المؤلفون: Bruno Guilherme Christoff, Humberto Brito-Santana, Ramesh Talreja, Volnei Tita

المصدر: Composites Part C: Open Access, Vol 9, Iss , Pp 100303- (2022)

مصطلحات موضوعية: Finite Element Method, Virtual tensile testing, Asymptotic Homogenization Method, Multiscale embedded models, Materials of engineering and construction. Mechanics of materials, TA401-492

وصف الملف: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2666682022000676; https://doaj.org/toc/2666-6820

URL الوصول: https://doaj.org/article/db0a49b52bee4d1d98e1a81db18375a9

المؤلفون: Montoya, José A., Figueroa-Preciado, Gudelia

المصدر: Revista de la Facultad de Ciencias; Vol. 11 No. 2 (2022): Special Issue: Flat Likelihoods; 39-53 ; Revista de la Facultad de Ciencias; Vol. 11 Núm. 2 (2022): Número Especial: Verosimilitudes Planas; 39-53 ; 2357-5549 ; 0121-747X

مصطلحات موضوعية: Flat likelihood function, threshold parameter, embedded models, GEV distribution, likelihood contours, profile likelihood function, Función de verosimilitud plana, parámetro umbral, modelo empotrado, contornos de verosimilitud, función de verosimilitud perfil, Distribución de VEG

وصف الملف: application/pdf

Relation: https://revistas.unal.edu.co/index.php/rfc/article/view/98450/84228; Barnard, G. A. (1967). The use of the likelihood function in statistical practice. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1, 27--40.; Barnard, G. A. & Sprott D. A. (1983). Likelihood. In: Kotz S, Johnson NL (eds) Encyclopedia of statistical science, Vol 4. Wiley, New York, pp 639--644.; Barndorff-Nielsen, O. E. & Cox, D. R. (1994). Inference and asymptotics. Chapman & Hall/CRC. Boca Raton.; Berger, J. O., Liseo, B. & Wolpert, R. L. (1999). Integrated likelihood methods for eliminating nuisance parameters. Statistical Science, 14(1), 1-28.; Bolívar-Cimé, A., Díaz-Francés, E. & Ortega, J. (2015). Optimality of profile likelihood intervals for quantiles of extreme value distributions: application to environmental disasters. Hydrological Sciences Journal, 60(4), 651-670.; Breusch, T. S., Robertson, J. C. & Welsh, A. H. (1997). The emperor's new clothes: a critique of the multivariate t regression model. Statistica Neerlandica, 51(3), 269-286.; Catchpole, E. A. & Morgan, B. J. (1997). Detecting parameter redundancy. Biometrika, 84(1), 187-196.; Cheng, R. C. H. & Iles, T. C. (1990). Embedded models in three-parameter distributions and their estimation. Journal of the Royal Statistical Society. Series B (Methodological), 52(1), 135-149.; Cole, S. R., Chu, H. & Greenland, S. (2013). Maximum likelihood, profile likelihood, and penalized likelihood: a primer. American Journal of Epidemiology, 179(2), 252-260.; Cousineau, D., Goodman, V. W. & Shiffrin, R. M. (2002). Extending statistics of extremes to distributions varying in position and scale and the implications for race models. Journal of Mathematical Psychology, 46(4), 431-454.; De Haan, L. (1990). Fighting the arch-enemy with mathematics. Statistica neerlandica, 44(2), 45-68.; Deng, B., Jiang, D. & Gong, J. (2018). Is a three-parameter Weibull function really necessary for the characterization of the statistical variation of the strength of brittle ceramics?. Journal of the European Ceramic Society, 38(4), 2234-2242.; El Adlouni, S., Ouarda, T. B., Zhang, X., Roy, R. & Bobée, B. (2007). Generalized maximum likelihood estimators for the nonstationary generalized extreme value model. Water Resources Research, 43(3), W03410.; Elmahdy, E. E. & Aboutahoun, A. W.(2013). A new approach for parameter estimation of finite Weibull mixture distributions for reliability modeling. Applied Mathematical Modelling, 37(4), 1800-1810.; Elmahdy, E. E. (2015). A new approach for Weibull modeling for reliability life data analysis. Applied Mathematics and computation, 250, 708-720.; Farcomeni, A. & Tardella, L. (2012). Identifiability and inferential issues in capture-recapture experiments with heterogeneous detection probabilities. Electronic Journal of Statistics, 6, 2602-2626.; Frery, A. C., Cribari-Neto, F. & De Souza, M. O. (2004). Analysis of minute features in speckled imagery with maximum likelihood estimation. EURASIP Journal on Advances in Signal Processing, 2004(16), 2476-2491.; Ghosh, M., Datta, G. S., Kim, D. & Sweeting, T. J. (2006). Likelihood-based inference for the ratios of regression coefficients in linear models. Annals of the Institute of Statistical Mathematics, 58(3), 457-473.; Green, E. J., Roesch, F. A., Smith, A. F. & Strawderman, W. E. (1994). Bayesian Estimation for the Three-Parameter Weibull Distribution with Tree Diameter Data. Biometrics, 50(1), 254-269.; Harter, H. L. & Moore, A. H. (1966). Local-maximum-likelihood estimation of the parameters of three-parameter lognormal populations from complete and censored samples. Journal of the American Statistical Association, 61(315), 842-851.; Hirose, H. & Lai, T. L. (1997). Inference from grouped data in three-parameter Weibull models with applications to breakdown-voltage experiments. Technometrics, 39(2), 199-210.; Khan, H. M., Albatineh, A., Alshahrani, S., Jenkins, N. & Ahmed, N. U. (2011). Sensitivity analysis of predictive modeling for responses from the three-parameter Weibull model with a follow-up doubly censored sample of cancer patients. Computational Statistics & Data Analysis, 55(12), 3093-3103.; Kalbfleisch, J. G. (1985). Probability and Statistical Inference, Vol. 2. Springer-Verlag. New York.; Koutsoyiannis, D. (2004). Statistics of extremes and estimation of extreme rainfall: I. Theoretical investigation/Statistiques de valeurs extrêmes et estimation de précipitations extrêmes: I. Recherche théorique. Hydrological Sciences Journal, 49(4).; Kreutz, C., Raue, A., Kaschek, D. & Timmer, J. (2013). Profile likelihood in systems biology. The FEBS journal, 280(11), 2564-2571.; Li, R. & Sudjianto, A. (2005). Analysis of computer experiments using penalized likelihood in Gaussian Kriging models. Technometrics, 47(2), 111-120.; Lima, V. M. & Cribari-Neto, F. (2019). Penalized maximum likelihood estimation in the modified extended Weibull distribution. Communications in Statistics-Simulation and Computation, 48(2), 334-349.; Liu, S., Wu, H. & Meeker, W. Q. (2015). Understanding and addressing the unbounded ``likelihood'' problem. The American Statistician}, 69(3), 191-200.; Martins, E. S. & Stedinger, J. R. (2000). Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data. Water Resources Research, 36(3), 737-744.; Martins, E. S. & Stedinger, J. R. (2001). Generalized maximum likelihood Pareto-Poisson estimators for partial duration series. Water Resources Research, 37(10), 2551-2557.; Montoya, J. A. (2008). La verosimilitud perfil en la Inferencia Estadística. Centro de Investigación en Matemáticas, A. C., Guanajuato, Gto., México.; Montoya, J. A., Díaz-Francés, E. & Sprott, D.A. (2009). On a criticism of the profile likelihood function. Statistical Papers, 50(1), 195-202.; Murphy, S. A. & Van Der Vaart, A. W. (2000). On profile likelihood. Journal of the American Statistical Association, 95(450), 449-465.; Pawitan, Y. (2001). In All Likelihood: Statistical Modelling and Inference Using Likelihood. Oxford University Press. New York.; Pewsey, A. (2000). Problems of inference for Azzalini's skewnormal distribution. Journal of Applied Statistics, 27(7), 859-870.; Raue, A., Kreutz, C., Maiwald, T., Bachmann, J., Schilling, M., Klingmüller, U. & Timmer, J. (2009). Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood. Bioinformatics, 25(15), 1923-1929.; Serfling, R. J. (2002). Approximation Theorems of Mathematical Statistics. John Wiley & Sons. New York.; Silva, H. P. T. N. & Peiris, T. S. G.(2017). Statistical modeling of weekly rainfall: a case study in Colombo city in Sri Lanka. Proceedings of the Engineering Research Conference (MERCon), Moratuwa, IEEE, 241-246.; Smith, R. L. & Naylor, J. C. (1987). A comparison of maximum likelihood and Bayesian estimators for the three parameter Weibull distribution. Journal of the Royal Statistical Society, 36(3), 358--369.; Sprott, D. A. (2000). Statistical inference in science. Springer-Verlag. New York.; Sundberg, R. (2010). Flat and multimodal likelihoods and model lack of fit in curved exponential families. Scandinavian Journal of Statistics, 37(4), 632-643.; Tsionas, E. G. (2001). Likelihood and Posterior Shapes in Johnson's System. Sankhya: The Indian Journal of Statistics, Series B, 63(1), 3-9.; Tumlinson, S. E. (2015). On the non-existence of maximum likelihood estimates for the extended exponential power distribution and its generalizations. Statistics & Probability Letters, 107, 111-114.; https://revistas.unal.edu.co/index.php/rfc/article/view/98450

الاتاحة: https://revistas.unal.edu.co/index.php/rfc/article/view/98450

المؤلفون: Montoya, José A.

المصدر: Revista de la Facultad de Ciencias; Vol. 11 No. 2 (2022): Special Issue: Flat Likelihoods; 8-24 ; Revista de la Facultad de Ciencias; Vol. 11 Núm. 2 (2022): Número Especial: Verosimilitudes Planas; 8-24 ; 2357-5549 ; 0121-747X

مصطلحات موضوعية: Verosimilitud plana, parámetro umbral, modelo empotrado, distribución Poisson, contornos de verosimilitud, verosimilitud perfil, función de verosimilitud perfil, Flat likelihood, threshold parameter, embedded models, Poisson distribution, likelihood contours, profile likelihood function

وصف الملف: application/pdf

Relation: https://revistas.unal.edu.co/index.php/rfc/article/view/97888/85205; Aitkin, M. & Stasinopoulos, M. (1989). Likelihood analysis of a binomial sample size problem. Contributions to Probability and Statistics (pp. 399-411). Springer. New York.; Barndorff-Nielsen, O. E. & Cox, D. R. (1994). Inference and asymptotics. Chapman & Hall/CRC. Boca Raton.; Berger, J. O., Liseo, B. & Wolpert, R. L. (1999). Integrated likelihood methods for eliminating nuisance parameters. Statistical Science, 14(1), 1-28.; Breusch, T. S., Robertson, J. C. & Welsh, A. H. (1997). The emperor's new clothes: a critique of the multivariate t regression model. Statistica Neerlandica, 51(3), 269-286.; Carroll, R. J. & Lombard, F. (1985). A note on N estimators for the binomial distribution. Journal of the American Statistical Association, 80(390), 423-426.; Casella, G. (1986). Stabilizing binomial n estimators. Journal of the American Statistical Association, 81(393), 172-175.; Catchpole, E. A.& Morgan, B. J. (1997). Detecting parameter redundancy. Biometrika, 84(1), 187-196.; Cheng, R. C. H. & Iles, T. C. (1990). Embedded models in three-parameter distributions and their estimation. Journal of the Royal Statistical Society. Series B (Methodological), 52(1), 135-149.; Cole, S. R., Chu, H. & Greenland, S. (2013). Maximum likelihood, profile likelihood, and penalized likelihood: a primer. American Journal of Epidemiology, 179(2), 252-260.; DasGupta, A. & Rubin, H. (2005). Estimation of binomial parameters when both n, p are unknown. Journal of Statistical Planning and Inference, 130(1-2), 391-404.; Draper, N.; Guttman, I. (1971), Bayesian estimation of the binomial parameter. Technometrics, 13(3), 667-673.; El Adlouni, S., Ouarda, T. B., Zhang, X., Roy, R. & Bobée, B. (2007). Generalized maximum likelihood estimators for the nonstationary generalized extreme value model. Water Resources Research, 43(3), W03410.; Farcomeni, A. & Tardella, L. (2012). Identifiability and inferential issues in capture-recapture experiments with heterogeneous detection probabilities. Electronic Journal of Statistics, 6, 2602-2626.; Fisher, R. A. (1941). The negative binomial distribution. Annals of Eugenics, 11(1), 182-187.; Frery, A. C., Cribari-Neto, F.& De Souza, M. O. (2004). Analysis of minute features in speckled imagery with maximum likelihood estimation. EURASIP Journal on Advances in Signal Processing, 2004(16), 2476-2491.; Ghosh, M., Datta, G. S., Kim, D. & Sweeting, T. J. (2006). Likelihood-based inference for the ratios of regression coeficients in linear models. Annals of the Institute of Statistical Mathematics, 58(3), 457-473.; Gupta, A. K., Nguyen, T. T. &Wang, Y. (1999). On maximum likelihood estimation of the binomial parameter n. Canadian Journal of Statistics, 27(3), 599-606.; Hall, P. (1994). On the erratic behavior of estimators of N in the binomial N, p distribution. Journal of the American Statistical Association, 89(425), 344-352.; Harter, H. L. & Moore, A. H. (1966). Local-maximum-likelihood estimation of the parameters of three-parameter lognormal populations from complete and censored samples. Journal of the American Statistical Association, 61(315), 842-851.; Kahn, W. D. (1987). A cautionary note for Bayesian estimation of the binomial parameter n. The American Statistician, 41(1), 38-40.; Kalbfleisch, J. G. (1985). Probability and Statistical Inference, Vol. 2. Springer-Verlag. New York.; Kreutz, C., Raue, A., Kaschek, D. & Timmer, J. (2013). Prole likelihood in systems biology. The FEBS Journal, 280(11), 2564-2571.; Li, R. & Sudjianto, A. (2005). Analysis of computer experiments using penalized likelihood in Gaussian Kriging models. Technometrics, 47(2), 111-120.; Lima, V. M. & Cribari-Neto, F. (2019). Penalized maximum likelihood estimation in the modified extended Weibull distribution. Communications in Statistics-Simulation and Computation, 48(2), 334-349.; Lindsey, J. K. (1996). Parametric statistical inference. Oxford University Press. New York.; Liu, S., Wu, H. & Meeker, W. Q. (2015). Understanding and addressing the unbounded likelihood problem. The American Statistician, 69(3), 191-200.; Martins, E. S. & Stedinger, J. R. (2000). Generalized maximum-likelihood generalized extreme value quantile estimators for hydrologic data. Water Resources Research, 36(3), 737-744.; Martins, E. S. & Stedinger, J. R. (2001). Generalized maximum likelihood Pareto-Poisson estimators for partial duration series. Water Resources Research, 37(10), 2551-2557.; Montoya, J. A., Díaz-Francés, E. & Sprott, D. A. (2009). On a criticism of the progile likelihood function. Statistical Papers, 50(1), 195-202.; Moran, P. A. P. (1951). A mathematical theory of animal trapping. Biometrika, 38(3-4), 307-311.; Murphy, S. A. & Van Der Vaart, A. W. (2000). On profile likelihood. Journal of the American Statistical Association, 95(450), 449-465.; Olkin, I., Petkau, A. J. & Zidek, J. V. (1981). A comparison of n estimators for the binomial distribution. Journal of the American Statistical Association, 76(375), 637-642.; Pawitan, Y. (2001). In All Likelihood: Statistical Modelling and Inference Using Likelihood. Oxford University Press. New York.; Pewsey, A. (2000). Problems of inference for Azzalini's skewnormal distribution. Journal of Applied Statistics, 27(7), 859-870.; Raftery, A. E. (1988). Inference for the binomial N parameter: A hierarchical Bayes approach. Biometrika, 75(2), 223-228.; Raue, A., Kreutz, C., Maiwald, T., Bachmann, J., Schilling, M., Klingmüller, U. & Timmer, J. (2009). Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood. Bioinformatics, 25(15), 1923-1929.; Serfling, R. J. (2002). Approximation Theorems of Mathematical Statistics. John Wiley & Sons. New York.; Sprott, D. A. (2000). Statistical inference in science. Springer-Verlag. New York.; Sundberg, R. (2010). Flat and multimodal likelihoods and model lack of fit in curved exponential families. Scandinavian Journal of Statistics, 37(4), 632-643.; Tsionas, E. G. (2001). Likelihood and Posterior Shapes in Johnson's System. Sankhya: The Indian Journal of Statistics, Series B, 63(1), 3-9.; Tumlinson, S. E. (2015). On the non-existence of maximum likelihood estimates for the extended exponential power distribution and its generalizations. Statistics & Probability Letters, 107, 111-114; https://revistas.unal.edu.co/index.php/rfc/article/view/97888

الاتاحة: https://revistas.unal.edu.co/index.php/rfc/article/view/97888

المؤلفون: Cheng, R. C. H., Liu, W. B.

المصدر: Journal of the Royal Statistical Society. Series B (Methodological), 1997 Jan 01. 59(1), 137-145.

URL الوصول: https://www.jstor.org/stable/2345920

المؤلفون: Denkena, Berend, Dittrich, M.-A., Keunecke, L., Wilmsmeier, S.

المصدر: Production Engineering 14 (2020)

مصطلحات موضوعية: Failure duration, Manufacturing system, Production planning, Scheduling, Data mining, Manufacture, Production control, Continuous modelling, Cross industry, Embedded models, Failure classification, High potential, Multiple distribution, Production Scheduling, Machine tools, ddc:620

Relation: ESSN:1863-7353; http://dx.doi.org/10.15488/10739; https://www.repo.uni-hannover.de/handle/123456789/10817

الاتاحة: https://www.repo.uni-hannover.de/handle/123456789/10817
https://doi.org/10.15488/10739

المؤلفون: Marianna Puccia, Antonino Spada, Giuseppe Giambanco

المساهمون: Marianna Puccia, Antonino Spada, Giuseppe Giambanco

مصطلحات موضوعية: Interphase elements, strain localization, A-FEM, embedded models

Relation: ispartofbook:Book of Abstracts ECCOMAS Congress 2022; The 8th European Congress on Computational Methods in Applied Sciences and Engineering ECCOMAS Congress 2022; numberofpages:1; https://hdl.handle.net/10447/576434

الاتاحة: https://hdl.handle.net/10447/576434

المؤلفون: Cheng, Russell, author

المصدر: Non-Standard Parametric Statistical Inference, 2017, ill.

الاتاحة: https://doi.org/10.1093/oso/9780198505044.003.0005

المؤلفون: Cheng, Russell, author

المصدر: Non-Standard Parametric Statistical Inference, 2017, ill.

الاتاحة: https://doi.org/10.1093/oso/9780198505044.003.0006

المؤلفون: Cheng, Russell, author

المصدر: Non-Standard Parametric Statistical Inference, 2017, ill.

الاتاحة: https://doi.org/10.1093/oso/9780198505044.003.0009

المؤلفون: Alizon, Samuel, van Baalen, Minus

المصدر: The American Naturalist, 2008 Oct . 172(4), E150-E168.

Relation: Corresponding author; e‐mail: samuel.alizon@env.ethz.ch . † E‐mail: minus.van.baalen@ens.fr .

URL الوصول: https://www.jstor.org/stable/10.1086/590958

المؤلفون: Berend Denkena, Marc-André Dittrich, Sören Wilmsmeier, L. Keunecke

المصدر: Production Engineering 14 (2020)

مصطلحات موضوعية: 0209 industrial biotechnology, business.product_category, Production control, Computer science, Process (engineering), media_common.quotation_subject, Dewey Decimal Classification::600 | Technik::620 | Ingenieurwissenschaften und Maschinenbau, 02 engineering and technology, High potential, Industrial and Manufacturing Engineering, 020901 industrial engineering & automation, 0203 mechanical engineering, Failure classification, Production (economics), Multiple distribution, Quality (business), Production Scheduling, Duration (project management), Data mining, media_common, Manufacturing system, Machine tools, Continuous modelling, Scheduling, Mechanical Engineering, Manufacture, Production planning, Reliability engineering, Machine tool, 020303 mechanical engineering & transports, Embedded models, ddc:620, business, Failure duration, Cross industry

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a899fdf8c26ff05e8b5cc487ce5199b7
https://doi.org/10.1007/s11740-020-00955-y

المؤلفون: Cheng, R. C. H., Iles, T. C.

المصدر: Journal of the Royal Statistical Society. Series B (Methodological), 1990 Jan 01. 52(1), 135-149.

URL الوصول: https://www.jstor.org/stable/2345655

المؤلفون: Cheng, R. C. H., Traylor, L.

المصدر: Journal of the Royal Statistical Society. Series B (Methodological), 1995 Jan 01. 57(1), 3-44.

URL الوصول: https://www.jstor.org/stable/2346086

المؤلفون: Cheng, R. C. H., Evans, B. E., Iles, T. C.

المصدر: Journal of the Royal Statistical Society. Series B (Methodological), 1992 Jan 01. 54(3), 877-888.

URL الوصول: https://www.jstor.org/stable/2345866

المؤلفون: Πυλαδαρινός, Αλέξανδρος, Pyladarinos, Alexandros

مصطلحات موضوعية: Μηχανική Μάθηση, Μαθηματική προτυποποίηση, Μύλος άλεσης, Νευρωνικά δίκτυα, Cement grinding, Machine learning, Embedded models, Process modelling, Ball mill

وصف الملف: application/pdf

Relation: https://dspace.lib.ntua.gr/xmlui/handle/123456789/55144; http://dx.doi.org/10.26240/heal.ntua.22842

الاتاحة: https://dspace.lib.ntua.gr/xmlui/handle/123456789/55144
https://doi.org/10.26240/heal.ntua.22842