A Theory of Dichotomous Valuation with Applications to Variable Selection

08/01/2018
by   Xingwei Hu, et al.
0

An econometric or statistical model may undergo a marginal gain when a new variable is admitted, and a marginal loss if an existing variable is removed. The value of a variable to the model is quantified by its expected marginal gain and marginal loss. Assuming the equality of opportunity, we derive a few formulas which evaluate the overall performance in potential modeling scenarios. However, the value is not symmetric to marginal gain and marginal loss; thus, we introduce an unbiased solution. Simulation studies show that our new approaches significantly outperform a few practice-used variable selection methods.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/23/2021

Identifying Gene-environment interactions with robust marginal Bayesian variable selection

In high-throughput genetics studies, an important aim is to identify gen...
research
07/05/2020

Geographically Weighted Regression Analysis for Spatial Economics Data: a Bayesian Recourse

The geographically weighted regression (GWR) is a well-known statistical...
research
03/11/2020

Lifted samplers for partially ordered discrete state-spaces

A technique called lifting is employed in practice for avoiding that the...
research
11/05/2020

Nonparametric Variable Screening with Optimal Decision Stumps

Decision trees and their ensembles are endowed with a rich set of diagno...
research
10/05/2021

Feature Selection by a Mechanism Design

In constructing an econometric or statistical model, we pick relevant fe...
research
07/10/2019

Bayesian Variable Selection for Non-Gaussian Responses: A Marginally Calibrated Copula Approach

We propose a new highly flexible and tractable Bayesian approach to unde...
research
09/22/2020

The Linear Lasso: a location model resolution

We use location model methodology to guide the least squares analysis of...

Please sign up or login with your details

Forgot password? Click here to reset