Linear Model Regression Task
(Redirected from estimation in linear models)
A Linear Model Regression Task is an model estimation task whose input is a linear meta-model.
- Context:
- It can be solved by a Linear Model Estimation System (that implements a linear model estimation algorithm, such as the Lasso).
- …
- Counter-Example(s):
- See: Linear Model Classification Algorithm.
References
2006
- (Tibshirani, 1996) ⇒ Robert Tibshirani. (1996). “Regression Shrinkage and Selection via the Lasso.” In: Journal of the Royal Statistical Society, Series B, 58(1).
- QUOTE: We propose a new method for estimation in linear models. The “lasso” minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. …
… Consider the usual regression situation: we have data [math]\displaystyle{ (\mathbf{x}^i, y^i), i=1,2,...,N \ , }[/math] where [math]\displaystyle{ \mathbf{x}^i=(x_{i1},..., x_{ip})^T }[/math] and [math]\displaystyle{ y_i }[/math] are the regressors and response for the ith observation. The ordinary least squares (OLS) estimates are obtained by minimizing the residual squared error.
- QUOTE: We propose a new method for estimation in linear models. The “lasso” minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. …