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Using principal components for estimating logistic regression with high-dimensional multicollinear data
Open accessAbstract
The logistic regression model is used to predict a binary response variable in terms of a set of explicative ones. The estimation of the model parameters is not too accurate and their interpretation in terms of odds ratios may be erroneous, when there is multicollinearity (high dependence) among the predictors. Other important problem is the great number of explicative variables usually needed to explain the response. In order to improve the estimation of the logistic model parameters under multicollinearity and to reduce the dimension of the problem with continuous covariates, it is proposed to use as covariates of the logistic model a reduced set of optimum principal components of the original predictors. Finally, the performance of the proposed principal component logistic regression model is analyzed by developing a simulation study where different methods for selecting the optimum principal components are compared.
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Metadata
- Authors
- Aguilera, A. M., Escabias, M., & Valderrama, M. J.
- Venue
- Computational Statistics & Data Analysis
- Publisher
- Elsevier BV
- Published
- 2006-04
- Volume
- 50
- Issue
- 8
- Pages
- 1905-1924
- ISSN
- 0167-9473 (ISSN-L)
- OpenAlex
- W1995523953
- Cited by
- 227
- References
- 0
- FWCI
- 5.31
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Aguilera, A. M., Escabias, M., & Valderrama, M. J. (2006). Using principal components for estimating logistic regression with high-dimensional multicollinear data. Computational Statistics & Data Analysis. https://doi.org/10.1016/j.csda.2005.03.011
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