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STUDY OF PARTIAL LEAST SQUARES AND RIDGE REGRESSION METHODS

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dc.creatorLUIS ALBERTO FIRINGUETTI LIMONE
dc.date2017
dc.date.accessioned2025-01-10T14:56:12Z
dc.date.available2025-01-10T14:56:12Z
dc.date.issued2017
dc.description.abstractTHIS ARTICLE CONSIDERS BOTH PARTIAL LEAST SQUARES (PLS) AND RIDGE REGRESSION (RR) METHODS TO COMBAT MULTICOLLINEARITY PROBLEM. A SIMULATION STUDY HAS BEEN CONDUCTED TO COMPARE THEIR PERFORMANCES WITH RESPECT TO ORDINARY LEAST SQUARES (OLS). WITH VARYING DEGREES OF MULTICOLLINEARITY, IT IS FOUND THAT BOTH, PLS AND RR, ESTIMATORS PRODUCE SIGNIFICANT REDUCTIONS IN THE MEAN SQUARE ERROR (MSE) AND PREDICTION MEAN SQUARE ERROR (PMSE) OVER OLS. HOWEVER, FROM THE SIMULATION STUDY IT IS EVIDENT THAT THE RR PERFORMS BETTER WHEN THE ERROR VARIANCE IS LARGE AND THE PLS ESTIMATOR ACHIEVES ITS BEST RESULTS WHEN THE MODEL INCLUDES MORE VARIABLES. HOWEVER, THE ADVANTAGE OF THE RIDGE REGRESSION METHOD OVER PLS IS THAT IT CAN PROVIDE THE 95% CONFIDENCE INTERVAL FOR THE REGRESSION COEFFICIENTS WHILE PLS CANNOT.
dc.formatapplication/pdf
dc.identifier.doi10.1080/03610918.2016.1210168
dc.identifier.issn1532-4141
dc.identifier.issn0361-0918
dc.identifier.urihttps://repositorio.ubiobio.cl/handle/123456789/9635
dc.languagespa
dc.publisherCOMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
dc.relation.uri10.1080/03610918.2016.1210168
dc.rightsPUBLICADA
dc.titleSTUDY OF PARTIAL LEAST SQUARES AND RIDGE REGRESSION METHODS
dc.title.alternativeESTUDIO DE MÍNIMOS CUADRADOS PARCIALES Y MÉTODOS DE REGRESIÓN DE CRESTAS
dc.typeARTÍCULO
dspace.entity.typePublication
ubb.EstadoPUBLICADA
ubb.Otra ReparticionDEPARTAMENTO DE ESTADISTICA
ubb.SedeCONCEPCIÓN
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