We apply these LASSO methods to evaluate the short- and long-horizon predictability of S&P 500 excess returns.doi:10.1016/j.jeconom.2021.02.002Ji Hyung LeeZhentao ShiZhan GaoJournal of EconometricsLee, J., Z. Shi, and Z. Gao (2018): "On Lasso for predictive regressions," arxiv:...
In prognostic studies, the lasso is particularly appealing for its ability to shrink regression coefficients and automatically perform variable selection by setting some coefficients to zero. This improves predictive performance and introduces parsimony. Models with fewer predictor variables are usually easier...
In this paper, we give new insights into this relationship in the context of multiple linear regression. We show, in particular, that the incorporation of a simple correlation measure into the tuning parameter leads to a nearly optimal prediction performance of the Lasso even for highly correlated...
and vice versa. Shi et al. provided LRF-DTIs which exploits pseudo-position specific scoring matrix (PsePSSM) and FP2 molecular fingerprint to obtain the raw features, and after dimension reduction by Lasso, the random forest is used to classify [29]. Similar to LRF-DTIs,...
(AUC) for predicting mortality was 0.735(95%CI:0.667-0.804) for the Fried frailty phenotype and 0.777(95%CI:0.713-0.840) for CGA-FI.A predictive model constructed using LASSO regression with 6 selected factors achieved an AUC of 0.882 (95%CI:0.835-0.929).The average AUC validated by Boot...
(identified_estimand,method_name="backdoor.econml.dml.DML",control_value=0,treatment_value=1,target_units=lambdadf:df["X0"]>1,confidence_intervals=False,method_params={"init_params":{'model_y':GradientBoostingRegressor(),'model_t':GradientBoostingRegressor(),'model_final':LassoCV(),'featur...
The two most widely used regularization techniques are LASSO (L1) and Ridge (L2). L1 adds the mean absolute error and L2 adds mean squared error to the loss. Without going into too many mathematical details, the basic differences are: lasso regression (L1) does both variable selection and ...
Forest plots of multivariable analysis showing the LASSO score and adjuvant chemotherapy as independent prognostic factors for OS. The center of the Forest plot represents the HR of the Cox proportional hazards model, the error bars are two-sided 95% CIs (Cox regression multivariable analysis). N...
The optimal survival-related genes in the training cohort were selected by LASSO analysis based on the “glmnet” R package75. Tenfold cross-validation was used for filtering, and the λ value was obtained. Meanwhile, the coefficients of the selected genes were acquired. Then, the risk score ...
The prediction models that used only imaging features showed a high correlation for all three approaches. The correlation values derived from mRMR and LASSO were higher than our approach partially due to the nature of the mRMR and LASSO. This may be because LASSO and mRMR are tailored for extra...