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Bayesian method for solving the problem of multicollinearity in regression. (English. French summary) Zbl 1409.62057

Summary: The popular method of estimation in regression, Ordinary Least Squares (OLS) often displays inefficiency especially with large variances and wide confidence intervals thereby making precise estimate difficult when there is strong multicollinearity. Bayesian method of estimation is expected to improve the efficiency of estimated regression model when there is relevant prior information and belief of situation being modelled is available. This study however provided an alternative approach to OLS when there is almost perfect multicollinearity while its performance were compared with the aid of simulation approach to OLS estimator. Results of the simulation study indicate that with respect to Mean Squared Error (MSE) criterion and other criteria, the proposed method perform better than OLS.

MSC:

62F15 Bayesian inference
62H10 Multivariate distribution of statistics
62F25 Parametric tolerance and confidence regions
62G08 Nonparametric regression and quantile regression
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Full Text: Euclid