Добірка наукової літератури з теми "Hierarchical variables"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Hierarchical variables".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Hierarchical variables":
Shneiberg, I. Ya. "Hierarchical Sequences of Random Variables." Theory of Probability & Its Applications 31, no. 1 (March 1987): 137–41. http://dx.doi.org/10.1137/1131018.
Yoshimura, Masataka, and Kazuhiro Izui. "Smart Optimization of Machine Systems Using Hierarchical Genotype Representations." Journal of Mechanical Design 124, no. 3 (August 6, 2002): 375–84. http://dx.doi.org/10.1115/1.1486013.
Lopera Gonzalez, Luis I., and Oliver Amft. "Mining hierarchical relations in building management variables." Pervasive and Mobile Computing 26 (February 2016): 91–101. http://dx.doi.org/10.1016/j.pmcj.2015.10.009.
Camiz, S., J. Denimal, and V. Pillar. "Hierarchical factor classification of variables in ecology." Community Ecology 7, no. 2 (December 2006): 165–79. http://dx.doi.org/10.1556/comec.7.2006.2.4.
Wang, Jie, and Xiao Dong Zhu. "Analysis and Application of a Kind of Hierarchical Fuzzy Systems." Advanced Materials Research 219-220 (March 2011): 1097–100. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.1097.
Pulido-Valdeolivas, I., D. Gómez-Andrés, J. A. Martin, J. López, E. Gómez-Barrena, and E. Rausell. "P6.14 Hierarchical clustering of Gillette Gait Index variables." Clinical Neurophysiology 122 (June 2011): S87. http://dx.doi.org/10.1016/s1388-2457(11)60303-9.
Kristan, William B. "Sources and Expectations for Hierarchical Structure in Bird-habitat Associations." Condor 108, no. 1 (February 1, 2006): 5–12. http://dx.doi.org/10.1093/condor/108.1.5.
JIA, WEIJIA, and ZHIBIN SUN. "ON COMPUTATIONAL COMPLEXITY OF HIERARCHICAL OPTIMIZATION." International Journal of Foundations of Computer Science 13, no. 05 (October 2002): 667–70. http://dx.doi.org/10.1142/s0129054102001369.
Asfaw Dagne, Getachew. "Bayesian analysis of hierarchical poisson models with latent variables." Communications in Statistics - Theory and Methods 28, no. 1 (1999): 119–36. http://dx.doi.org/10.1080/03610929908832286.
Hajnal, Istvan, and Geert Loosveldt. "The Sensitivity of Hierarchical Clustering Solutions to Irrelevant Variables." Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique 50, no. 1 (March 1996): 56–70. http://dx.doi.org/10.1177/075910639605000105.
Дисертації з теми "Hierarchical variables":
Auyang, Arick Gin-Yu. "Robustness and hierarchical control of performance variables through coordination during human locomotion." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/42837.
RIGGI, DANIELE. "Mixture factor model for hierarchical data structure and applications to the italian educational school system." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2011. http://hdl.handle.net/10281/19465.
Chastaing, Gaëlle. "Indices de Sobol généralisés par variables dépendantes." Thesis, Grenoble, 2013. http://www.theses.fr/2013GRENM046.
A mathematical model aims at characterizing a complex system or process that is too expensive to experiment. However, in this model, often strongly non linear, input parameters can be affected by a large uncertainty including errors of measurement of lack of information. Global sensitivity analysis is a stochastic approach whose objective is to identify and to rank the input variables that drive the uncertainty of the model output. Through this analysis, it is then possible to reduce the model dimension and the variation in the output of the model. To reach this objective, the Sobol indices are commonly used. Based on the functional ANOVA decomposition of the output, also called Hoeffding decomposition, they stand on the assumption that the incomes are independent. Our contribution is on the extension of Sobol indices for models with non independent inputs. In one hand, we propose a generalized functional decomposition, where its components is subject to specific orthogonal constraints. This decomposition leads to the definition of generalized sensitivity indices able to quantify the dependent inputs' contribution to the model variability. On the other hand, we propose two numerical methods to estimate these constructed indices. The first one is well-fitted to models with independent pairs of dependent input variables. The method is performed by solving linear system involving suitable projection operators. The second method can be applied to more general models. It relies on the recursive construction of functional systems satisfying the orthogonality properties of summands of the generalized decomposition. In parallel, we illustrate the two methods on numerical examples to test the efficiency of the techniques
Pfister, Mark. "Distribution of a Sum of Random Variables when the Sample Size is a Poisson Distribution." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/etd/3459.
Hay, John Leslie. "Statistical modelling for non-Gaussian time series data with explanatory variables." Thesis, Queensland University of Technology, 1999.
Gebremeskel, Haftu Gebrehiwot. "Implementing hierarchical bayesian model to fertility data: the case of Ethiopia." Doctoral thesis, Università degli studi di Padova, 2016. http://hdl.handle.net/11577/3424458.
Background: L’Etiopia è una nazione divisa in 9 regioni amministrative (definite su base etnica) e due città. Si tratta di una nazione citata spesso come esempio di alta fecondità e rapida crescita demografica. Nonostante gli sforzi del governo, fecondità e cresita della popolazione rimangono elevati, specialmente a livello regionale. Pertanto, lo studio della fecondità in Etiopia e nelle sue regioni – caraterizzate da un’alta variabilità – è di vitale importanza. Un modo semplice di rilevare le diverse caratteristiche della distribuzione della feconditàè quello di costruire in modello adatto, specificando diverse funzioni matematiche. In questo senso, vale la pena concentrarsi sui tassi specifici di fecondità, i quali mostrano una precisa forma comune a tutte le popolazioni. Tuttavia, molti paesi mostrano una “simmetrizzazione” che molti modelli non riescono a cogliere adeguatamente. Pertanto, per cogliere questa la forma dei tassi specifici, sono stati utilizzati alcuni modelli parametrici ma l’uso di tali modelliè ancora molto limitato in Africa ed in Etiopia in particolare. Obiettivo: In questo lavoro si utilizza un nuovo modello per modellare la fecondità in Etiopia con quattro obiettivi specifici: (1). esaminare la forma dei tassi specifici per età dell’Etiopia a livello nazionale e regionale; (2). proporre un modello che colga al meglio le varie forme dei tassi specifici sia a livello nazionale che regionale. La performance del modello proposto verrà confrontata con quella di altri modelli esistenti; (3). adattare la funzione di fecondità proposta attraverso un modello gerarchico Bayesiano e mostrare che tale modelloè sufficientemente flessibile per stimare la fecondità delle singole regioni – dove le stime possono essere imprecise a causa di una bassa numerosità campionaria; (4). confrontare le stime ottenute con quelle fornite da metodi non gerarchici (massima verosimiglianza o Bayesiana semplice) Metodologia: In questo studio, proponiamo un modello a 4 parametri, la Normale Asimmetrica, per modellare i tassi specifici di fecondità. Si mostra che questo modello è sufficientemente flessibile per cogliere adeguatamente le forme dei tassi specifici a livello sia nazionale che regionale. Per valutare la performance del modello, si è condotta un’analisi preliminare confrontandolo con altri dieci modelli parametrici e non parametrici usati nella letteratura demografica: la funzione splie quadratica, la Cubic-Spline, i modelli di Coale e Trussel, Beta, Gamma, Hadwiger, polinomiale, Gompertz, Peristera-Kostaki e l’Adjustment Error Model. I modelli sono stati stimati usando i minimi quadrati non lineari (nls) e il Criterio d’Informazione di Akaike viene usato per determinarne la performance. Tuttavia, la stima per le singole regioni pu‘o risultare difficile in situazioni dove abbiamo un’alta variabilità della numerosità campionaria. Si propone, quindi di usare procedure gerarchiche che permettono di ottenere stime più affidabili rispetto ai modelli non gerarchici (“pooling” completo o “unpooling”) per l’analisi a livello regionale. In questo studia si formula un modello Bayesiano gerarchico ottenendo la distribuzione a posteriori dei parametri per i tassi di fecnodità specifici a livello regionale e relativa stima dell’incertezza. Altri metodi non gerarchici (Bayesiano semplice e massima verosimiglianza) vengono anch’essi usati per confronto. Gli algoritmi Gibbs Sampling e Metropolis-Hastings vengono usati per campionare dalla distribuzione a posteriori di ogni parametro. Anche il metodo del “Data Augmentation” viene utilizzato per ottenere le stime. La robustezza dei risultati viene controllata attraverso un’analisi di sensibilità e l’opportuna diagnostica della convergenza degli algoritmi viene riportata nel testo. In tutti i casi, si sono usate distribuzioni a priori non-informative. Risultati: I risutlati ottenuti dall’analisi preliminare mostrano che il modello Skew Normal ha il pi`u basso AIC nelle regioni Addis Ababa, Dire Dawa, Harari, Affar, Gambela, Benshangul-Gumuz e anche per le stime nazionali. Nelle altre regioni (Tigray, Oromiya, Amhara, Somali e SNNP) il modello Skew Normal non risulta il milgiore, ma comunque mostra un buon adattamento ai dati. Dunque, il modello Skew Normal risulta il migliore in 6 regioni su 11 e sui tassi specifici di tutto il paese. Conclusioni: Dunque, il modello Skew Normal risulta globalmente il migliore. Da questo risultato iniziale, siè partiti per costruire i modelli Gerachico Bayesiano, Bayesiano semplice e di massima verosimiglianza. Il risultato del confronto tra questi tre approcci è che il modello gerarchico fornisce stime più preciso rispetto agli altri.
Gardiner, Robert B. "The relationship between teacher qualifications and chemistry achievement in the context of other student and teacher/school variables : application of hierarchical linear modelling /." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0003/MQ42384.pdf.
Han, Gang. "Modeling the output from computer experiments having quantitative and qualitative input variables and its applications." Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1228326460.
Saves, Paul. "High dimensional multidisciplinary design optimization for eco-design aircraft." Electronic Thesis or Diss., Toulouse, ISAE, 2024. http://www.theses.fr/2024ESAE0002.
Nowadays, there has been significant and growing interest in improving the efficiency of vehicle design processes through the development of tools and techniques in the field of multidisciplinary design optimization (MDO). In fact, when optimizing both the aerodynamics and structures, one needs to consider the effect of the aerodynamic shape variables and structural sizing variables on the weight which also affects the fuel consumption. MDO arises as a powerful tool that can perform this trade-off automatically. The objective of the Ph. D project is to propose an efficient approach for solving an aero-structural wing optimization process at the conceptual design level. The latter is formulated as a constrained optimization problem that involves a large number of design variables (typically 700 variables). The targeted optimization approach is based on a sequential enrichment (typically efficient global optimization (EGO)), using an adaptive surrogate model. Kriging surrogate models are one of the most widely used in engineering problems to substitute time-consuming high fidelity models. EGO is a heuristic method, designed for the solution of global optimization problems that has performed well in terms of quality of the solution computed. However, like any other method for global optimization, EGO suffers from the curse of dimensionality, meaning that its performance is satisfactory on lower dimensional problems, but deteriorates as the dimensionality of the optimization search space increases. For realistic aircraft wing design problems, the typical size of the design variables exceeds 700 and, thus, trying to solve directly the problems using EGO is ruled out. In practical test cases, high dimensional MDO problems may possess a lower intrinsic dimensionality, which can be exploited for optimization. In this context, a feature mapping can then be used to map the original high dimensional design variable onto a sufficiently small design space. Most of the existing approaches in the literature use random linear mapping to reduce the dimension, sometimes active learning is used to build this linear embedding. Generalizations to non-linear subspaces are also proposed using the so-called variational autoencoder. For instance, a composition of Gaussian processes (GP), referred as deep GP, can be very useful. In this PhD thesis, we will investigate efficient parameterization tools to significantly reduce the number of design variables by using active learning technics. An extension of the method could be also proposed to handle mixed continuous and categorical inputs using some previous works on low dimensional problems. Practical implementations within the OpenMDAO framework (an open source MDO framework developed by NASA) are expected
Guin, Ophélie. "Méthodes bayésiennes semi-paramétriques d'extraction et de sélection de variables dans le cadre de la dendroclimatologie." Phd thesis, Université Paris Sud - Paris XI, 2011. http://tel.archives-ouvertes.fr/tel-00636704.
Книги з теми "Hierarchical variables":
Babeshko, Lyudmila, and Irina Orlova. Econometrics and econometric modeling in Excel and R. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1079837.
Hierarchical Modelling of Discrete Longitudinal Data: Applications of Markov Chain Monte Carlo. Munich, Germany: Herbert Witz Verlag, Wissenschaft, 1997.
Lee, Patricia, Donald Stewart, and Stephen Clift. Group Singing and Quality of Life. Edited by Brydie-Leigh Bartleet and Lee Higgins. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190219505.013.22.
Cemgil, A. Taylan, Simon Godsill, Paul Peeling, and Nick Whiteley. Bayesian statistical methods for audio and music processing. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.25.
Ślusarski, Marek. Metody i modele oceny jakości danych przestrzennych. Publishing House of the University of Agriculture in Krakow, 2017. http://dx.doi.org/10.15576/978-83-66602-30-4.
Sobczyk, Eugeniusz Jacek. Uciążliwość eksploatacji złóż węgla kamiennego wynikająca z warunków geologicznych i górniczych. Instytut Gospodarki Surowcami Mineralnymi i Energią PAN, 2022. http://dx.doi.org/10.33223/onermin/0222.
Частини книг з теми "Hierarchical variables":
Ilić, Marija D., and Shell Liu. "Structural Modeling and Control Design Using Interaction Variables." In Hierarchical Power Systems Control, 61–81. London: Springer London, 1996. http://dx.doi.org/10.1007/978-1-4471-3461-9_4.
Azbel, Mark Ya. "Non-Separable Variables: Hierarchical Quantization and Tunneling Resonances." In Quantum Coherence in Mesoscopic Systems, 597–605. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4899-3698-1_39.
Abdesselam, Rafik. "A Topological Clustering of Individuals." In Studies in Classification, Data Analysis, and Knowledge Organization, 1–9. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_1.
Bickel, Peter J., Ya’acov Ritov, and Alexandre B. Tsybakov. "Hierarchical selection of variables in sparse high-dimensional regression." In Institute of Mathematical Statistics Collections, 56–69. Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2010. http://dx.doi.org/10.1214/10-imscoll605.
Hengst, Bernhard. "Generating Hierarchical Structure in Reinforcement Learning from State Variables." In PRICAI 2000 Topics in Artificial Intelligence, 533–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44533-1_54.
da Silva, Ana Lorga, Helena Bacelar-Nicolau, and Gilbert Saporta. "Missing Data in Hierarchical Classification of Variables — a Simulation Study." In Classification, Clustering, and Data Analysis, 121–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56181-8_13.
Tanioka, Kensuke, and Hiroshi Yadohisa. "Three-Mode Hierarchical Subspace Clustering with Noise Variables and Occasions." In Studies in Classification, Data Analysis, and Knowledge Organization, 91–99. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01264-3_8.
Bonales Valencia, Joel, and Odette Virginia Delfín Ortega. "Hierarchical Structure of Variables in Export Agribusiness: The Case of Michoacan." In Lecture Notes in Business Information Processing, 144–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30433-0_15.
Ross, Amanda, and Victor L. Willson. "Hierarchical Multiple Regression Analysis Using at Least Two Sets of Variables (In Two Blocks)." In Basic and Advanced Statistical Tests, 61–74. Rotterdam: SensePublishers, 2017. http://dx.doi.org/10.1007/978-94-6351-086-8_10.
Zhao, Feixiang, Mingzhe Liu, Binyang Jia, Xin Jiang, and Jun Ren. "Key Variables Soft Measurement of Wastewater Treatment Process Based on Hierarchical Extreme Learning Machine." In Proceedings in Adaptation, Learning and Optimization, 45–54. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23307-5_6.
Тези доповідей конференцій з теми "Hierarchical variables":
de Soto, Adolfo R. "Hierarchical Linguistic Variables." In NAFIPS 2009 - 2009 Annual Meeting of the North American Fuzzy Information Processing Society. IEEE, 2009. http://dx.doi.org/10.1109/nafips.2009.5156432.
Yoshimura, Masataka, and Kazuhiro Izui. "Smart Optimization of Machine Systems Using Hierarchical Genotype Representations." In ASME 1999 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/detc99/dac-8631.
Yoshimura, Masataka, and Kazuhiro Izui. "Global System Optimization Using Hierarchical Genetic Algorithms Based on Decision-Making Components." In ASME 2001 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/detc2001/dac-21075.
Chen, Zhengming, Feng Xie, Jie Qiao, Zhifeng Hao, and Ruichu Cai. "Some General Identification Results for Linear Latent Hierarchical Causal Structure." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/397.
Jayasimha, D. N. "Partially shared variables and hierarchical shared memory multiprocessor architectures." In Eleventh Annual International Phoenix Conference on Computers and Communication [1992 Conference Proceedings]. IEEE, 1992. http://dx.doi.org/10.1109/pccc.1992.200539.
Silva, Eliana Costa e., Isabel Cristina Lopes, Aldina Correia, and A. Manuela Gonçalves. "Hierarchical clusters of phytoplankton variables in dammed water bodies." In APPLIED MATHEMATICS AND COMPUTER SCIENCE: Proceedings of the 1st International Conference on Applied Mathematics and Computer Science. Author(s), 2017. http://dx.doi.org/10.1063/1.4981978.
Tunc, Pervin. "ORGANIZATIONAL BEHAVIOR VARIABLES AND HIERARCHICAL REGRESSION ANALYSIS: A RESEARCH." In 5th SGEM International Multidisciplinary Scientific Conferences on SOCIAL SCIENCES and ARTS SGEM2018. STEF92 Technology, 2018. http://dx.doi.org/10.5593/sgemsocial2018/1.5/s05.094.
Liu, Yu, Xiaolei Yin, Paul Arendt, Wei Chen, and Hong-Zhong Huang. "An Extended Hierarchical Statistical Sensitivity Analysis Method for Multilevel Systems With Shared Variables." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87434.
Michalek, Jeremy J., and Panos Y. Papalambros. "BB-ATC: Analytical Target Cascading Using Branch and Bound for Mixed-Integer Nonlinear Programming." In ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/detc2006-99040.
Zhang, Xiao-Ling, Po Ting Lin, Hae Chang Gea, and Hong-Zhong Huang. "Bounded Target Cascading in Hierarchical Design Optimization." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48614.
Звіти організацій з теми "Hierarchical variables":
Chamberlain, Gary, and Guido Imbens. Hierarchical Bayes Models with Many Instrumental Variables. Cambridge, MA: National Bureau of Economic Research, September 1996. http://dx.doi.org/10.3386/t0204.
Zhang, Zhen. From CFA to SEM with Moderated Mediation in Mplus. Instats Inc., 2022. http://dx.doi.org/10.61700/e6lwwzg27rqsr469.
Zyphur, Michael. From CFA to SEM with Moderated Mediation in R. Instats Inc., 2022. http://dx.doi.org/10.61700/75sjvfs0ve1d4469.
Zyphur, Michael. From CFA to SEM with Moderated Mediation in Mplus. Instats Inc., 2022. http://dx.doi.org/10.61700/a6tru90pc9miu469.
Zyphur, Michael. From CFA to SEM with Moderated Mediation in R (Free On-Demand Seminar). Instats Inc., 2022. http://dx.doi.org/10.61700/xria1if8u3nip469.
Zyphur, Michael. Intermediate SEM in Stata: From CFA to SEM. Instats Inc., 2022. http://dx.doi.org/10.61700/9qo0ssbbzp4nl469.
Swan, Megan, and Christopher Calvo. Site characterization and change over time in semi-arid grassland and shrublands at three parks?Chaco Culture National Historic Park, Petrified Forest National Park, and Wupatki National Monument: Upland vegetation and soils monitoring 2007?2021. National Park Service, 2024. http://dx.doi.org/10.36967/2301582.
McPhedran, R., K. Patel, B. Toombs, P. Menon, M. Patel, J. Disson, K. Porter, A. John, and A. Rayner. Food allergen communication in businesses feasibility trial. Food Standards Agency, March 2021. http://dx.doi.org/10.46756/sci.fsa.tpf160.
Searcy, Stephen W., and Kalman Peleg. Adaptive Sorting of Fresh Produce. United States Department of Agriculture, August 1993. http://dx.doi.org/10.32747/1993.7568747.bard.