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Fast and robust bootstrap for LTS
2005
Computational Statistics & Data Analysis
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To overcome these problems, an alternative bootstrap method is proposed which is both computationally simple and robust. ...
The Least Trimmed Squares (LTS) estimator is a frequently used robust estimator of regression. ...
Acknowledgements We are grateful to Elke Maesen for contributing to the simulation study. We also thank both referees for their helpful comments and remarks. ...
doi:10.1016/j.csda.2004.03.018
fatcat:zgk4aswxuvhlnkje4acjaecd54
Weighted Split Sample Bootstrap for Regression Models with High Dimensional Data
2016
Indian Journal of Science and Technology
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The proposed bootstrap procedure gives bootstrap estimates having smaller bootstrap estimates of the standard errors and as a result, we get narrow confidence intervals of the estimates. ...
In case of outliers in the data, the classical bootstrap procedure fails to give us fine results even if robust regression estimates are used. ...
This procedure is simple, larger number of outliers as compared to the number of fast and robust (also known as fast and robust bootstrap outliers in the original sample [2] . ...
doi:10.17485/ijst/2016/v9i28/97789
fatcat:37ceserxi5en7modd6uii7jns4
Rapid Replanning in Consecutive Pick-and-Place Tasks with Lazy Experience Graph
[article]
2022
arXiv
pre-print
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Previous experience is summarised in a lazy graph structure, and LTR* is formulated to be robust and beneficial regardless of the extent of changes in the workspace. ...
Experimentally, we show that in repeated pick-and-place tasks, LTR* attains a high gain in performance when planning for subsequent tasks. ...
Sampling-based motion planners (SBPs) are a class of robust methods for motion planning [5] . ...
arXiv:2109.10209v2
fatcat:5dmifxsmfnfadbbeiuouhba5iq
A Least Trimmed Square Regression Method for Second Level fMRI Effective Connectivity Analysis
2012
Neuroinformatics
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A bootstrap method for the LTS estimation is employed to detect model outliers. We compared the LTS robust method with a non-robust method using simulated and real datasets. ...
The difference between LTS and the non-robust method for second level effective connectivity analysis is significant, suggesting the conventional non-robust method is easily affected by the model variability ...
Hess) and Kathy T. Mullen (#MOP10819). ...
doi:10.1007/s12021-012-9168-8
pmid:23093379
fatcat:crso36yp4rh6lkgximibcks6vu
A robust sparse linear approach for contaminated data
2019
Communications in statistics. Simulation and computation
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The performance of the proposed method is evaluated on a simulation study with a different type of outliers and high leverage points and also on a real data set. ARTICLE HISTORY ...
In this paper, we present a sparse linear approach which detects outliers by using a highly robust regression method. ...
Acknowledgments The authors would like to thank to the Associate Editor and the reviewers for their useful comments which led to a considerable improvement of the manuscript. ...
doi:10.1080/03610918.2019.1588304
fatcat:jn3cgjf6mfgphkdgf5rykke53u
Predictability Hidden by Anomalous Observations
2013
Social Science Research Network
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We propose a novel robust method for testing the null of no predictability, using predictive regression models that might be violated by a minority of anomalous observations. ...
Predictive relations motivated by financial theory should hold at least for the majority of the data, but they can be violated by a small fraction of the data. ...
We propose a new robust testing method for predictive regressions by introducing a novel general class of fast and robust bootstrap and subsampling procedures for times series. ...
doi:10.2139/ssrn.2237447
fatcat:of5fso5c2jgnddbemywmbphzbq
High-Breakdown Robust Multivariate Methods
2008
Statistical Science
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We give an overview of recent high-breakdown robust methods for multivariate settings such as covariance estimation, multiple and multivariate regression, discriminant analysis, principal components and ...
The goal of robust statistics is to develop methods that are robust against the possibility that one or several unannounced outliers may occur anywhere in the data. ...
ACKNOWLEDGMENTS We would like to thank Sanne Engelen, Karlien Vanden Branden and Sabine Verboven for help with preparing the figures of this paper. ...
doi:10.1214/088342307000000087
fatcat:fmak7kc7pnfivnzhzjy6ce4cfm
On robust linear regression with incomplete data
2000
Computational Statistics & Data Analysis
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The combination of the forward search algorithm for high breakdown point estimators and the EM algorithm or multiple imputation for missing values can avoid problems of this kind. ...
In this paper, we use recently developed methods of very robust regression to extend missing value techniques to data with several outliers. ...
squares (LTS) (see Rousseeuw, 1984; Rousseeuw and Leroy, 1987) . ...
doi:10.1016/s0167-9473(99)00061-4
fatcat:53r2tytzmvhi7pewjua3tbl3ve
Page 441 of Mathematical Reviews Vol. , Issue 2004a
[page]
2004
Mathematical Reviews
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Welsch, Robust portfolio optimization (235-245); R. Y. Liu [Regina Y. C. Liu], BootQC: bootstrap for robust analysis of aviation safety data. Sta- tistical quality control by bootstrap (246-258); S. ...
Gonzalez Carmona, Exploring the structure of regression surfaces by using SiZer map for additive models (361-366); Roland Fried and Ur- sula Gather, Fast and robust filtering of time series with trends ...
Robust multivariate methods in Chemometrics
[article]
2020
arXiv
pre-print
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for some methods for multivariate analysis frequently used in chemometrics, such as principal component analysis and partial least squares. ...
Following a description of the basic ideas and concepts behind robust statistics, including how robust estimators can be conceived, the chapter builds up to the construction (and use) of robust alternatives ...
For material on specific robust methods for chemometrics, as no textbook on this subject exists, the reader is referred to articles, i.e. the corresponding references in the list given below. ...
arXiv:2006.01617v1
fatcat:kslgpipl4zap3hbbhm6rqb6mte
Robust Multivariate Methods in Chemometrics
[chapter]
2009
Comprehensive Chemometrics
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for some methods for multivariate analysis frequently used in chemometrics, such as principal component analysis and partial least squares. ...
Following a description of the basic ideas and concepts behind robust statistics, including how robust estimators can be conceived, the chapter builds up to the construction (and use) of robust alternatives ...
Fast and robust bootstrap exists (up to our knowledge) for S-estimators, 62 and least trimmed squares. 61
Model selection The last important issue concerning robust estimation, is model selection ...
doi:10.1016/b978-044452701-1.00113-7
fatcat:qvh65q7oyzeihl5ldn7zapshri
Monte Carlo and Quasi-Monte Carlo for Statistics
[chapter]
2009
Monte Carlo and Quasi-Monte Carlo Methods 2008
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Acknowledgments I thank Pierre L'Ecuyer and the other organizers of MCQMC 2008 for inviting this tutorial, and for organizing such a productive meeting. ...
Thanks also to two anonymous reviewers and Pierre for helpful comments. This research was supported by grant DMS-0604939 of the U.S. National Science Foundation. ...
It brings a big improvement for this data, but it is still not robust, and gets fooled badly on other data. ...
doi:10.1007/978-3-642-04107-5_1
fatcat:zg4y5l36hrcmjjfhg2ywf5mrza
Uniform Inference for Characteristic Effects of Large Continuous-Time Linear Models
[article]
2018
arXiv
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Our procedure allows both known and estimated factors, and also features a bias correction for the effect of estimating unknown factors. ...
We show that the uniformity can be achieved by cross-sectional bootstrap. ...
Here we consider a highdimensional system where p → ∞ fast. ...
arXiv:1711.04392v2
fatcat:waekhifxajc23ap3a7b4o5p57y
Predictability Hidden by Anomalous Observations
[article]
2016
arXiv
pre-print
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Testing procedures for predictive regressions with lagged autoregressive variables imply a suboptimal inference in presence of small violations of ideal assumptions. ...
, both in- and out-of-sample, using predictive variables such as the dividend yield or the volatility risk premium. ...
Robust Subsampling and Robust Block Bootstrap for the studentized Statistic Tn. ...
arXiv:1612.05072v1
fatcat:owihavhvmvco3ox5pv6m5h6irq
Uniform Inference for Conditional Factor Models with Instrumental and Idiosyncratic Betas
2017
Social Science Research Network
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We show that a cross-sectional bootstrap procedure is essential for the uniform inference, and our procedure also features a bias correction for the effect of estimating unknown factors. ...
It is often the case that researchers do not know whether or not the idiosyncratic beta exists, or its strengths, and thus uniformity is essential for inferences. ...
The cross-sectional bootstrap is important because the discontinuity arises due to the strength of the cross-sectional variance of γ lt , and the cross-sectional bootstrap avoids the estimation error for ...
doi:10.2139/ssrn.3069985
fatcat:iktjxc7oqvcubjlm7k2q4m47ru
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