priori sobre o formato da curva e sobre aspectos da formação das taxas e liquidez de cada um dos contratos a partir dos quais ela é construída. Estas últimas são quantificadas pelo bid-ask spread (BAS) de cada contrato. A formulação básica da MVS é alterada para assimilar diferentes valores do BAS sem que as propriedades dela sejam perdidas. É dada especial atenção ao levantamento de informação a priori para seleção dos parâmetros da MVS a partir do formato típico da curva. A amostra tem freqüência diária: março de 1997 a abril de 2001. Os desempenhos fora da amostra de diversas especificações da MVS foram confrontados com aqueles de outros métodos de estimação. A MVS foi o método que melhor controlou o trade- off entre viés e variância dos erros.
This thesis investigates interest rates curve estimation under non-parametric approach. The text is divided into two parts. The first one focus on which criterion to use to select the best performance method in the task of interpolating Brazilian interest rate curve. A selection criterion is proposed to measure out-of-sample performance by combining resample strategies leave-k-out cross validation applied upon the whole sample curves, where K k £ £ 1 and K is function of observed contract number in each curve. Some particularities reduce substantially the required computational effort, making the proposed criterion feasible. The data sample range is daily, from January 1997 to February 2001. The proposed criterion selected natural cubic spline, used as data perfect-fitting estimation method. Considering the trade rate precision, the spline is non-biased. However, quantitative analysis of performance determinant factors showed the existence of out-of-sample error heteroskedasticities. From a conditional variance specification of these errors, a security interval scheme is proposed for interest rate generated by perfect-fitting natural cubic spline. A backtest showed that the proposed security interval is consistent, accommodating the evolved assumptions and approximations. The second part estimate US free-for-floating interest rate swap contract curve by using Support Vector Machine (SVM), a method derived from Statistical Learning Theory. The SVM research has got important theoretical results, however the number of implementation on real regression problems is low. SVM has some attractive characteristics for interest rates curves modeling: it has the ability to introduce already in its estimation process a priori information about curve shape and about liquidity and price formation aspects of the contracts that generate the curve. The last information set is quantified by the bid-ask spread. The basic SVM formulation is changed in order to be able to incorporate the different values for bid-ask spreads, without losing its properties. Great attention is given to the question of how to extract a priori information from swap curve typical shape to be used in MVS parameter selection. The data sample range is daily, from March 1997 to April 2001. The out-of-sample performances of different SVM specifications are faced with others method performances. SVM got the better control of trade- off between bias and variance of out-of-sample errors.
Chia, Yan Wah. "Radiation from curved (conical) frequency selective surfaces." Thesis, Loughborough University, 1993. https://dspace.lboro.ac.uk/2134/7200.
Full textPaterson, Chay Giles Blair. "Minimal models of invasion and clonal selection in cancer." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/28986.
Full textWang, Wen-Chyi. "Regularized variable selection in proportional hazards model using area under receiver operating characteristic curve criterion." College Park, Md.: University of Maryland, 2009. http://hdl.handle.net/1903/9972.
Full textThesis research directed by: Dept. of Mathematics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
Flake, Darl D. II. "Separation of Points and Interval Estimation in Mixed Dose-Response Curves with Selective Component Labeling." DigitalCommons@USU, 2016. https://digitalcommons.usu.edu/etd/4697.
Full textBoruvka, Audrey. "Data-driven estimation for Aalen's additive risk model." Thesis, Kingston, Ont. : [s.n.], 2007. http://hdl.handle.net/1974/489.
Full textLee, Kyeong Eun. "Bayesian models for DNA microarray data analysis." Diss., Texas A&M University, 2005. http://hdl.handle.net/1969.1/2465.
Full textPlašil, Miroslav. "Empirické ověření nové Keynesiánské Philipsovy křivky v ČR." Doctoral thesis, Vysoká škola ekonomická v Praze, 2003. http://www.nusl.cz/ntk/nusl-77088.
Full textRückert, Nadja. "Studies on two specific inverse problems from imaging and finance." Doctoral thesis, Universitätsbibliothek Chemnitz, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-91587.
Full textSun, Limei. "Probabilistic model designs and selection curves of trawl gears /." 2001.
Find full textBooks on the topic "Model selection curves"
Weinberg, Jonathan M. Knowledge, Noise, and Curve-Fitting. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198724551.003.0016.
Full textBack, Kerry E. Information, Strategic Trading, and Liquidity. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190241148.003.0024.
Full textCrespo Miguel, Mario. Automatic corpus-based translation of a spanish framenet medical glossary. 2020th ed. Editorial Universidad de Sevilla, 2020. http://dx.doi.org/10.12795/9788447230051.
Full textAguilera-Cobos, Lorena, Rebeca Isabel-Gómez, and Juan Antonio Blasco-Amaro. Efectividad de la limitación de la movilidad en la evolución de la pandemia por Covid-19. AETSA Área de Evaluación de Tecnologías Sanitarias de Andalucía, Fundación Progreso y salud. Consejería de Salud y Familias. Junta de Andalucía, 2022. http://dx.doi.org/10.52766/pyui7071.
Full textBook chapters on the topic "Model selection curves"
Dasgupta, Ratan. "Model Selection and Validation in Agricultural Context: Extended Uniform Distribution and Some Characterization Theorems." In Growth Curve Models and Applications, 183–97. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63886-7_9.
Full textBab-Hadiashar, Alireza, and Niloofar Gheissari. "Model Selection for Range Segmentation of Curved Objects." In Lecture Notes in Computer Science, 83–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24670-1_7.
Full textLu, Zhenqiu, Zhiyong Zhang, and Allan Cohen. "Bayesian Methods and Model Selection for Latent Growth Curve Models with Missing Data." In Springer Proceedings in Mathematics & Statistics, 275–304. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-9348-8_18.
Full textTowill, D. R. "Selecting Learning Curve Models for Human Operator Performance." In Applications of Human Performance Models to System Design, 403–17. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4757-9244-7_29.
Full textFujikoshi, Yasunori. "Model Selection Criteria for Growth Curve Model with Hierachical Within-Individual Design Matrices." In New Developments in Psychometrics, 433–41. Tokyo: Springer Japan, 2003. http://dx.doi.org/10.1007/978-4-431-66996-8_49.
Full textHammes, Gabriel Almeida, Paula Medina Maçaira, and Fernando Luiz Cyrino Oliveira. "Data Analytics for the Selection of Wind Turbine Power Curve Models." In Operations Management for Social Good, 37–44. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23816-2_4.
Full textOung, O., A. Bezuijen, and F. A. Weststrate. "Development of Selective Pore Pressure Transducers to Measure In Situ Pc-S Curves During Model Tests." In Field Screening Europe 2001, 85–90. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0564-7_14.
Full textZhu, Hongxiao, and Dennis D. Cox. "A Functional Generalized Linear Model with Curve Selection in Cervical Pre-cancer Diagnosis Using Fluorescence Spectroscopy." In Institute of Mathematical Statistics Lecture Notes - Monograph Series, 173–89. Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2009. http://dx.doi.org/10.1214/09-lnms5711.
Full textAnderson, Raymond A. "Model Training." In Credit Intelligence & Modelling, 741–70. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192844194.003.0024.
Full textMusonge, P. "A Statistical Approach to Model Selection for Dynamic Adsorption Columns." In Advances in Wastewater Treatment II, 128–67. Materials Research Forum LLC, 2021. http://dx.doi.org/10.21741/9781644901397-5.
Full textConference papers on the topic "Model selection curves"
Arkalgud, Ravi, Andrew McDonald, and Ross Brackenridge. "AUTOMATED SELECTION OF INPUTS FOR LOG PREDICTION MODELS USING A NEW FEATURE SELECTION METHOD." In 2021 SPWLA 62nd Annual Logging Symposium Online. Society of Petrophysicists and Well Log Analysts, 2021. http://dx.doi.org/10.30632/spwla-2021-0091.
Full textXu, Zhi Gang, and JianFeng Li. "Human Head Aesthetic Design Using Hybrid Genetic Algorithm." In ASME 2002 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/detc2002/dac-34039.
Full textLu, Xiaohong, Haixing Zhang, Zhenyuan Jia, Yixuan Feng, and Steven Y. Liang. "A New Method for the Prediction of Micro-Milling Tool Breakage." In ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/msec2017-2999.
Full textAndertova´, Jana, and Frantisˇek Rieger. "Rheology and Rotational Rheometry of Concentrated Clay Based Ceramic Suspensions: Steps From Measured to Relevant Data." In ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-38487.
Full textCho, Heejin, Rogelio Luck, and Louay M. Chamra. "Power Generation and Heat Recovery Model of Reciprocating Internal Combustion Engines for CHP Applications." In ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-11634.
Full textQuinci, Gianluca, Nam Hoang Phan, and Fabrizio Paolacci. "On the Use of Artificial Neural Network Technique for Seismic Fragility Analysis of a Three-Dimensional Industrial Frame." In ASME 2022 Pressure Vessels & Piping Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/pvp2022-83874.
Full textBUENROSTRO, JAVIER, HYONNY KIM, ROBERT K. GOLDBERG, and TRENTON M. RICKS. "HYBRID EXPERIMENTAL AND NUMERICAL CHARACTERIZATION OF THE 3D RESPONSE OF WOVEN POLYMER MATRIX COMPOSITES." In Thirty-sixth Technical Conference. Destech Publications, Inc., 2021. http://dx.doi.org/10.12783/asc36/35940.
Full textTambat, Abhishek, Hung-Yun Lin, Ian Claydon, Ganesh Subbarayan, Dae-Young Jung, and Bahgat Sammakia. "Modeling Fracture in Dielectric Stacks due to Chip-Package Interaction: Impact of Dielectric Material Selection." In ASME 2011 Pacific Rim Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Systems. ASMEDC, 2011. http://dx.doi.org/10.1115/ipack2011-52237.
Full textVan Valkenburgh, Owen F., Thomas C. Ekstrom, Erica M. Goodman, Cameryn C. Leborte, Kevin M. Haaland, Nathan K. Yasuda, and Frank J. Shih. "Energy Absorption Characteristics of a Nested Curved Column Reinforced Elastomer Composite." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-12096.
Full textNeydorf, Rudolf. "“Cut-Glue” Approximation in Problems on Static and Dynamic Mathematical Model Development." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-37236.
Full textReports on the topic "Model selection curves"
Ungar, Eugene D., Montague W. Demment, Uri M. Peiper, Emilio A. Laca, and Mario Gutman. The Prediction of Daily Intake in Grazing Cattle Using Methodologies, Models and Experiments that Integrate Pasture Structure and Ingestive Behavior. United States Department of Agriculture, July 1994. http://dx.doi.org/10.32747/1994.7568789.bard.
Full textTayeb, Shahab. Taming the Data in the Internet of Vehicles. Mineta Transportation Institute, January 2022. http://dx.doi.org/10.31979/mti.2022.2014.
Full textCOMPARATIVE STUDY ON STABILITY OF WELDED AND HOT-ROLLED Q420 L300×30 COLUMNS. The Hong Kong Institute of Steel Construction, August 2022. http://dx.doi.org/10.18057/icass2020.p.255.
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