A comparaison between the two principal components regression methods and the Spekman method for the semi-parametric partial linear model
DOI:
https://doi.org/10.31272/jae.i141.1004Keywords:
semi-parametric, partial linear model, Spekman method, principal components regression .Abstract
Many researchers are interested in semi-parametric methods, which combine parametric and non-parametric methods, because they provide a more advanced level of statistical analysis that can be used to achieve high-level estimators. The importance of the research appears through the comparison between the two semi-parametric estimation methods and the use of the semi-parametric partial linear regression model. Two estimation methods were used, namely the Spykman method (PLMS) and the principal components regression method (PCR), using the simulation method and with different sample sizes (128, 256, 512) and different variances, which are (0.5, 1, 2), and through the simulation results it was concluded The researcher concluded that the best method is the principal components regression (PCR) method, which outperforms the Spekman method (PLMS) when the number of variables is eight variables and when linear, quadratic, and exponential smoothing functions are used.
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