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Kismi En Kucuk Kareler Regresyonu Yardimiyla Optimum Bilesen Sayisini Secmede Model Secim Kriterlerinin Performans Karsilastimasi


  • Elif BULUT

    () (Ondokuz Mayis University)


    () (Mugla University)


Partial Least Squares Regression (PLSR) is a multivariate statistical method for constructing predictive models when the variables are many and highly collinear. Its goal is to predict a set of response variables from a set of predictor variables. This prediction is achieved by extracting a set of orthogonal factors called latent variables from the predictor variables. This study investigated the performances of model selection criteria in selecting the optimum number of latent variables from PLSR models for data sets that have various observations and variable numbers. Their performances have been compared in a simulation study with k-fold cross validation. This simulation has been performed to compare the performance of MAIC (Bedrick & Tsai, 1994), MAIC (Bozdogan, 2000), MA_opt(PRESS) and Wold’s R criterion in finding the optimum number of latent variables. The simulation results show that all the criteria achieved the optimum number of latent variables for a small-sized design matrix. But when the data dimensions get bigger, MAKAKIE and MBEDRICK could not find the optimum number of latent variables. MA_opt(PRESS) and Wold’s R criteria gave almost the same results and found the optimum number of latent variables with a better performance than the MAIC’s.

Suggested Citation

  • Elif BULUT & Ozlem GURUNLU ALMA, 2011. "Kismi En Kucuk Kareler Regresyonu Yardimiyla Optimum Bilesen Sayisini Secmede Model Secim Kriterlerinin Performans Karsilastimasi," Istanbul University Econometrics and Statistics e-Journal, Department of Econometrics, Faculty of Economics, Istanbul University, vol. 15(1), pages 38-52, November.
  • Handle: RePEc:ist:ancoec:v:15:y:2011:i:1:p:38-52

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    More about this item


    Partial Least Squares; MAIC (Multivariate Akaike Information Criterion); PRESS (Predicted Residual Sum of Squares); Wold’s R.;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques


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