Relevant bibliographies by topics / Multivariate depth

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Multivariate depth'

Author: Grafiati
Published: 28 September 2024

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Journal articles on the topic "Multivariate depth"

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Bern and Eppstein. "Multivariate Regression Depth." Discrete & Computational Geometry 28, no. 1 (July 2002): 1–17. http://dx.doi.org/10.1007/s00454-001-0092-1.

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Claeskens, Gerda, Mia Hubert, Leen Slaets, and Kaveh Vakili. "Multivariate Functional Halfspace Depth." Journal of the American Statistical Association 109, no. 505 (January 2, 2014): 411–23. http://dx.doi.org/10.1080/01621459.2013.856795.

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Vencálek, Ondřej. "Depth-based Classification for Multivariate Data." Austrian Journal of Statistics 46, no. 3-4 (April 12, 2017): 117–28. http://dx.doi.org/10.17713/ajs.v46i3-4.677.

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Concept of data depth provides one possible approach to the analysis of multivariate data.Among other it can be also used for classification purposes. The present paper is an overview of the research in the field of depth-based classification for multivariate data.It provides a short summary of current state of knowledge in the field of depth-based classification followed by detailed discussion of four main directions in the depth-based classification, namely semiparametric depth-based classifiers, maximal depth classifier, (maximal depth) classifiers which use local depth functions and finally advanced depth-based classifiers.We do not restrict our attention only on proposed classifiers. The paper rather aims to overview the ideas connected with depth-based classification and problems that were discussed in this context.
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Ieva, Francesca, and Anna M. Paganoni. "Depth Measures for Multivariate Functional Data." Communications in Statistics - Theory and Methods 42, no. 7 (April 2013): 1265–76. http://dx.doi.org/10.1080/03610926.2012.746368.

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Cascos, Ignacio, and Ilya Molchanov. "Multivariate risks and depth-trimmed regions." Finance and Stochastics 11, no. 3 (May 15, 2007): 373–97. http://dx.doi.org/10.1007/s00780-007-0043-7.

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Tat, Samaneh, and Mohammad Reza Faridrohani. "Predicting depth value of the future depth-based multivariate record." Communications for Statistical Applications and Methods 30, no. 5 (September 30, 2023): 453–65. http://dx.doi.org/10.29220/csam.2023.30.5.453.

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Muthukrishnan, R., and Surabhi S. Nair. "Computing Robust Measure of Location on Multivariate Statistical Data Using Euclidean Depth Procedures." Indian Journal Of Science And Technology 16, no. 26 (July 23, 2023): 1927–34. http://dx.doi.org/10.17485/ijst/v16i26.847.

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He, Xuming, and Gang Wang. "Convergence of depth contours for multivariate datasets." Annals of Statistics 25, no. 2 (April 1997): 495–504. http://dx.doi.org/10.1214/aos/1031833661.

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Tsao, Min. "An empirical depth function for multivariate data." Statistics & Probability Letters 83, no. 1 (January 2013): 213–18. http://dx.doi.org/10.1016/j.spl.2012.09.007.

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Romanazzi, Mario. "Data depth, random simplices and multivariate dispersion." Statistics & Probability Letters 79, no. 12 (June 2009): 1473–79. http://dx.doi.org/10.1016/j.spl.2009.03.022.

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Dissertations / Theses on the topic "Multivariate depth"

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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.

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Although there has been progress in the area of Multivariate Statistical Process Control (MSPC), there are numerous limitations as well as unanswered questions with the current techniques. MSPC charts plotting Hotelling's T2 require the normality assumption for the joint distribution among the process variables, which is not feasible in many industrial settings, hence the motivation to investigate nonparametric techniques for multivariate data in quality control. In this research, the goal will be to create a systematic distribution-free approach by extending current developments and focusing on the dimensionality reduction using Principal Component Analysis. The proposed technique is different from current approaches given that it creates a nonparametric control chart using robust simplicial depth ranks of the first and last set of principal components to improve signal detection in multivariate quality control with no distributional assumptions. The proposed technique has the advantages of ease of use and robustness in MSPC for monitoring variability and correlation shifts. By making the approach simple to use in an industrial setting, the probability of adoption is enhanced. Improved MSPC can result in a cost savings and improved quality.
Ph.D.
Department of Industrial Engineering and Management Systems
Engineering and Computer Science
Industrial Engineering and Management Systems
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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.

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Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a powerful technique for the analysis of organic surfaces and interfaces for many innovative technologies. However, despite recent developments, there are still many issues and challenges hindering the robust, validated use of ToF-SIMS for quantitative measurement. These include: the lack of metrology and fundamental understanding for the use of novel cluster primary ion beams such as C60n+ and Ar2000+; the need for validated and robust measurement protocols for difficult samples, such as those with significant micron scale surface topography; the lack of guidance on novel data analysis methods including multivariate analysis which have the potential to simplify many time-consuming and intensive analyses in industry; and the need to establish best practice to improve the accuracy of measurements. This thesis describes research undertaken to address the above challenges. Sample topography and field effects were evaluated experimentally using model conducting and insulating fibres and compared with computer simulations to provide recommendation to diagnose and reduce the effects. Two popular multivariate methods, principal component analysis (PCA) and multivariate curve resolution (MCR), were explored using mixed organic systems consisting of a simple polymer blend and complex hair fibres treated with a multi-component formulation to evaluate different multivariate and data preprocessing methods for the optimal identification, localisation and quantification of the chemical components. Finally, cluster ion beams C60n+ and Ar500-2500+ were evaluated on an inorganic surface and an organic delta layer reference material respectively to elucidate the fundamental metrology of cluster ion sputtering and pave the way for their use in organic depth profiling. These studies provide the essential metrological foundation to address frontier issues in surface and nanoanalysis and extend the measurement capabilities of ToF-SIMS.
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Armaut, 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.

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Les fonctions de profondeur statistiques jouent un rôle fondamental dans l'analyse et la caractérisation des structures de données complexes. Les profondeurs fournissent une mesure de centralité ou d'excentricité pour une observation individuelle ou pour l'ensemble des données, ce qui aide à comprendre leurs positions relatives et leurs distributions sous-jacentes. Les concepts relatifs à la profondeur, tels qu'ils sont présents dans la littérature, trouvent leur origine dans la notion de profondeur de Tukey, également désignée sous le nom de profondeur médiane. Cette notion a été introduite par le statisticien John W. Tukey dans son article intitulé "Mathematics and the Picturing of Data" publié en 1975 [170]. La principale idée sous-jacente à la profondeur de Tukey consiste à généraliser la médiane univariée d'un jeu de données unidimensionnel en dimension supérieure.Dans un premier temps, nous nous intéressons aux profondeurs multivariées suivies des profondeurs fonctionnelles, pour lesquelles nous construisons une revue générale dans le Chapitre 1.Dans la seconde partie de la thèse, i.e. dans le Chapitre 2, nous entreprenons une étude rigoureuse des ensembles de niveaux des fonctions de profondeur multivariées et établissons plusieurs propriétés analytiques et statistiques. Tout d'abord, nous montrons que lorsque la profondeur multivariée sous-jacente est suffisamment régulière, la différence symétrique entre l'ensemble de niveaux de profondeur estimé et son équivalent théorique converge vers zéro en termes de volume d-dimensionel et de probabilité sous la distribution considérée. Outre ces contributions, la nouveauté du Chapitre 2, dans le cadre de la théorie du risque, réside dans l'introduction d'une mesure de risque basée sur une profondeur appelée Covariate-Conditional-Tail-Expectation (CCTE). Globalement, la CCTE vise à calculer un coût moyen sachant qu'au moins un des facteurs de risque en jeu est 'élevé' suivant une certaine direction. Cette dernière zone de risque est modélisée par un ensemble de niveau de faible profondeur. Contrairement à des mesures de risques fondées sur les queues de distribution, notre définition de CCTE est indépendante de toute direction grâce à l'implication des ensembles de niveaux d'une profondeur. Nous démontrons également que, lorsque la taille de l'échantillon tend vers l'infini, la CCTE basée sur la profondeur empirique est consistante par rapport à sa version théorique. Et nous fournissons les taux de convergence pour la CCTE, pour des niveaux de risque fixes ainsi que lorsque le niveau de risque tend vers zéro quand la taille de l'échantillon tend vers l'infini. Dans ce dernier cas d'étude, nous analysons de même le comportement de la définition originelle de CCTE basée sur une fonction de répartition, cas qui n'a pas été étudié dans [56]. En plus des simulations effectuées sur la CCTE, nous illustrons son utilité sur des données environnementales.La dernière partie de cette thèse, le Chapitre 3, conclut notre travail et consiste à définir une profondeur fonctionnelle générale pour des données fonctionnelles basée sur l'analyse en composantes principales fonctionnelles. Cela implique l'utilisation d'une profondeur multivariée générique. Dans cette optique, nous utilisons la décomposition bien connue de Karhunen-Loève comme outil pour pro- jeter un processus aléatoire centré et de carré intégrable le long d'une combinaison linéaire finie de fonctions orthogonales appelées composantes principales. À notre connaissance, il s'agit d'une approche novatrice dans le cadre des profondeurs fonctionnelles. Naturellement, nous proposons un estimateur de notre profondeur fonctionnelle pour lequel nous démontrons une consistance uniforme. Nous complétons enfin notre étude avec des simulations et des applications sur données réelles dans des problèmes de classifications, où notre nouvelle profondeur se révèle être au moins aussi performante que la plupart des concurrents classiques
Statistical 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
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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.

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Cantu, 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.

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Suite à l’amélioration des outils de capture et de stockage des données, ces dernières années ont vu les quantités de données à traiter croître énormément. De nombreux travaux, allant du traitement automatique à la visualisation d’information, ont alors été mis en place, mais certains domaines sont encore trop spécifiques pour en profiter. C’est le cas du Renseignement d’Origine ÉlectroMagnétique (ROEM). Ce domaine ne fait pas uniquement face à de grandes quantités de données mais doit aussi gérer des données et des usages complexes ainsi que des populations d’utilisateurs ayant de moins en moins d’expérience. Dans cette thèse nous nous sommes intéressés à l’usage de l’existant et des nouvelles technologies appliquées à la visualisation pour proposer des solutions à la combinaison de problématiques comme les données en grandes quantité et les données complexes. Nous commençons par présenter une analyse du domaine du ROEM qui a permis d’extraire les problématiques auxquelles il doit faire face. Nous nous intéressons ensuite aux solutions gérant les combinaisons de telles problématiques. L’existant ne contenant pas directement de telles solutions, nous nous intéressons alors à la description des problématiques de visualisation et proposons une caractérisation de ces problématiques. Cette caractérisation nous permet de décrire les représentations existantes et de mettre en place un outil de recommandation des représentations basé sur la façon dont l’existant résout les problématiques. Enfin nous nous intéressons à identifier de nouvelles métaphores pour compléter l’existant et proposons une représentation immersive permettant de résoudre les problématiques du ROEM. Ces contributions permettent d’analyser et d’utiliser l’existant et approfondissent l’usage des représentations immersives pour la visualisation d’information
As 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
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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.

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Carvalho, 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/.

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O objetivo do presente trabalho é analisar as correlações existentes entre variáveis ambientais e a distribuição das espécies arbóreas. O estudo foi realizado em uma parcela permanente de 320 x 320 m, um fragmento de cerradão, situado na Estação Ecológica de Assis, Assis, SP. Coletaram-se amostras em cinco pontos de uma pedosequência, realizando-se análises químicas, físicas do solo e micromorfologicas. A vegetação foi amostrada em áreas de 314m² em torno de cada ponto de coleta do solo. Foram considerados todos os indivíduos vivos e com DAP (diâmetro à altura do peito) igual ou superior a 4,8cm e PAP (perímetro à altura do peito) 15 cm. Para analisar o banco de dados foram utilizadas três técnicas de análise multivariada: análise de componentes principais (PCA), para variáveis ambientais; análise de correspondência retificada (DCA), para variáveis florísticas; e análise de correspondência canônica (CCA), para verificar a possível associação entre estas duas variáveis. A análise dos componentes principais (PCA) demonstrou que a maioria das variáveis apresentaram correlação semelhante nas camadas superficiais (0-20 cm e 20-60 cm). Essa tendência não se repetiu nas demais profundidades (60-80 e >80 cm), evidenciando maior diferenciação do perfil de solo com o aumento da profundidade analisada. A análise de correspondência canônica (CCA) mostrou coerência nos padrões de distribuição das espécies em relação às variáveis ambientais do fragmento, caracterizada pelos atributos químicos e físicos, mas principalmente pelo regime hídrico dos solos, sugerindo que este seja um fator fortemente determinante na distribuição das espécies.
The 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.
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Salawu, Emmanuel Oluwatobi. "Spatiotemporal Variations in Coexisting Multiple Causes of Death and the Associated Factors." ScholarWorks, 2018. https://scholarworks.waldenu.edu/dissertations/6108.

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The study and practice of epidemiology and public health benefit from the use of mortality statistics, such as mortality rates, which are frequently used as key health indicators. Furthermore, multiple causes of death (MCOD) data offer important information that could not possibly be gathered from other mortality data. This study aimed to describe the interrelationships between various causes of death in the United States in order to improve the understanding of the coexistence of MCOD and thereby improve public health and enhance longevity. The social support theory was used as a framework, and multivariate linear regression analyses were conducted to examine the coexistence of MCOD in approximately 80 million death cases across the United States from 1959 to 2005. The findings showed that in the United States, there is a statistically significant relationship between the number of coexisting MCOD, race, education, and the state of residence. Furthermore, age, gender, and marital status statistically influence the average number of coexisting MCOD. The results offer insights into how the number of coexisting MCOD vary across the United States, races, education levels, gender, age, and marital status and lay a foundation for further investigation into what people are dying from. The results have the long-term potential of helping public health practitioners identify individuals or communities that are at higher risks of death from a number of coexisting MCOD such that actions could be taken to lower the risks to improve people's wellbeing, enhance longevity, and contribute to positive social change.
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Chih-TingHsieh and 謝芝庭. "Multivariate Process Capability Index Based on Data Depth Concept." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/2cw2p6.

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碩士
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.
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Kong, Linglong. "On Multivariate Quantile Regression: Directional Approach and Application with Growth Charts." Phd thesis, 2009. http://hdl.handle.net/10048/462.

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Thesis (Ph. D.)--University of Alberta, 2009.
Title 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.
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Books on the topic "Multivariate depth"

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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.

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Y, Liu Regina, Serfling Robert J, and Souvaine Diane L, eds. Data depth: Robust multivariate analysis, computational geometry, and applications. New York: American Mathematical Society, 2006.

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Kosiorowski, Daniel. Statystyczne funkcje głębi w odpornej analizie ekonomicznej: The statistical functions of depth in robust economic analysis. Kraków: Wydawnictwo Uniwersytetu Ekonomicznego w Krakowie, 2012.

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Multivariate Dispersion, Central Regions, and Depth. Springer, 2002.

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Mosler, Karl. Multivariate Dispersion, Central Regions, and Depth. Springer, 2011.

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Mosler, Karl. Multivariate Dispersion, Central Regions, and Depth: The Lift Zonoid Approach. Springer London, Limited, 2012.

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Baillo, Amparo, Antonio Cuevas, and Ricardo Fraiman. Classification methods for functional data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.10.

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This article reviews the literature concerning supervised and unsupervised classification of functional data. It first explains the meaning of unsupervised classification vs. supervised classification before discussing the supervised classification problem in the infinite-dimensional case, showing that its formal statement generally coincides with that of discriminant analysis in the classical multivariate case. It then considers the optimal classifier and plug-in rules, empirical risk and empirical minimization rules, linear discrimination rules, the k nearest neighbor (k-NN) method, and kernel rules. It also describes classification based on partial least squares, classification based on reproducing kernels, and depth-based classification. Finally, it examines unsupervised classification methods, focusing on K-means for functional data, K-means for data in a Hilbert space, and impartial trimmed K-means for functional data. Some practical issues, in particular real-data examples and simulations, are reviewed and some selected proofs are given.
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Müller, Wolfgang C., and 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.

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This volume investigates nuclear energy policies in Western Europe over the entire post-war period, giving special attention to the two most recent decades. It focuses on the interplay between voters’ attitudes, anti-nuclear movements, party competition, and coalition formation. Based on a mixed-methods approach using structured case studies, qualitative comparison, and quantitative analyses, it shows that the nature of party competition under given institutional contexts is a key driver for policy change. Part I introduces the practical and theoretical relevance of the topic. It outlines the reasoning of the major scientific contribution with regard to nuclear energy policies, and offers a theoretical alternative to the previous literature that has been predominantly movements-oriented. It also formulates a set of specific hypotheses on policy change and stability. Additionally, it provides core economic and political indicators of the changing role of nuclear energy in the countries. Part II consists of seven in-depth country case studies applying the outlined analytical perspective. Part III consists of an evaluation of the hypotheses, qualitative comparison of sixteen Western European cases (drawing, in addition to the country case studies on short narratives of the remaining countries) and of a quantitative assessment of the multivariate impact of factors for policy change.
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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.

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Hard coal mining is characterised by features that pose numerous challenges to its current operations and cause strategic and operational problems in planning its development. The most important of these include the high capital intensity of mining investment projects and the dynamically changing environment in which the sector operates, while the long-term role of the sector is dependent on factors originating at both national and international level. At the same time, the conditions for coal mining are deteriorating, the resources more readily available in active mines are being exhausted, mining depths are increasing, temperature levels in pits are rising, transport routes for staff and materials are getting longer, effective working time is decreasing, natural hazards are increasing, and seams with an increasing content of waste rock are being mined. The mining industry is currently in a very difficult situation, both in technical (mining) and economic terms. It cannot be ignored, however, that the difficult financial situation of Polish mining companies is largely exacerbated by their high operating costs. The cost of obtaining coal and its price are two key elements that determine the level of efficiency of Polish mines. This situation could be improved by streamlining the planning processes. This would involve striving for production planning that is as predictable as possible and, on the other hand, economically efficient. In this respect, it is helpful to plan the production from operating longwalls with full awareness of the complexity of geological and mining conditions and the resulting economic consequences. The constraints on increasing the efficiency of the mining process are due to the technical potential of the mining process, organisational factors and, above all, geological and mining conditions. The main objective of the monograph is to identify relations between geological and mining parameters and the level of longwall mining costs, and their daily output. In view of the above, it was assumed that it was possible to present the relationship between the costs of longwall mining and the daily coal output from a longwall as a function of onerous geological and mining factors. The monograph presents two models of onerous geological and mining conditions, including natural hazards, deposit (seam) parameters, mining (technical) parameters and environmental factors. The models were used to calculate two onerousness indicators, Wue and WUt, which synthetically define the level of impact of onerous geological and mining conditions on the mining process in relation to: —— operating costs at longwall faces – indicator WUe, —— daily longwall mining output – indicator WUt. In the next research step, the analysis of direct relationships of selected geological and mining factors with longwall costs and the mining output level was conducted. For this purpose, two statistical models were built for the following dependent variables: unit operating cost (Model 1) and daily longwall mining output (Model 2). The models served two additional sub-objectives: interpretation of the influence of independent variables on dependent variables and point forecasting. The models were also used for forecasting purposes. Statistical models were built on the basis of historical production results of selected seven Polish mines. On the basis of variability of geological and mining conditions at 120 longwalls, the influence of individual parameters on longwall mining between 2010 and 2019 was determined. The identified relationships made it possible to formulate numerical forecast of unit production cost and daily longwall mining output in relation to the level of expected onerousness. The projection period was assumed to be 2020–2030. On this basis, an opinion was formulated on the forecast of the expected unit production costs and the output of the 259 longwalls planned to be mined at these mines. A procedure scheme was developed using the following methods: 1) Analytic Hierarchy Process (AHP) – mathematical multi-criteria decision-making method, 2) comparative multivariate analysis, 3) regression analysis, 4) Monte Carlo simulation. The utilitarian purpose of the monograph is to provide the research community with the concept of building models that can be used to solve real decision-making problems during longwall planning in hard coal mines. The layout of the monograph, consisting of an introduction, eight main sections and a conclusion, follows the objectives set out above. Section One presents the methodology used to assess the impact of onerous geological and mining conditions on the mining process. Multi-Criteria Decision Analysis (MCDA) is reviewed and basic definitions used in the following part of the paper are introduced. The section includes a description of AHP which was used in the presented analysis. Individual factors resulting from natural hazards, from the geological structure of the deposit (seam), from limitations caused by technical requirements, from the impact of mining on the environment, which affect the mining process, are described exhaustively in Section Two. Sections Three and Four present the construction of two hierarchical models of geological and mining conditions onerousness: the first in the context of extraction costs and the second in relation to daily longwall mining. The procedure for valuing the importance of their components by a group of experts (pairwise comparison of criteria and sub-criteria on the basis of Saaty’s 9-point comparison scale) is presented. The AHP method is very sensitive to even small changes in the value of the comparison matrix. In order to determine the stability of the valuation of both onerousness models, a sensitivity analysis was carried out, which is described in detail in Section Five. Section Six is devoted to the issue of constructing aggregate indices, WUe and WUt, which synthetically measure the impact of onerous geological and mining conditions on the mining process in individual longwalls and allow for a linear ordering of longwalls according to increasing levels of onerousness. Section Seven opens the research part of the work, which analyses the results of the developed models and indicators in individual mines. A detailed analysis is presented of the assessment of the impact of onerous mining conditions on mining costs in selected seams of the analysed mines, and in the case of the impact of onerous mining on daily longwall mining output, the variability of this process in individual fields (lots) of the mines is characterised. Section Eight presents the regression equations for the dependence of the costs and level of extraction on the aggregated onerousness indicators, WUe and WUt. The regression models f(KJC_N) and f(W) developed in this way are used to forecast the unit mining costs and daily output of the designed longwalls in the context of diversified geological and mining conditions. The use of regression models is of great practical importance. It makes it possible to approximate unit costs and daily output for newly designed longwall workings. The use of this knowledge may significantly improve the quality of planning processes and the effectiveness of the mining process.
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Book chapters on the topic "Multivariate depth"

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Mosler, Karl. "Data depth." In 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.

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Mosler, Karl. "Depth of hyperplanes." In 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.

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Serfling, Robert. "Depth functions in nonparametric multivariate inference." In 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.

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Fraiman, Ricardo, Regina Y. Liu, and Jean Meloche. "Multivariate density estimation by probing depth." In 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.

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Mosler, Karl. "Introduction." In 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.

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Mosler, Karl. "Zonoids and lift zonoids." In 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.

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Mosler, Karl. "Central regions." In 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.

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Dykerhoff, Rainer. "Inference based on data depth by Rainer Dyckerhoff." In 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.

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Mosler, Karl. "Volume statistics." In 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.

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Mosler, Karl. "Orderings and indices of dispersion." In 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.

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Conference papers on the topic "Multivariate depth"

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Bern, Marshall, and David Eppstein. "Multivariate regression depth." In the sixteenth annual symposium. New York, New York, USA: ACM Press, 2000. http://dx.doi.org/10.1145/336154.336218.

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Eduardo Miranda Cunha, Paulo, and Eduardo Filpo Ferreira da Silva. "Time To Depth Conversion By Multivariate Mapping." In 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.

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Rongen, Guus, and Ben Throssell. "Schematizing Rainfall Events with Multivariate Depth-Duration Dependence." In 33rd European Safety and Reliability Conference. Singapore: Research Publishing Services, 2023. http://dx.doi.org/10.3850/978-981-18-8071-1_p727-cd.

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Liu, Ce, Suryansh Kumar, Shuhang Gu, Radu Timofte, and Luc Van Gool. "Single Image Depth Prediction Made Better: A Multivariate Gaussian Take." In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2023. http://dx.doi.org/10.1109/cvpr52729.2023.01664.

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Yao, Yuan, and Norbert J. Pelc. "Multivariate Gaussian model based Cramér-Rao lower bound evaluation of the in-depth PCXD." In SPIE Medical Imaging, edited by Christoph Hoeschen, Despina Kontos, and Thomas G. Flohr. SPIE, 2015. http://dx.doi.org/10.1117/12.2082111.

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Xiao, G., Y. Li, B. Jiang, X. Duan, and Y. Li. "Multivariate Constraints Time-Depth Conversion Method Based On Velocity Tomography and Application in Bohai K Oilfield." In 80th EAGE Conference and Exhibition 2018. Netherlands: EAGE Publications BV, 2018. http://dx.doi.org/10.3997/2214-4609.201800993.

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Vasconcelos, Israel L. C., and André L. L. Aquino. "Multivariate Modeling to handle Urban Air Pollution Data observed trough Vehicular Sensor Networks." In Simpósio Brasileiro de Computação Ubíqua e Pervasiva. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/sbcup.2021.16011.

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This work presents an interdisciplinary assessment that looks in-depth at the tracking of air quality in urban environments. The proposed application takes advantage of Vehicle Sensor Networks (VSN) by embedding sensor nodes to public transportation, spreading the sampling activity through different places visited during the route. We perform environmental modeling based on real data collected from the city of São Paulo, considering the multivariate spatial behavior of five different air pollutants from fossil-fueled vehicles (CO, O3, PM10, NO2 and SO2) simultaneously while it also varies in time. Finally, our VSN-based approach showed an improvement of 126 times lower error and 11 times higher coverage about conventional monitoring with air quality stations.
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Raghupathi, Laks, David Randell, Kevin Ewans, and Philip Jonathan. "Non-Stationary Estimation of Joint Design Criteria With a Multivariate Conditional Extremes Approach." In 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.

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Understanding the interaction of ocean environments with fixed and floating structures is critical to the design of offshore and coastal facilities. Structural response to environmental loading is typically the combined effect of multiple environmental parameters over a period of time. Knowledge of the tails of marginal and joint distributions of these parameters (e.g. storm peak significant wave height and associated current) as a function of covariates (e.g. dominant wave and current directions) is central to the estimation of extreme structural response, and hence of structural reliability and safety. In this paper, we present a framework for the joint estimation of multivariate extremal dependencies with multi-dimensional covariates. We demonstrate proof of principle with a synthetic bi-variate example with two covariates quantified by rigorous uncertainty analysis. We further substantiate it using two practical applications (associated current given significant wave height for northern North Sea and joint current profile for offshore Brazil locations). Further applications include the estimation of associated criteria for response-based design (e.g., TP given HS), extreme current profiles with depth for mooring and riser loading, weathervaning systems with non-stationary effects for the design of FLNG/FPSO installations, etc.
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Mercelis, Peter, Marc Dufour, Ariel Alvarez Gebelin, Vincent Gruwez, Sarah Doorme, Marc Sas, and Gert Leyssen. "Generation of Multivariate Wave Conditions as Input for a Probabilistic Level III Breakwater Design." In 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.

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For an offshore LNG project situated in the estuary of the Rio de la Plata nearby Montevideo, Uruguay, it was required to verify the deterministic design of the protective rubble mound breakwater and the jetty infrastructure with a level three probabilistic design. Therefore, in first instance extreme site conditions were required both in front of and behind the breakwater. To obtain these conditions, the first step is to extrapolate the offshore variables in order to translate them to the breakwater location. All the possible combinations of extreme wind, water level and waves are quantified with a probability of occurrence. A combination of univariate extreme value distributions, copula’s and regression is used to describe the multivariate statistical behaviour of the offshore variables. The main variable is the wind velocity, as in the area of concern extreme wave conditions are wind driven. The secondary variable is water level. Wind velocity and water levels are only correlated for some wind directions. For these directions, wind velocity and water level extreme value distributions are linked through a multivariate Gumbel Copula. The wave height at the model boundaries was taken into account by a regression function with the extreme wind velocity at the offshore location and the wave period by a regression function with the wave height. This way 1515 synthetic events were selected and simulated with the spectral wave model SWAN, each of which a frequency of occurrence is calculated for. However, due to refraction and diffraction effects of the approach channel (in the area of concern water depths are limited to about 7 m and the navigation channel has a depth of about 14 m), the port basin and the breakwater itself, the spectral wave model SWAN is not sufficient to accurately calculate the local wave conditions in the entire area of interest. Therefore a non-linear Boussinesq wave model (i.e. Mike 21 BW) was set up in addition, using input from the spectral model at the boundary and including the navigation channel of more than 12 km long. Combining both models, significant wave heights are obtained on both the seaward side and the leeside of the breakwater with corresponding frequencies of occurrence. This approach allows the determination of conditional return periods and generates the site conditions required for a probabilistic level three design of the breakwater and the jetty infrastructure taking for example the joint probabilities between waves and water levels fully into account as needed for overtopping or failure calculations.
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Speed, Jonathon. "Demystifying chemometrics: how multivariate analysis allows spectroscopy to be used to solve most analytical problems." In 2022 AOCS Annual Meeting & Expo. American Oil Chemists' Society (AOCS), 2022. http://dx.doi.org/10.21748/pkrn4677.

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Chemists have been using spectroscopic techniques for decades if not hundreds of years. The large range of different physical transitions nuclei and molecules can undertake when irradiate with specific wavelengths of light means that almost every property of interest can and has been studied by spectroscopic means. However, the need to interpret the raw spectra means only specialists are able to interpret the fundamental information present in a spectrum, turning spectroscopy into a tool for experts. The advent of chemometrics meant that spectrometers could be changed into concentration meters, allowing a user to run a previously built calibration curve and understand the makeup of their sample. As a result, many industries now have a dwindling pool of spectroscopy “power users” and have retired their powerful instruments into pre-determined QC tests. This presentation aims to educate and empower the attendees as to the power of spectroscopy, and the different types of analysis it allows, ranging from fundamental chemical makeup through to on-line and real-time processing analysis for industrial installations. We will briefly outline the different types of spectroscopy commonly used in laboratories and on-line, and explain their strengths and weaknesses. Next, we will explain the fundamentals behind multivariate analysis, focusing on principal component analysis (PCA) and partial least squares (PLS) methodology as well as multivariate curve resolution (MCR). We will then explain how chemometrics can be used to investigate in detail what is occurring during a process, how the advanced statistics generated on every spectrum allow in depth analysis of the process (for example how to properly use Hotelling’s T2 and Q Residuals) and give some case studies.
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Reports on the topic "Multivariate depth"

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Rivera, Dion Arledge, and 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), January 2003. http://dx.doi.org/10.2172/918316.

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Sánchez-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, August 2021. http://dx.doi.org/10.1553/populationyearbook2022.res1.1.

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Evidence from research on infectious diseases suggests that income inequality is related to higher rates of infection and death in disadvantaged population groups. Our objective is to examine whether there was an association between income inequality and the numbers of cases and deaths during the first wave of the COVID- 19 pandemic in European countries. We determined the duration of the first wave by first smoothing the number of daily cases, and then using a LOESS regression to fit the smoothed trend. Next, we estimated quasi-Poisson regressions. Results from the bivariate models suggest there was a moderate positive association between the Gini index values and the cumulated number of infections and deaths during the first wave, although the statistical significance of this association disappeared when controls were included. Results from multivariate models suggest that higher numbers of infections and deaths from COVID-19 were associated with countries having more essential workers, larger elderly populations and lower health care capacities.
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