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Variable selection will often be put forward when building statistical models. It is not conducive to study the problems that the variables in the model are more or less than the actual variables. In the process of optimizing the models, most explanatory and influential subset of variables need to be found, in order to make the model more reasonable and high forecast precision. In the traditional method, the variable selection and parameter estimation are separated, such as AIC criterion proposed by Akaike, BIC criterion proposed by Schwarz G based on Bayes method. But Lasso method regards absolute coefficient function as a penalty term to compress the coefficients of the model, and coefficients which absolute value is relative smaller than others are compressed to 0, so as to achieve the purpose of variable selection and parameter estimation . Lasso is a continuous sequential process and overcomes the disadvantages of traditional methods in variable selection, at the same time the excellent properties of subset selection and ridge regression are preserved. As a result, this approach has been widely accepted. In this paper the Lasso method of variable selection for linear model is discussed. Given a set of observed data ) , ( i i y x , , , , 2 , 1 n i  = ) , , , ( 2 1 ip i i i x x x x  = is a vectors consist of variables, i y is dependent variable. Linear regression model can be expressed as:

Analysis of Influencing Factors to Economic in Sichuan Province Based on Lasso