Kismi En Kucuk Kareler Regresyonu Yardimiyla Optimum Bilesen Sayisini Secmede Model Secim Kriterlerinin Performans Karsilastimasi
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.
Volume (Year): 15 (2011)
Issue (Month): 1 (November)
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