Random variate
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Canonical Variate Dissimilarity Analysis for Process Incipient Fault Detection Open
Early detection of incipient faults in industrial processes is increasingly becoming important, as these faults can slowly develop into serious abnormal events, an emergency situation, or even failure of critical equipment. Multivariate st…
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Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond Open
The class of random features is one of the most popular techniques to speed up kernel methods in large-scale problems. Related works have been recognized by the NeurIPS Test-of-Time award in 2017 and the ICML Best Paper Finalist in 2019. T…
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Multivariate Drought Frequency Analysis using Four-Variate Symmetric and Asymmetric Archimedean Copula Functions Open
In drought frequency analysis, as the number of drought variables increases, the joint behavior between these variables needs to be studied. Therefore, this study aims to develop a flexible four-variate joint distribution function of the r…
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Constrained Factor Models for High-Dimensional Matrix-Variate Time Series Open
High-dimensional matrix-variate time series data are becoming widely available in many scientific fields, such as economics, biology, and meteorology. To achieve significant dimension reduction while preserving the intrinsic matrix structu…
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Statistical Inference for High-Dimensional Matrix-Variate Factor Models Open
This article considers the estimation and inference of the low-rank components in high-dimensional matrix-variate factor models, where each dimension of the matrix-variates (p × q) is comparable to or greater than the number of observation…
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Comparison of deep learning models for multivariate prediction of time series wind power generation and temperature Open
Wind power experienced a substantial growth over the past decade especially because it has been seen as one of the best ways towards meeting climate change and emissions targets by many countries. Since wind power is not fully dispatchable…
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Multidimensional Cross-Recurrence Quantification Analysis (MdCRQA) – A Method for Quantifying Correlation between Multivariate Time-Series Open
In this paper, Multidimensional Cross-Recurrence Quantification Analysis (MdCRQA) is introduced. It is an extension of Multidimensional Recurrence Quantification Analysis (MdRQA), which allows to quantify the (auto-)recurrence properties o…
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Smooth Random Functions, Random ODEs, and Gaussian Processes Open
International audience
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Classification With the Matrix-Variate-<i>t</i> Distribution Open
Matrix-variate distributions can intuitively model the dependence structure of matrix-valued observations that arise in applications with multivariate time series, spatio-temporal or repeated measures. This paper develops an Expectation-Ma…
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Gas–Liquid Flow Pattern Analysis Based on Graph Connectivity and Graph-Variate Dynamic Connectivity of ERT Open
Two-phase flow is widely encountered in process engineering and related scientific research. Understanding flow patterns and their transitions is important to discover the fluid mechanics of two-phase flow. In order to investigate the comp…
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Canonical variate regression Open
In many fields, multi-view datasets, measuring multiple distinct but interrelated sets of characteristics on the same set of subjects, together with data on certain outcomes or phenotypes, are routinely collected. The objective in such a p…
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Condition monitoring of rotating machines under time-varying conditions based on adaptive canonical variate analysis Open
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
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Feature Level-Sets: Generalizing Iso-Surfaces to Multi-Variate Data Open
Iso-surfaces or level-sets provide an effective and frequently used means for feature visualization. However, they are restricted to simple features for uni-variate data. The approach does not scale when moving to multi-variate data or whe…
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Review of Variable Selection Methods for Discriminant-Type Problems in Chemometrics Open
Discriminant-type analyses arise from the need to classify samples based on their measured characteristics (variables), usually with respect to some observable property. In the case of samples that are difficult to obtain, or using advance…
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Sampling techniques involving human subjects: Applications, pitfalls, and suggestions for further studies Open
The most commonly used sampling techniques in systematic investigations are probability and nonprobability methods. While probability sampling is based on the principle of a random selection of participants in a particular study, non-rando…
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Multi-scale variance reduction methods based on multiple control\n variates for kinetic equations with uncertainties Open
The development of efficient numerical methods for kinetic equations with\nstochastic parameters is a challenge due to the high dimensionality of the\nproblem. Recently we introduced a multiscale control variate strategy which is\ncapable …
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Enhancing the Transferability of Adversarial Examples with Random Patch Open
Adversarial examples can fool deep learning models, and their transferability is critical for attacking black-box models in real-world scenarios. Existing state-of-the-art transferable adversarial attacks tend to exploit intrinsic features…
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A New Acoustic Emission Source Location Method Using Tri-Variate Kernel Density Estimator Open
A new AE location method using tri-variate kernel density estimator is developed in this paper. Firstly, combinations of every six arrivals are obtained from a multi-sensor location system, and the preliminary location results are obtained…
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A Multi-Variate Triple-Regression Forecasting Algorithm for Long-Term Customized Allergy Season Prediction Open
In this paper, we propose a novel multi-variate algorithm using a triple-regression methodology to predict the airborne-pollen allergy season that can be customized for each patient in the long term. To improve the prediction accuracy, we …
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A Multi-Variate Approach to Predicting Myoelectric Control Usability Open
Pattern recognition techniques leveraging the use of electromyography signals have become a popular approach to provide intuitive control of myoelectric devices. Performance of these control interfaces is commonly quantified using offline …
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A copula based bi-variate model for temperature and rainfall processes Open
Rainfall and temperature remain the two major climatic parameters influencing agriculture productivity, meteorology and weather related industries. It is known that accurate analysis and simulation of temperature and rainfall processes is …
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On Random Matrices Arising in Deep Neural Networks. Gaussian Case Open
The paper deals with distribution of singular values of product of random matrices arising in the analysis of deep neural networks. The matrices resemble the product analogs of the sample covariance matrices, however, an important differen…
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A Linkage among Tree Diameter, Height, Crown Base Height, and Crown Width 4-Variate Distribution and Their Growth Models: A 4-Variate Diffusion Process Approach Open
The evolution of the 4-variate probability distribution of the diameter at the breast height, total height, crown base height, and crown width against the age in a forest stand is of great interest to forest management and the evaluation o…
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A Multi-Variate Predictability Framework to Assess Invasive Cardiac Activity and Interactions During Atrial Fibrillation Open
The proposed framework can potentially help to guide catheter ablation interventions of AF.
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Statistical convergence of bivariate generalized bernstein operators via four-dimensional infinite matrices Open
Our main aim in this work is to construct an original extension of bivariate Bernstein type operators based on multiple shape parameters to give an application of four-dimensional infinite matrices to approximation theory, and prove some K…
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National Holidays and Social Mobility Behaviors: Alternatives for Forecasting COVID-19 Deaths in Brazil Open
In this paper, we investigate the influence of holidays and community mobility on the transmission rate and death count of COVID-19 in Brazil. We identify national holidays and hallmark holidays to assess their effect on disease reports of…
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Variate Associated Domain Adaptation for Unsupervised Multivariate Time Series Anomaly Detection Open
Multivariate Time Series Anomaly Detection (MTS-AD) is crucial for the effective management and maintenance of devices in complex systems, such as server clusters, spacecrafts, and financial systems, and so on. However, upgrade or cross-pl…
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FINITE MIXTURES OF MATRIX VARIATE T DISTRIBUTIONS Open
Finite mixture of multivariate t distributions (Peel and McLachlan, 2000) was introduced as an alternative to the finite mixture of multivariate normal distributions to model datasets with heavy tails. In this study, we define the finite m…
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Crop recommendation and yield prediction using machine learning algorithms Open
Agriculture is the foundation of many countries' economies, particularly in India and Tamil Nadu. The young generation who are new to farming may confront the challenge of not understanding what to sow and what to reap benefit from. This i…
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Reduced-Rank Tensor-on-Tensor Regression and Tensor-Variate Analysis of Variance Open
Fitting regression models with many multivariate responses and covariates can be challenging, but such responses and covariates sometimes have tensor-variate structure. We extend the classical multivariate regression model to exploit such …