Asymptotics of self-weighted M-estimators for autoregressive models
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DOI: 10.1007/s00184-016-0592-x
Abstract: In this paper, we consider a stationary autoregressive AR(p) time series $$y_t=\phi _0+\phi _1y_{t-1}+\cdots +\phi _{p}y_{t-p}+u_t$$yt=ϕ0+ϕ1yt-1+⋯+ϕpyt-p+ut. A self-weighted M-estimator for the AR(p) model is proposed. The asymptotic normality of this estimator is established, which includes… read more here.
Keywords: weighted estimators; autoregressive models; self weighted; asymptotics self ... See more keywords
Asymptotics for the conditional self-weighted M-estimator of GRCA(1) models with possibly heavy-tailed errors
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DOI: 10.1007/s00362-019-01141-8
Abstract: Consider a generalized random coefficient AR(1) model, $$y_t=\Phi _t y_{t-1}+u_t$$ , where $$\{(\Phi _t, u_t)^\prime , t\ge 1\}$$ is a sequences of i.i.d. random vectors, and a conditional self-weighted M-estimator of $$\textsf {E}\Phi _t$$ is… read more here.
Keywords: estimator; self weighted; weighted estimator; asymptotics conditional ... See more keywords
Face image set classification with self-weighted latent sparse discriminative learning
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DOI: 10.1007/s00521-020-05479-1
Abstract: Since image set classification has strong power to overcome various variations in illumination, expression, pose, and so on, it has drawn extensive attention in recent years. Noteworthily, the point-to-point distance-based methods have achieved the promising… read more here.
Keywords: classification; latent sparse; self weighted; image set ... See more keywords
Self-Weighted Multi-View Fuzzy Clustering With Multiple Graph Learning
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DOI: 10.1109/lsp.2025.3558161
Abstract: Graph-based multi-view clustering has garnered considerable attention owing to its effectiveness. Nevertheless, despite the promising performance achieved by previous studies, several limitations remain to be addressed. Most graph-based models employ a two-stage strategy involving relaxation… read more here.
Keywords: view; self weighted; weighted multi; view fuzzy ... See more keywords
Trustworthiness of Process Monitoring in IIoT Based on Self-Weighted Dictionary Learning
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DOI: 10.1109/tii.2022.3205638
Abstract: Process monitoring, a typical application of industrial Internet of Things (IIOT), is crucial to ensure the reliable operation of the industrial system. In practice, due to the harsh environment and unreliable sensors and actuators, it… read more here.
Keywords: trustworthiness process; weighted dictionary; self weighted; process monitoring ... See more keywords
Kernelized Multiview Subspace Analysis By Self-Weighted Learning
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DOI: 10.1109/tmm.2020.3032023
Abstract: With the popularity of multimedia technology, information is always represented from multiple views. Even though multiview data can reflect the same sample from different perspectives, multiple views are consistent to some extent because they are… read more here.
Keywords: multiview; multiview data; subspace analysis; kernelized multiview ... See more keywords
Self-Weighted Supervised Discriminative Feature Selection
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DOI: 10.1109/tnnls.2017.2740341
Abstract: In this brief, a novel self-weighted orthogonal linear discriminant analysis (SOLDA) problem is proposed, and a self-weighted supervised discriminative feature selection (SSD-FS) method is derived by introducing sparsity-inducing regularization to the proposed SOLDA problem. By… read more here.
Keywords: feature selection; weighted supervised; method; selection ... See more keywords
Self-Weighted Unsupervised LDA.
Sign Up to like & getrecommendations! Published in 2021 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2021.3105196
Abstract: As a hot topic in unsupervised learning, clustering methods have been greatly developed. However, the model becomes more and more complex, and the number of parameters becomes more and more with the continuous development of… read more here.
Keywords: self weighted; unsupervised lda; method; weighted unsupervised ... See more keywords
Self-Weighted Quantile Estimation for Drift Coefficients of Ornstein–Uhlenbeck Processes with Jumps and Its Application to Statistical Arbitrage
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DOI: 10.3390/math13091399
Abstract: The estimation of drift parameters in the Ornstein–Uhlenbeck (O-U) process with jumps primarily employs methods such as maximum likelihood estimation, least squares estimation, and least absolute deviation estimation. These methods generally assume specific error distributions… read more here.
Keywords: self weighted; weighted quantile; estimation drift; estimator ... See more keywords