0000000000502278
AUTHOR
G Sottile
showing 2 related works from this author
Non-crossing quantile regression via monotone B-spline varying coefficients
2019
Quantile regression can be used to obtain a nonparametric estimate of a conditional quantile function. The presence of quantile crossing, however, leads to an invalid distribution of the response and makes it dicult to use the tted model for prediction. In this work, we show that crossing can be alleviated or completely eliminated by explicit modeling of the regression coecients as a function of the percentile values in (0,1). We illustrate the approach via a wellknown dataset by emphasizing dierences with respect to the competitors.
A new tuning parameter selector in lasso regression
2019
Penalized regression models are popularly used in high-dimensional data analysis to carry out variable selction and model fitting simultaneously. Whereas success has been widely reported in literature, their performance largely depend on the tuning parameter that balances the trade-off between model fitting and sparsity. In this work we introduce a new tuning parameter selction criterion based on the maximization of the signal-to-noise ratio. To prove its effectiveness we applied it to a real data on prostate cancer disease.