Contents
What is lasso logistic regression?
LASSO is a penalized regression approach that estimates the regression coefficients by maximizing the log-likelihood function (or the sum of squared residuals) with the constraint that the sum of the absolute values of the regression coefficients, ∑ j = 1 k β j , is less than or equal to a positive constant s.
Why adaptive lasso?
the adaptive lasso simply adds weights to this to try to counteract the known issue of LASSO estimates being biased. . In the glmnet package the weights can be specified with the penalty.
Does lasso have oracle property?
The lasso is a popular technique for simultaneous estimation and variable selection. We show that the adaptive lasso enjoys the oracle properties; namely, it performs as well as if the true underlying model were given in advance. Similar to the lasso, the adaptive lasso is shown to be near-minimax optimal.
What is Oracle Property statistics?
The oracle property means that the penalized estimator is asymptotically equivalent to the oracle estimator that is the ideal estimator obtained only with signal variables without penalization. Many noncovex penalties such as the bridge and SCAD. penalties possess the oracle property.
Which is better the adaptive version of Lasso?
The LASSO has an adaptive version that has some better properties regarding variable selection. Note that this does not always means better forecast. The idea behind the model is to use some previously know information to select the variables more efficiently. This information is, in general, the coefficients estimated by LASSO or some other model.
How is the adaptive lasso used in logistic regression?
The adaptive LASSO (APLR) was originally proposed to overcome the selection bias by assigning a consistent weight to each gene. In high-dimensional data, however, the adaptive LASSO faces practical problems in choosing the type of initial weight. In practice, the LASSO estimator itself has been used as an initial weight.
Can a lasso be used as an initial weight?
In practice, the LASSO estimator itself has been used as an initial weight. However, this may not be preferable because the LASSO is inconsistent in itself. To address this issue, an alternative initial weight in adaptive penalized logistic regression (CBPLR) is proposed.
What is the purpose of the LASSO model?
The LASSO is a model that uses a penalization on the size of the parameters in the objective function to try to exclude irrelevant variables from the model. It has two very natural uses, the first is variable selection, and the second is forecasting.