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Habtemicael, Semere; Ghebremichael, Musie; SenGupta, Indranil
2 Citations
The main goal of this paper is to model variance and volatility swap using superposition of BarndorffNielsen and Shephard (BNS) type models. In particular, in this paper we propose superposition of Lévy process driven by Γ(ν,α) and Inverse Gaussian distributions. Model performance is assessed on data not used to build the model (i.e., test data). It is shown that the prediction error rate for the models considered in this paper are much lower compared to those from previous related models. Moreover, it is shown that unlike previous related models which are restricted to stable markets, the present approach can be applied to both stable and unstable markets.
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Brombin, Chiara; Salmaso, Luigi; Fontanella, Lara; Ippoliti, Luigi; Fusilli, Caterina
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In this chapter we work directly with the offsetnormal shape distribution as a probability model for statistical inference on a sample of landmark configurations. This enables inference for induced Gaussian processes from configurations onto the shape space. Following Kume and Welling (J Comput Graph Stat 19:702–723, 2010), an Expectation Maximization (EM) algorithm for computing exact maximum likelihood (ML) estimation of the involved parameters is discussed. The chapter concludes with an application on facial expression analysis.
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Mazzali, Cristina; Maistriello, Mauro; Ieva, Francesca; Barbieri, Pietro
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1 Citations
Advantages and criticisms in using administrative data for clinical and epidemiological research are well discussed. These databases were originally designed for administrative aims rather than for clinical research. Several choices are necessary to make these databases suitable for clinical and epidemiological research. The choices have to be explicit and clearly declared, to let the reader know their possible effects. In this work we discuss methodological issues concerning the preliminary work on data from a regional project.
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Alfakih, Abdo Y.
Euclidean Distance Matrices fall into two classes: spherical and nonspherical. The first part of this chapter discusses various characterizations and several subclasses of spherical EDMs. Among the examples of spherical EDMs discussed are: regular EDMs, cell matrices, Manhattan distance matrices, Hamming distance matrices on the hypercube, distance matrices of trees and resistance distance matrices of electrical networks. The second part focuses on nonspherical EDMs and their characterization. As an interesting example of nonspherical EDMs, we discuss multispherical EDMs.
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Kim, Choongrak; Oh, Minkyung; Yang, Seong J.; Choi, Hyemi
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5 Citations
A local linear estimator of the conditional hazard function in censored data is proposed. The estimator suggested in this paper is motivated by the ideas of Fan, Yao, and Tong (1996) and Kim, Bae, Choi, and Park (2005). The asymptotic distribution of the proposed estimator is derived, and some numerical results are also provided.
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Kosfeld, Reinhold; Eckey, HansFriedrich; Türck, Matthias
Zusammenfassung
Bisher wurden Wahrscheinlichkeitsverteilungen in einer allgemeinen Form dargestellt. In der Praxis treten häufig ganz bestimmte Wahrscheinlichkeitsverteilungen auf, die nun vorgestellt werden. Während wir uns in diesem Kapitel mit diskreten Verteilungsmodellen beschäftigen, werden im nächsten Kapitel stetige Wahrscheinlichkeitsverteilungen diskutiert.
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Chiou, Sy Han; Kang, Sangwook; Kim, Junghi; Yan, Jun
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6 Citations
The semiparametric accelerated failure time (AFT) model is not as widely used as the Cox relative risk model due to computational difficulties. Recent developments in least squares estimation and induced smoothing estimating equations for censored data provide promising tools to make the AFT models more attractive in practice. For multivariate AFT models, we propose a generalized estimating equations (GEE) approach, extending the GEE to censored data. The consistency of the regression coefficient estimator is robust to misspecification of working covariance, and the efficiency is higher when the working covariance structure is closer to the truth. The marginal error distributions and regression coefficients are allowed to be unique for each margin or partially shared across margins as needed. The initial estimator is a rankbased estimator with Gehan’s weight, but obtained from an induced smoothing approach with computational ease. The resulting estimator is consistent and asymptotically normal, with variance estimated through a multiplier resampling method. In a large scale simulation study, our estimator was up to three times as efficient as the estimateor that ignores the withincluster dependence, especially when the withincluster dependence was strong. The methods were applied to the bivariate failure times data from a diabetic retinopathy study.
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Vicari, Donatella
Analysis of linear relations between variables, given a third one, can be investigated for threeway threemode data, by defining new measures of linear dependence between occasions. In this paper, two partial correlation coefficients between matrices are proposed. Their properties are analyzed, in particular with respect to the absence of conditional linear dependence.
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RamosPollán, Raúl; GuevaraLópez, Miguel Ángel; Oliveira, Eugénio
6 Citations
This paper describes the BiomedTK software framework, created to perform massive explorations of machine learning classifiers configurations for biomedical data analysis over distributed Grid computing resources. BiomedTK integrates ROC analysis throughout the complete classifier construction process and enables explorations of large parameter sweeps for training third party classifiers such as artificial neural networks and support vector machines, offering the capability to harness the vast amount of computing power serviced by Grid infrastructures. In addition, it includes classifiers modified by the authors for ROC optimization and functionality to build ensemble classifiers and manipulate datasets (import/export, extract and transform data, etc.). BiomedTK was experimentally validated by training thousands of classifier configurations for representative biomedical UCI datasets reaching in little time classification levels comparable to those reported in existing literature. The comprehensive method herewith presented represents an improvement to biomedical data analysis in both methodology and potential reach of machine learning based experimentation.
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