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Rosa E. Lillo

Bio: Rosa E. Lillo is an academic researcher from Charles III University of Madrid. The author has contributed to research in topics: Multivariate statistics & Estimator. The author has an hindex of 20, co-authored 114 publications receiving 1229 citations. Previous affiliations of Rosa E. Lillo include Carlos III Health Institute & Complutense University of Madrid.


Papers
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Journal ArticleDOI
TL;DR: In this paper, a new semidistance for functional observations was proposed that generalizes the Mahalanobis distance for multivariate datasets, and the main characteristics of the functional Mahalaobis semi-distance are shown.
Abstract: This article presents a new semidistance for functional observations that generalizes the Mahalanobis distance for multivariate datasets. The main characteristics of the functional Mahalanobis semidistance are shown. To illustrate the applicability of this measure of proximity between functional observations, new versions of several well-known functional classification procedures are developed using the functional Mahalanobis semidistance. A Monte Carlo study and the analysis of two real examples indicate that the classification methods used in conjunction with the functional Mahalanobis semidistance give better results than other well-known functional classification procedures. This article has supplementary material online.

71 citations

Journal ArticleDOI
TL;DR: In this paper, the authors describe various conditions on the parameters of pairs of nonhomogeneous Poisson or pure birth processes under which the corresponding epoch times or interepoch intervals are stochastically ordered in various senses.
Abstract: The purpose of this article is to describe various conditions on the parameters of pairs of nonhomogeneous Poisson or pure birth processes under which the corresponding epoch times or interepoch intervals are stochastically ordered in various senses. We derive results involving the usual stochastic order, the multivariate hazard rate order, the multivariate likelihood ratio order, as well as the dispersive and the mean residual life orders. A sample of applications involving generalized Yule processes, load-sharing models, and minimal repairs in reliability theory illustrate the usefulness of the new results.

65 citations

Journal ArticleDOI
TL;DR: Support Vector Machines is known to be a powerful nonparametric classification technique even for high-dimensional data, but the coefficient function that defines such linear score usually has many irregular oscillations, making it difficult to interpret.

65 citations

Journal ArticleDOI
TL;DR: The results suggest that the number ofCD34+ cells harvested in a single large-volume leukapheresis can be predicted from the measurement of peripheral blood CD34+ cell concentration on the collection day.
Abstract: BACKGROUND AND OBJECTIVE: In children it is very important to optimize PBPC harvesting and to reduce the number of leukaphereses per patient. The value of pre-apheresis peripheral blood CD34+ cell concentration as a predictor of PBPC yield was studied in 23 pediatric patients with hematologic and non-hematologic malignancies in order to optimize duration of PBPC collection. DESIGN AND METHODS: The patients underwent 25 stem-cell mobilization episodes with G-CSF alone and 40 large-volume leukapheresis procedures. Peripheral blood and harvested CD34+ cell concentrations were analyzed by means of flow cytometry. RESULTS: Using linear regression analysis, a highly significant correlation was found between the peripheral blood CD34+ cell count and the CD34+ cells/kg patient body weight collected on the apheresis day (r = 0.826, p = 0.0001). The results indicate that at least 1 x 10(6)/kg CD34+ cells can be harvested during one leukapheresis procedure in all patients if the pre-apheresis blood CD34+ cell count is > or = 30/microL and a CD34+ cell target of > or = 5 x 10(6)/kg is achieved in at least 80% of patients if this value is > or = 50 CD34+ cells/microL processing a median blood volume of 438.7 mL/kg (range, 207-560) over a median time of 232.5 minutes (range, 182-376). INTERPRETATION AND CONCLUSIONS: Our results suggest that the number of CD34+ cells harvested in a single large-volume leukapheresis can be predicted from the measurement of peripheral blood CD34+ cell concentration on the collection day.

61 citations

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TL;DR: A Monte Carlo study and the analysis of two real examples indicate that the classification methods used in conjunction with the functional Mahalanobis semidistance give better results than other well-known functional classification procedures.
Abstract: This paper presents a general notion of Mahalanobis distance for functional data that extends the classical multivariate concept to situations where the observed data are points belonging to curves generated by a stochastic process. More precisely, a new semi-distance for functional observations that generalize the usual Mahalanobis distance for multivariate datasets is introduced. For that, the development uses a regularized square root inverse operator in Hilbert spaces. Some of the main characteristics of the functional Mahalanobis semi-distance are shown. Afterwards, new versions of several well known functional classification procedures are developed using the Mahalanobis distance for functional data as a measure of proximity between functional observations. The performance of several well known functional classification procedures are compared with those methods used in conjunction with the Mahalanobis distance for functional data, with positive results, through a Monte Carlo study and the analysis of two real data examples.

60 citations


Cited by
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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Journal ArticleDOI
01 May 1975
TL;DR: The Fundamentals of Queueing Theory, Fourth Edition as discussed by the authors provides a comprehensive overview of simple and more advanced queuing models, with a self-contained presentation of key concepts and formulae.
Abstract: Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

2,562 citations

Journal ArticleDOI

793 citations

Journal ArticleDOI
13 Jul 2001-Science
TL;DR: Genetic evidence is provided that three closely related receptor tyrosine kinases, Tyro 3, Axl, and Mer, play an essential immunoregulatory role and Mutant mice that lack these receptors develop a severe lymphoproliferative disorder accompanied by broad-spectrum autoimmunity.
Abstract: Receptor tyrosine kinases and their ligands mediate cell-cell communication and interaction in many organ systems, but have not been known to act in this capacity in the mature immune system. We now provide genetic evidence that three closely related receptor tyrosine kinases, Tyro 3, Axl, and Mer, play an essential immunoregulatory role. Mutant mice that lack these receptors develop a severe lymphoproliferative disorder accompanied by broad-spectrum autoimmunity. These phenotypes are cell nonautonomous with respect to lymphocytes and result from the hyperactivation of antigen-presenting cells in which the three receptors are normally expressed.

659 citations