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Regression With Fuzzy Random Data
Computational Statistics and Data Analysis, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wolfgang Näther
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Online Rebuilding Regression Random Forests
Knowledge-Based Systems, 2021Abstract Continuous data streams mining is particularly challenging for machine learning. Many efforts have been devoted to propose online learning algorithms that can train iteratively from new coming data and provide evolutionary predictions. Compared to off-line approaches, these algorithms have shown better predictive performance and certain ...
Yuan Zhong +3 more
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Randomized algorithms for tensor response regression
Statistical Analysis and Data Mining: The ASA Data Science Journal, 2022AbstractIn this paper, we consider the estimation algorithm of tensor response on vector covariate regression model. Based on projection theory of tensor and the idea of randomized algorithm for tensor decomposition, three new algorithms named SHOLRR, RHOLRR and RSHOLRR are proposed under the low‐rank Tucker decomposition and some theoretical analyses ...
Zhe Cheng 0003 +3 more
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THE COEFFICIENT REGULARIZED REGRESSION WITH RANDOM PROJECTION
International Journal of Wavelets, Multiresolution and Information Processing, 2012In this paper, the coefficient regularized regression algorithm with random projection is proposed. The excess error of the proposed algorithm associated with the reproducing kernel Hilbert space is bounded. Theoretical analysis shows that it is possible to learn directly in the projected domain and that random projection, as a preprocessing step in ...
Di-Rong Chen, Han Li
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On a Regression Problem for Random Processes
Theory of Probability & Its Applications, 1987Consider the random process \(\xi (t)=m\cdot \theta (t)+\Delta (t)\), \(t\in [0,T]\), where \(\theta (t)=t^{\nu}+a_ 1t^{\nu -1}+...+a_{\nu}\) is a polynomial, \(\Delta\) (t) a generalized stationary random process with spectral density \(f(\lambda)=f_ w(\lambda)g(\lambda)\), \(f_ w(\lambda)=0\) if \(| \lambda | \leq w\), and \(=1/2\pi\) if \(| \lambda |
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Journal of the American Statistical Association, 1969
Abstract The use of observations of a random function in space (random field) as independent variables in regression is considered including the numerical aspects. Details are presented for obtaining a numerical approximation to a Karhunen-Loeve expansion when the random function is observed at a large number of points.
Ayala Cohen, Richard H. Jones
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Abstract The use of observations of a random function in space (random field) as independent variables in regression is considered including the numerical aspects. Details are presented for obtaining a numerical approximation to a Karhunen-Loeve expansion when the random function is observed at a large number of points.
Ayala Cohen, Richard H. Jones
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The Validity of Polynomial Regression in the Random Regression Model
Review of Educational Research, 1978Sockloff (1976), in reviewing the appropriateness of fixed and random models in regression analysis, has concluded "that the analysis of nonlinearity via polynomial and product regression should be limited to experimental studies" (p. 288), and that, "[r]egarding the analysis of nonlinearity in observational data under the Random Model, the Random ...
Elliot M. Cramer, Mark I. Appelbaum
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Online random forests regression with memories
Knowledge-Based Systems, 2020Abstract In recent years, the online schema of the conventional Random Forests(RFs) have attracted much attention because of its ability to handle sequential data or data whose distribution changes during the prediction process. However, most research on online RFs focuses on structural modification during the training stage, overlooking critical ...
Yuan Zhong +3 more
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Random sliced inverse regression
Communications in Statistics - Simulation and Computation, 2015ABSTRACTSliced Inverse Regression (SIR; 1991) is a dimension reduction method for reducing the dimension of the predictors without losing regression information. The implementation of SIR requires inverting the covariance matrix of the predictors—which has hindered its use to analyze high-dimensional data where the number of predictors exceed the ...
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