Literatura académica sobre el tema "Multivariate depth"
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Artículos de revistas sobre el tema "Multivariate depth"
Bern y Eppstein. "Multivariate Regression Depth". Discrete & Computational Geometry 28, n.º 1 (julio de 2002): 1–17. http://dx.doi.org/10.1007/s00454-001-0092-1.
Texto completoClaeskens, Gerda, Mia Hubert, Leen Slaets y Kaveh Vakili. "Multivariate Functional Halfspace Depth". Journal of the American Statistical Association 109, n.º 505 (2 de enero de 2014): 411–23. http://dx.doi.org/10.1080/01621459.2013.856795.
Texto completoVencálek, Ondřej. "Depth-based Classification for Multivariate Data". Austrian Journal of Statistics 46, n.º 3-4 (12 de abril de 2017): 117–28. http://dx.doi.org/10.17713/ajs.v46i3-4.677.
Texto completoIeva, Francesca y Anna M. Paganoni. "Depth Measures for Multivariate Functional Data". Communications in Statistics - Theory and Methods 42, n.º 7 (abril de 2013): 1265–76. http://dx.doi.org/10.1080/03610926.2012.746368.
Texto completoCascos, Ignacio y Ilya Molchanov. "Multivariate risks and depth-trimmed regions". Finance and Stochastics 11, n.º 3 (15 de mayo de 2007): 373–97. http://dx.doi.org/10.1007/s00780-007-0043-7.
Texto completoTat, Samaneh y Mohammad Reza Faridrohani. "Predicting depth value of the future depth-based multivariate record". Communications for Statistical Applications and Methods 30, n.º 5 (30 de septiembre de 2023): 453–65. http://dx.doi.org/10.29220/csam.2023.30.5.453.
Texto completoMuthukrishnan, R. y Surabhi S. Nair. "Computing Robust Measure of Location on Multivariate Statistical Data Using Euclidean Depth Procedures". Indian Journal Of Science And Technology 16, n.º 26 (23 de julio de 2023): 1927–34. http://dx.doi.org/10.17485/ijst/v16i26.847.
Texto completoHe, Xuming y Gang Wang. "Convergence of depth contours for multivariate datasets". Annals of Statistics 25, n.º 2 (abril de 1997): 495–504. http://dx.doi.org/10.1214/aos/1031833661.
Texto completoTsao, Min. "An empirical depth function for multivariate data". Statistics & Probability Letters 83, n.º 1 (enero de 2013): 213–18. http://dx.doi.org/10.1016/j.spl.2012.09.007.
Texto completoRomanazzi, Mario. "Data depth, random simplices and multivariate dispersion". Statistics & Probability Letters 79, n.º 12 (junio de 2009): 1473–79. http://dx.doi.org/10.1016/j.spl.2009.03.022.
Texto completoTesis sobre el tema "Multivariate depth"
Beltran, Luis. "NONPARAMETRIC MULTIVARIATE STATISTICAL PROCESS CONTROL USING PRINCIPAL COMPONENT ANALYSIS AND SIMPLICIAL DEPTH". Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4080.
Texto completoPh.D.
Department of Industrial Engineering and Management Systems
Engineering and Computer Science
Industrial Engineering and Management Systems
Lee, Joanna L. S. "Time-of-flight secondary ion mass spectrometry - fundamental issues for quantitative measurements and multivariate data analysis". Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:f0e4b8ff-f563-429e-9e71-9c277a5139c4.
Texto completoArmaut, Elisabeth. "Estimation d'ensembles de niveau d'une fonction de profondeur pour des données fonctionnelles. Applications au clustering et à la théorie du risque". Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ5021.
Texto completoStatistical depth functions play a fundamental role in analyzing and characterizing complex data structures. Depth functions provide a measure of centrality or outlyingness for individual observations or entire datasets, aiding in the understanding of their relative positions and underlying distributions. The concepts related to depth, as found in the literature, originate from the notion of Tukey's depth, also known as the median depth. This concept was introduced by the statistician John W. Tukey in his article titled "Mathematics and the Picturing of Data," published in 1975 [170]. The fundamental idea underlying Tukey's depth is to generalize the univariate median of a one-dimensional dataset in higher dimension. First, our interest focuses on multivariate depths followed by functional depths, both of which we build an overall review within Chapter 1. In the second part of this thesis, i.e. in Chapter 2, we undertake a rigorous study of multivariate depth-level sets and establish several analytical and statistical properties. First, we show that, when the underlying multivariate depth is smooth enough, then the symmetric difference between the estimated depth-level set and its theoretical counterpart converges to zero in terms of the d-dimensional volume and of the probability under the unknown distribution. Apart from these contributions, the novelty of Chapter 2 is the introduction and study of a depth-based risk measure called the Covariate-Conditional- Tail-Expectation (CCTE), within a risk theory setup. Roughly, the CCTE aims at computing an average cost knowing that at least one of the risk factors at hand is 'high' in some direction. The latter risk area is modelled by a level-set of low depth value. In contrast to risk measures based on distribution tails, our definition of CCTE is direction-free, owing to the involvement of depth level sets. We establish that, as the sample size goes to infinity the empirical depth-based CCTE is consistent for its theoretical version. We demonstrate consistency and provide rates of convergence for the depth- CCTE, for fixed levels of risk as well as when the risk level goes to zero as the sample size goes to infinity. In this last case of study, we also analyze the behavior of the original CCTE definition based on a distribution function, a case that was not studied in [56]. On top of several simulations performed on the CCTE, we illustrate its usefulness on environmental data.The final part of this thesis, Chapter 3, wraps up our work in which we contribute to defining a new type of depth for functional data based on functional principal component analysis. This includes using a generic multivariate depth. In this view, we use the well known Karhunen-Loève decomposition as a tool to project a centered square-integrable random process along some finite linear combination of orthogonal functions called the principal components. To the best of our knowledge, this is a novel approach in the functional depth literature. In this extent, we involve a multivariate depth function for the vector of the projected principal components. Naturally, we provide an estimator of our functional depth for which we demonstrate uniform consistency with a rate of convergence. We complement our study with several simulations and real data applications to functional classification, where our new depth equals or outperforms most of conventional competitors
Baffoe, Nana Ama Appiaa. "Diagnostic Tools for Forecast Ensembles". Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1522964882574611.
Texto completoCantu, Alma. "Proposition de modes de visualisation et d'interaction innovants pour les grandes masses de données et/ou les données structurées complexes en prenant en compte les limitations perceptives des utilisateurs". Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2018. http://www.theses.fr/2018IMTA0068/document.
Texto completoAs a result of the improvement of data capture and storage, recent years have seen the amount of data to be processed increase dramatically. Many studies, ranging from automatic processing to information visualization, have been performed, but some areas are still too specific to take advantage of. This is the case of ELectromagnetic INTelligence(ELINT). This domain does not only deal with a huge amount of data but also has to handle complex data and usage as well as populations of users with less and less experience. In this thesis we focus on the use of existing and new technologies applied to visualization to propose solutions to the combination of issues such as huge amount and complex data. We begin by presenting an analysis of the ELINT field which made it possible to extract the issues that it must faces. Then, we focus on the visual solutions handling the combinations of such issues but the existing work do not contain directly such solutions. Therefore, we focus on the description of visual issues and propose a characterization of these issues. This characterization allows us to describe the existing representations and to build a recommendation tool based on how the existing work solves the issues. Finally, we focus on identifying new metaphors to complete the existing work and propose an immersive representation to solve the issues of ELINT. These contributions make it possible to analyze and use the existing and deepen the use of immersive representations for the visualization of information
Dogra, Jody A. Busch Kenneth W. Busch Marianna A. "Multivariate analyses of near-infrared and UV spectral data". Waco, Tex. : Baylor University, 2009. http://hdl.handle.net/2104/5347.
Texto completoCarvalho, Aline Roberta de. "Atributos do solo associados às variações na vegetação em fragmento de cerrado, Assis, SP". Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/11/11140/tde-09022009-152200/.
Texto completoThe objective of the present work is to analyze correlation the correlation between environmental variables and tree species distribution. The study was developed in a permanent plot of 320 x 320 m, in a cerrado fragment, located at Assis Ecological Station, Assis County, São Paulo. Soil samples were collected at five sites in a pedosequence, and submitted to chemical, physical and micromorphological analysis. The vegetation was sampled within 314 m2 area around each site where the soil sample was collected. All alive trees which diameter at breast height was equal or higher than 4,8 cm and perimeter at the breast height was equal or superior to 15 cm, were considered. To analyze the data bank, three multivariate techniques analysis were used: principal component analysis (PCA), to environment variables; certificated correspondent analysis (DCA), to floristic variables; and canonic correspondent analysis (CCA), to verify a possible association between two variables. The principal component analysis demonstrated that the majority of variables presented similar correlation within superficial layers (0-20 and 20-60 cm). This trend was not the same for the other layers (60-80 and > 80 cm), suggesting more changes in soil profile with soil depth. The correspondence canonic analysis showed to be reliable to demonstrate standard distribution of species in relation to environmental variables for fragment, characterized by soil physical and chemical attributes. But, the key character was the soil water regime, suggesting that the water availability had strong influence over species distributions.
Salawu, Emmanuel Oluwatobi. "Spatiotemporal Variations in Coexisting Multiple Causes of Death and the Associated Factors". ScholarWorks, 2018. https://scholarworks.waldenu.edu/dissertations/6108.
Texto completoChih-TingHsieh y 謝芝庭. "Multivariate Process Capability Index Based on Data Depth Concept". Thesis, 2017. http://ndltd.ncl.edu.tw/handle/2cw2p6.
Texto completo國立成功大學
統計學系
105
Generally, an industrial product has more than one quality characteristic. In order to establish performance measures for evaluating the capability of a multivariate manufacturing process, several multivariate process capability indices have been developed in the past. Most of the proposed in the literature MPCIs are defined under an assumption, that process quality characteristics are normally distributed. However, this assumption may not hold in practice In this research, based on the data depth concept, we proposed two multivariate process capability indices, which could be used regardless on data distribution. Finally, simulation results show that our proposed indices outperform than existing model. A numerical example further demonstrate the usefulness of the proposed indices.
Kong, Linglong. "On Multivariate Quantile Regression: Directional Approach and Application with Growth Charts". Phd thesis, 2009. http://hdl.handle.net/10048/462.
Texto completoTitle from pdf file main screen (viewed on July 21, 2009). "A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Statistics, Department of Mathematical and Statistical Sciences, University of Alberta." Includes bibliographical references.
Libros sobre el tema "Multivariate depth"
Mosler, Karl. Multivariate Dispersion, Central Regions, and Depth. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4613-0045-8.
Texto completoY, Liu Regina, Serfling Robert J y Souvaine Diane L, eds. Data depth: Robust multivariate analysis, computational geometry, and applications. New York: American Mathematical Society, 2006.
Buscar texto completoKosiorowski, Daniel. Statystyczne funkcje głębi w odpornej analizie ekonomicznej: The statistical functions of depth in robust economic analysis. Kraków: Wydawnictwo Uniwersytetu Ekonomicznego w Krakowie, 2012.
Buscar texto completoMultivariate Dispersion, Central Regions, and Depth. Springer, 2002.
Buscar texto completoMosler, Karl. Multivariate Dispersion, Central Regions, and Depth. Springer, 2011.
Buscar texto completoMosler, Karl. Multivariate Dispersion, Central Regions, and Depth: The Lift Zonoid Approach. Springer London, Limited, 2012.
Buscar texto completoBaillo, Amparo, Antonio Cuevas y Ricardo Fraiman. Classification methods for functional data. Editado por Frédéric Ferraty y Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.10.
Texto completoMüller, Wolfgang C. y Paul W. Thurner, eds. The Politics of Nuclear Energy in Western Europe. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198747031.001.0001.
Texto completoSobczyk, 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.
Texto completoCapítulos de libros sobre el tema "Multivariate depth"
Mosler, Karl. "Data depth". En Multivariate Dispersion, Central Regions, and Depth, 105–31. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4613-0045-8_4.
Texto completoMosler, Karl. "Depth of hyperplanes". En Multivariate Dispersion, Central Regions, and Depth, 165–79. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4613-0045-8_6.
Texto completoSerfling, Robert. "Depth functions in nonparametric multivariate inference". En DIMACS Series in Discrete Mathematics and Theoretical Computer Science, 1–16. Providence, Rhode Island: American Mathematical Society, 2006. http://dx.doi.org/10.1090/dimacs/072/01.
Texto completoFraiman, Ricardo, Regina Y. Liu y Jean Meloche. "Multivariate density estimation by probing depth". En Institute of Mathematical Statistics Lecture Notes - Monograph Series, 415–30. Hayward, CA: Institute of Mathematical Statistics, 1997. http://dx.doi.org/10.1214/lnms/1215454155.
Texto completoMosler, Karl. "Introduction". En Multivariate Dispersion, Central Regions, and Depth, 1–24. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4613-0045-8_1.
Texto completoMosler, Karl. "Zonoids and lift zonoids". En Multivariate Dispersion, Central Regions, and Depth, 25–78. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4613-0045-8_2.
Texto completoMosler, Karl. "Central regions". En Multivariate Dispersion, Central Regions, and Depth, 79–104. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4613-0045-8_3.
Texto completoDykerhoff, Rainer. "Inference based on data depth by Rainer Dyckerhoff". En Multivariate Dispersion, Central Regions, and Depth, 133–63. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4613-0045-8_5.
Texto completoMosler, Karl. "Volume statistics". En Multivariate Dispersion, Central Regions, and Depth, 181–206. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4613-0045-8_7.
Texto completoMosler, Karl. "Orderings and indices of dispersion". En Multivariate Dispersion, Central Regions, and Depth, 207–28. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4613-0045-8_8.
Texto completoActas de conferencias sobre el tema "Multivariate depth"
Bern, Marshall y David Eppstein. "Multivariate regression depth". En the sixteenth annual symposium. New York, New York, USA: ACM Press, 2000. http://dx.doi.org/10.1145/336154.336218.
Texto completoEduardo Miranda Cunha, Paulo y Eduardo Filpo Ferreira da Silva. "Time To Depth Conversion By Multivariate Mapping". En 7th International Congress of the Brazilian Geophysical Society. European Association of Geoscientists & Engineers, 2001. http://dx.doi.org/10.3997/2214-4609-pdb.217.269.
Texto completoRongen, Guus y Ben Throssell. "Schematizing Rainfall Events with Multivariate Depth-Duration Dependence". En 33rd European Safety and Reliability Conference. Singapore: Research Publishing Services, 2023. http://dx.doi.org/10.3850/978-981-18-8071-1_p727-cd.
Texto completoLiu, Ce, Suryansh Kumar, Shuhang Gu, Radu Timofte y Luc Van Gool. "Single Image Depth Prediction Made Better: A Multivariate Gaussian Take". En 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2023. http://dx.doi.org/10.1109/cvpr52729.2023.01664.
Texto completoYao, Yuan y Norbert J. Pelc. "Multivariate Gaussian model based Cramér-Rao lower bound evaluation of the in-depth PCXD". En SPIE Medical Imaging, editado por Christoph Hoeschen, Despina Kontos y Thomas G. Flohr. SPIE, 2015. http://dx.doi.org/10.1117/12.2082111.
Texto completoXiao, G., Y. Li, B. Jiang, X. Duan y Y. Li. "Multivariate Constraints Time-Depth Conversion Method Based On Velocity Tomography and Application in Bohai K Oilfield". En 80th EAGE Conference and Exhibition 2018. Netherlands: EAGE Publications BV, 2018. http://dx.doi.org/10.3997/2214-4609.201800993.
Texto completoVasconcelos, Israel L. C. y André L. L. Aquino. "Multivariate Modeling to handle Urban Air Pollution Data observed trough Vehicular Sensor Networks". En Simpósio Brasileiro de Computação Ubíqua e Pervasiva. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/sbcup.2021.16011.
Texto completoRaghupathi, Laks, David Randell, Kevin Ewans y Philip Jonathan. "Non-Stationary Estimation of Joint Design Criteria With a Multivariate Conditional Extremes Approach". En ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/omae2016-54355.
Texto completoMercelis, Peter, Marc Dufour, Ariel Alvarez Gebelin, Vincent Gruwez, Sarah Doorme, Marc Sas y Gert Leyssen. "Generation of Multivariate Wave Conditions as Input for a Probabilistic Level III Breakwater Design". En ASME 2014 33rd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/omae2014-24143.
Texto completoSpeed, Jonathon. "Demystifying chemometrics: how multivariate analysis allows spectroscopy to be used to solve most analytical problems". En 2022 AOCS Annual Meeting & Expo. American Oil Chemists' Society (AOCS), 2022. http://dx.doi.org/10.21748/pkrn4677.
Texto completoInformes sobre el tema "Multivariate depth"
Rivera, Dion Arledge y Mary Kathleen Alam. Use of step scan FT-IR and multivariate curve resolution to understand aging of propellant binder as a function of depth into the polymer material. Office of Scientific and Technical Information (OSTI), enero de 2003. http://dx.doi.org/10.2172/918316.
Texto completoSánchez-Páez, David A. Effects of income inequality on COVID-19 infections and deaths during the first wave of the pandemic: Evidence from European countries. Verlag der Österreichischen Akademie der Wissenschaften, agosto de 2021. http://dx.doi.org/10.1553/populationyearbook2022.res1.1.
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