This article investigates a new parameter for the high-dimensional regression with noise: the distortion.
IFS has been widely used to solve high dimensional regression [ 65] and classification problems [ 66– 66].
Penalized likelihood methods can be applied to these high dimensional regression problems to perform model selection.
Network-based approaches have recently gained considerable popularity in high- dimensional regression settings.
Positioning Alus among the most relevant variables confirms our prior analyses based on 1 and 2 dimensional regression.
More recent applications include dimensionality reduction prior to gene set testing [ 12, 13] and high-dimensional regression [ 14].
The Lasso is an attractive regularisation method for high-dimensional regression.
HyperLasso regression (HL, Hoggart et al. [ 2008 ]) is a penalised regression method that simultaneously considers all predictor variables in a high-dimensional regression problem.
This method is used to extract relevant information for high-dimensional regression problems and also for noisy data.
The example also illustrates a very important concept: in high-dimensional regressions it is possible to have similar predictions with very different estimates of effects.
The notion of inverse regression works with the curve computed by (text{ E }(mathbf X |Y),) which consists of p one-dimensional regressions.
