Tesis
Comparação de métodos de estimação para problemas com colinearidade e/ou alta dimensionalidade (p > n)
Fecha
2016-04-29Registro en:
Autor
Casagrande, Marcelo Henrique
Institución
Resumen
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.