We consider estimation and variable selection in the partial linear model for censored data. The partial linear model for censored data is a direct extension of the accelerated failure time model, the latter of which is a very important alternative model to the proportional hazards model. We extend rank-based lasso-type estimators to a model that may contain nonlinear effects. Variable selectio...

Rainfall is one of the climatic elements in tropics which very influential agriculture, especially determining growing season. Thus, proper rainfall modeling needed to help determine best time start cultivating soil. can be done using Statistical Downscaling (SDS) method. SDS a statistical model field climatology analyze relationship between large-scale and small-scale climate data. This study ...

A great deal of interest has recently focused on conducting inference the parameters in a high-dimensional linear model. In this paper, we consider simple and very naïve two-step procedure for task, which (i) fit lasso model order to obtain subset variables, (ii) least squares lasso-selected set. Conventional statistical wisdom tells us that cannot make use standard tools resulting (such as con...

The Dantzig Selector is a recent approach to estimation in high-dimensional linear regression models with a large number of explanatory variables and a relatively small number of observations. As in the least absolute shrinkage and selection operator (LASSO), this approach sets certain regression coefficients exactly to zero, thus performing variable selection. However, such a framework, contra...

We consider the least-square linear regression problem with regularization by the l 1-norm, a problem usually referred to as the Lasso. In this paper, we first present a detailed asymptotic analysis of model consistency of the Lasso in low-dimensional settings. For various decays of the regularization parameter, we compute asymptotic equivalents of the probability of correct model selection. Fo...

Many statistical machine learning techniques sacrifice convenient computational structures to gain estimation robustness and modeling flexibility. In this paper, we study this fundamental tradeoff through a SQRT-Lasso problem for sparse linear regression and sparse precision matrix estimation in high dimensions. We explain how novel optimization techniques help address these computational chall...

We assessed the ability of several penalized regression methods for linear and logistic models to identify outcome-associated predictors and the impact of predictor selection on parameter inference for practical sample sizes. We studied effect estimates obtained directly from penalized methods (Algorithm 1), or by refitting selected predictors with standard regression (Algorithm 2). For linear ...