Heavy-tailed distributions in haptic perception of wielded rods.
Sign Up to like & getrecommendations! Published in 2021 at "Experimental brain research"
DOI: 10.1007/s00221-021-06131-7
Abstract: Humans identify properties (e.g., the length or weight) of objects through touch using somatosensory perceptions in the limbs. Humans identify these properties by manipulating an object to access its inertial qualities. However, there is little… read more here.
Keywords: haptic perception; angular acceleration; heavy tailed; tailed distributions ... See more keywords
Concentration and moment inequalities for sums of independent heavy-tailed random matrices
Sign Up to like & getrecommendations! Published in 2024 at "Probability Theory and Related Fields"
DOI: 10.1007/s00440-025-01412-6
Abstract: We prove Fuk-Nagaev and Rosenthal-type inequalities for sums of independent random matrices, focusing on the situation when the norms of the matrices possess finite moments of only low orders. Our bounds depend on the “intrinsic”… read more here.
Keywords: moment inequalities; random matrices; heavy tailed; sums independent ... See more keywords
Heavy-tailed phase-type distributions: a unified approach
Sign Up to like & getrecommendations! Published in 2022 at "Extremes"
DOI: 10.1007/s10687-022-00436-8
Abstract: A phase-type distribution is the distribution of the time until absorption in a finite state-space time-homogeneous Markov jump process, with one absorbing state and the rest being transient. These distributions are mathematically tractable and conceptually… read more here.
Keywords: time; heavy tailed; phase type; type distributions ... See more keywords
Heavy Lasso: sparse penalized regression under heavy-tailed noise via data-augmented soft-thresholding
Sign Up to like & getrecommendations! Published in 2025 at "Statistics and Computing"
DOI: 10.1007/s11222-025-10785-6
Abstract: High-dimensional linear regression is a fundamental tool in modern statistics, particularly when the number of predictors exceeds the sample size. The classical Lasso, which relies on the squared loss, performs well under Gaussian noise assumptions… read more here.
Keywords: heavy lasso; tailed noise; heavy tailed; regression ... See more keywords
Large-Scale Estimation of Distribution Algorithms with Adaptive Heavy Tailed Random Projection Ensembles
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Computer Science and Technology"
DOI: 10.1007/s11390-019-1973-1
Abstract: We present new variants of Estimation of Distribution Algorithms (EDA) for large-scale continuous optimisation that extend and enhance a recently proposed random projection (RP) ensemble based approach. The main novelty here is to depart from… read more here.
Keywords: large scale; estimation distribution; random projection; heavy tailed ... See more keywords
Heavy-tailed longitudinal regression models for censored data: a robust parametric approach
Sign Up to like & getrecommendations! Published in 2018 at "TEST"
DOI: 10.1007/s11749-018-0603-5
Abstract: Longitudinal HIV-1 RNA viral load measures are often subject to censoring due to upper and lower detection limits depending on the quantification assays. A complication arises when these continuous measures present a heavy-tailed behavior because… read more here.
Keywords: regression; heavy tailed; regression models; models censored ... See more keywords
Coverings of random ellipsoids, and invertibility of matrices with i.i.d. heavy-tailed entries
Sign Up to like & getrecommendations! Published in 2018 at "Israel Journal of Mathematics"
DOI: 10.1007/s11856-018-1732-y
Abstract: Let A = (aij) be an n × n random matrix with i.i.d. entries such that Ea11 = 0 and Ea112 = 1. We prove that for any δ > 0 there is L >… read more here.
Keywords: invertibility matrices; heavy tailed; random ellipsoids; matrices heavy ... See more keywords
Kernel-based testing with skewed and heavy-tailed data: Evidence from a nonparametric test for heteroskedasticity
Sign Up to like & getrecommendations! Published in 2018 at "Economics Letters"
DOI: 10.1016/j.econlet.2018.08.007
Abstract: We examine the performance of a nonparametric kernel-based specification test in the presence of skewed and heavy-tailed regressors. We start by modifying the Zheng (2009) test for heteroskedasticity by removing the random denominator in the… read more here.
Keywords: kernel based; test heteroskedasticity; heavy tailed; test ... See more keywords
A modified bootstrap for kernel-based specification test with heavy-tailed data
Sign Up to like & getrecommendations! Published in 2020 at "Economics Letters"
DOI: 10.1016/j.econlet.2020.108986
Abstract: Abstract This paper provides a new resampling strategy to improve the finite sample performance of a nonparametric kernel-based specification test in the presence of heavy-tailed error terms. Based on the test statistic of Li and… read more here.
Keywords: kernel based; heavy tailed; test; specification test ... See more keywords
A Novel Robust Kalman Filter With Non-stationary Heavy-tailed Measurement Noise
Sign Up to like & getrecommendations! Published in 2020 at "IFAC-PapersOnLine"
DOI: 10.1016/j.ifacol.2020.12.188
Abstract: Abstract A novel robust Kalman filter based on Gaussian-Student’s t mixture (GSTM) distribution is proposed to address the filtering problem of a linear system with non-stationary heavy-tailed measurement noise. The mixing probability is recursively estimated… read more here.
Keywords: heavy tailed; tailed measurement; non stationary; robust kalman ... See more keywords
A novel optimization algorithm for MIMO Hammerstein model identification under heavy-tailed noise.
Sign Up to like & getrecommendations! Published in 2018 at "ISA transactions"
DOI: 10.1016/j.isatra.2017.10.001
Abstract: In this paper, we study the system identification of multi-input multi-output (MIMO) Hammerstein processes under the typical heavy-tailed noise. To the best of our knowledge, there is no general analytical method to solve this identification… read more here.
Keywords: algorithm; heavy tailed; optimization; hammerstein ... See more keywords