Design of Prediction Model for Small Sample Data Based on Multivariate Statistical Analysis
At present,big prediction errors of small sample data may affect the data application effect directly.In order to improve the prediction accuracy,this paper presented a method of designing a small sample data prediction model based on multivariate statistical analysis.At first,we put the small sample data into SPSS software,and used the bootstrap method to analyze the empirical distribution of small sample data.Based on the empirical distribution characteristics,we used the Fisher face algorithm with learning ability to pre-classify the data of small sample,and then constructed a test sample category label to extract the feature of small sample data.Through the principal compo-nents of data features based on multivariate statistical analysis,we determined the regression function of the model.Fi-nally,we built a data prediction model in combination with a support vector machine.Thus,an accurate prediction was achieved.Experimental results show that the proposed method has lower prediction error and higher efficiency in pre-dicting small sample data,indicating that the prediction effect is better.