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Sensitivity Analysis for Nonrandom Dropout: A Local Influence Approach
Biometrics, 2001Summary.Diggle and Kenward (1994,Applied Statistics43, 49–93) proposed a selection model for continuous longitudinal data subject to nonrandom dropout. It has provoked a large debate about the role for such models. The original enthusiasm was followed by skepticism about the strong but untestable assumptions on which this type of model invariably rests.
Verbeke, Geert +4 more
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A locally sensitive method for cluster analysis
Pattern Recognition, 1976Abstract In this paper a new method of mode separation is proposed. The method is based on mapping of data points from the N -dimensional space onto a sequence so that the majority of points from each mode become successive elements of the sequence. The intervals of points in the sequence belonging to the respective modes of the p.d.f.
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Local Sensitivity Analysis in Estimation Problems
Journal of Computational and Graphical Statistics, 2008This article deals with the problem of local sensitivity analysis, that is, how sensitive are the results of a statistical analysis to changes in the data? A general methodology of sensitivity analysis is applied to some statistical problems. The proposed methods are applicable to any statistical problem that can be expressed as an optimization problem
Enrique Castillo +3 more
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A fatigue model with local sensitivity analysis
Fatigue & Fracture of Engineering Materials & Structures, 2007ABSTRACTThe goal of this paper is two fold. First, it introduces a general parametric lifetime model for high‐cycle fatigue regime derived from physical, statistical, engineering and dimensional analysis considerations. The proposed model has two threshold parameters and three Weibull distribution parameters.
E. CASTILLO +3 more
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Fast Object Localization via Sensitivity Analysis
2019Deep Convolutional Neural Networks (CNNs) have been repeatedly shown to perform well on image classification tasks, successfully recognizing a broad array of objects when given sufficient training data. Methods for object localization, however, are still in need of substantial improvement.
Mohammad K. Ebrahimpour, David C. Noelle
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Hoffman's Error Bound, Local Controllability, and Sensitivity Analysis
SIAM Journal on Control and Optimization, 2000Summary: Our aim is to present sufficient conditions ensuring Hoffman's error bound for lower semicontinuous nonconvex inequality systems and to analyze its impact on the local controllability, implicit function theorem for (non-Lipschitz) multivalued mappings, generalized equations (variational inequalities), and sensitivity analysis and on other ...
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On the Local Sensitivity Analysis of the Inoperability Input‐Output Model
Risk Analysis, 2011Natural and man‐made disasters are currently a source of major concern for contemporary societies. In order to understand their economic impacts, the inoperability input‐output model has recently gained recognition among scholars. In a recent paper, Percoco (2006) has proposed an extension of the model to map the technologically most important sectors ...
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Stable locality sensitive discriminant analysis for image recognition
Neural Networks, 2014Locality Sensitive Discriminant Analysis (LSDA) is one of the prevalent discriminant approaches based on manifold learning for dimensionality reduction. However, LSDA ignores the intra-class variation that characterizes the diversity of data, resulting in unstableness of the intra-class geometrical structure representation and not good enough ...
Quanxue Gao +4 more
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Local Sensitivity Analysis with Constraints
2017This chapter, which is our last on deterministic methods, addresses the removal of a typical assumption in sensitivity analysis.
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Two-dimensional locality sensitive discriminant analysis
2008 International Conference on Wavelet Analysis and Pattern Recognition, 2008Recently, locality sensitive discriminant analysis (LSDA) was proposed for dimensionality reduction. As far as matrix data, such as images, they are often vectorized for LSDA algorithm to find the intrinsic manifold structure. Such a matrix-to-vector transform may cause the loss of some structural information residing in original 2D images.
null Yantao Wei +2 more
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