Results 21 to 30 of about 32,006,081 (306)
Tensor Completion for Weakly-dependent Data on Graph for Metro Passenger Flow Prediction [PDF]
AAAI Conference on Artificial Intelligence, 2019 Low-rank tensor decomposition and completion have attracted significant interest from academia given the ubiquity of tensor data. However, low-rank structure is a global property, which will not be fulfilled when the data presents complex and weak ...
Ziyue Li +4 more
semanticscholar +1 more source
Recovery guarantees for polynomial coefficients from weakly dependent data with outliers
Journal of Approximation Theory, 2020 Learning non-linear systems from noisy, limited, and/or dependent data is an important task across various scientific fields including statistics, engineering, computer science, mathematics, and many more.
L. Ho +3 more
semanticscholar +1 more source
Exact and Robust Conformal Inference Methods for Predictive Machine Learning With Dependent Data [PDF]
Annual Conference Computational Learning Theory, 2018 We extend conformal inference to general settings that allow for time series data. Our proposal is developed as a randomization method and accounts for potential serial dependence by including block structures in the permutation scheme.
V. Chernozhukov +2 more
semanticscholar +1 more source
Nonparametric analysis of the time structure of seismicity in a geographic region
Annals of Geophysics, 2002 As an alternative to traditional parametric approaches, we suggest nonparametric methods for analyzing temporal data on earthquake occurrences. In particular, the kernel method for estimating the hazard function and the intensity function are presented ...
A. Quintela-del-Río +2 more
doaj +1 more source
Strong consistency of a kernel-based rule for spatially dependent data [PDF]
Arab Journal of Mathematical Sciences, 2020 We consider the kernel-based classifier proposed by Younso (2017). This nonparametric classifier allows for the classification of missing spatially dependent data. The weak consistency of the classifier has been studied by Younso (2017).
Ahmad Younso, Ziad Kanaya, Nour Azhari
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Review, 2007 A speech at the Middle Tennessee State University, Annual Economic Outlook Conference, Murfreesboro, Tenn., Sept.
openaire +3 more sources
Dependent Functional Data [PDF]
ISRN Probability and Statistics, 2012 This paper reviews recent research on dependent functional data. After providing an introduction to functional data analysis, we focus on two types of dependent functional data structures: time series of curves and spatially distributed curves. We review statistical models, inferential methodology, and possible extensions.
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Stochastic interpolants with data-dependent couplings [PDF]
International Conference on Machine Learning, 2023 Generative models inspired by dynamical transport of measure -- such as flows and diffusions -- construct a continuous-time map between two probability densities.
M. Albergo +4 more
semanticscholar +1 more source
Gamma Kernel Estimation of the Density Derivative on the Positive Semi-Axis by Dependent Data
Revstat Statistical Journal, 2016 We estimate the derivative of a probability density function defined on [0,∞). For this purpose, we choose the class of kernel estimators with asymmetric gamma kernel functions.
L.A. Markovich
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Multiscale change point detection for dependent data [PDF]
Scandinavian Journal of Statistics, 2018 In this article we study the theoretical properties of the simultaneous multiscale change point estimator (SMUCE) in piecewise‐constant signal models with dependent error processes. Empirical studies suggest that in this case the change point estimate is
H. Dette, Theresa Eckle, Mathias Vetter
semanticscholar +1 more source