Optimal two-step prediction in regression

Optimal Prediction in Linear Regression Analysis

Expressions are derived for generalized ridge and ordinary ridge predictors that are optimal in terms of mean squared error of prediction (MSEP) for predicting the response at a single or at multiple future observation(s). Using the MSEP criterion, operational predictors are compared to the ordinary least squares (OLS) predictor and to several biased predictors derived from some popular biased ...

Regression quantiles and their two-step modifications

Regression quantiles and their two-step modifications based on R-estimation of slope components of regression parameter are asymptotically equivalent and are very close numerically even for small sample sizes; their extreme versions even exactly coincide. Regarding that, we study their relations under finite samples and show under which conditions their values exactly coincide. This interrelati...

Two-step optimal thermal generation scheduling

A new approach to the solution of the optimal thermal generation scheduling problem is presented. The problem is solved in two steps. As a first step, the optimal production schedule for the next day is obtained based on a daily load forecast, reserve capacity requirements, and present status of generating units. The second-step algorithm uses the results of the first step and adjusts the previ...

Oracally efficient two-step estimation for additive regression

A Two-Step Penalized Regression Method with Networked Predictors.

Penalized regression incorporating prior dependency structure of predictors can be effective in high-dimensional data analysis (Li and Li 2008). Pan, Xie and Shen (2010) proposed a penalized regression method for better outcome prediction and variable selection by smoothing parameters over a given predictor network, which can be applied to analysis of microarray data with a given gene network. ...