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Random feature weights for regression trees
Progress in Artificial Intelligence, 2016Ensembles are learning methods the operation of which relies on a combination of different base models. The diversity of ensembles is a fundamental aspect that conditions their operation. Random Feature Weights ( $${\mathcal {RFW}}$$
Álvar Arnaiz-González +3 more
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Regression with random coefficients
Omega, 1978Abstract The first half of the seventies has seen a virtual explosion of interest in the use of random coefficient regression techniques which, for the most part, have been developed or refined by econometricians. However, given the importance of regression analysis as a managerial tool, it is surprising to find scant mention of random coefficient ...
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Iterative randomized robust linear regression
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015A promising approach when dealing with massive data sets is to apply randomized dimensionality reduction and then operate in lower dimensions. This paper deals with the randomized linear regression task in the case where the available data are sporadically corrupted.
Yannis Kopsinis +2 more
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Wavelet regression for random or irregular design
Computational Statistics & Data Analysis, 1998zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Antoniadis, Anestis, Pham, Dinh Tuan
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Random regression coefficients
1994Abstract In Chapter 2 we studied the analysis of covariance with random effects as a method for the description of between-cluster differences.
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Prediction in Random Coefficient Regression Models
Biometrical Journal, 1990AbstractMuch attention has been given to the problem of predicting future observations for some individual within a random coefficient regression (RCR) model, using the previous observations on that individual as well as the information from the rest of the data material.
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Advances in Methodology for Random Regression Analyses*
Australian Journal of Experimental Agriculture, 2005Random regression analyses have become standard methodology for the analysis of traits with repeated records that are thought of as representing points on a trajectory. Modelling curves as a regression on functions of a continuous covariable, such as time, for each individual, random regression models are readily implemented in standard, linear mixed ...
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Drop-outs and a random regression model
Journal of Biopharmaceutical Statistics, 1997The implications of drop-outs for power of random regression model (RRM) tests of significance for differences in the rate of change produced by two treatments in a randomized parallel-groups design were investigated by Monte Carlo simulation methods. The two-stage RRM fitted a least squares linear regression equation to all of the available data for ...
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