Comparação de métodos de estimação para problemas com colinearidade e/ou alta dimensionalidade (p > n)

• Autor(a) principal: Casagrande, Marcelo Henrique
• Data de Publicação: 2016
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFSCAR
• Texto Completo: https://repositorio.ufscar.br/handle/ufscar/7954
• topic: Regressão ridge; LASSO; Mínimos quadrados parciais; Regressão por componentes principais; Alta dimensionalidade; Ridge regression; Partial least squares; Principal component regression; High dimensionality; CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA

This paper presents a comparative study of the predictive power of four suitable regression methods for situations in which data, arranged in the planning matrix, are very poorly multicolinearity and / or high dimensionality, wherein the number of covariates is greater the number of observations. In this study, the methods discussed are: principal component regression, partial least squares regression, ridge regression and LASSO. The work includes simulations, wherein the predictive power of each of the techniques is evaluated for di erent scenarios de ned by the number of covariates, sample size and quantity and intensity ratios (e ects) signi cant, highlighting the main di erences between the methods and allowing for the creating a guide for the user to choose which method to use based on some prior knowledge that it may have. An application on real data (not simulated) is also addressed.