| dbo:abstract |
In statistics, least-angle regression (LARS) is an algorithm for fitting linear regression models to high-dimensional data, developed by Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani. Suppose we expect a response variable to be determined by a linear combination of a subset of potential covariates. Then the LARS algorithm provides a means of producing an estimate of which variables to include, as well as their coefficients. Instead of giving a vector result, the LARS solution consists of a curve denoting the solution for each value of the L1 norm of the parameter vector. The algorithm is similar to forward stepwise regression, but instead of including variables at each step, the estimated parameters are increased in a direction equiangular to each one's correlations with the residual. (en) 统计学中，最小角回归（英语：least-angle regression (LARS)）是一种对高维数据进行线性回归的算法，由等人提出。 在线性回归中，因变量由一组自变量的线性组合表达，这些自变量可能是，也有可能与因变量无关。最小角算法不会像传统算法那样给出自变量的向量表达，而是对每个参数向量的L1范数的值给出一条曲线。 (zh) |
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统计学中，最小角回归（英语：least-angle regression (LARS)）是一种对高维数据进行线性回归的算法，由等人提出。 在线性回归中，因变量由一组自变量的线性组合表达，这些自变量可能是，也有可能与因变量无关。最小角算法不会像传统算法那样给出自变量的向量表达，而是对每个参数向量的L1范数的值给出一条曲线。 (zh) In statistics, least-angle regression (LARS) is an algorithm for fitting linear regression models to high-dimensional data, developed by Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani. Suppose we expect a response variable to be determined by a linear combination of a subset of potential covariates. Then the LARS algorithm provides a means of producing an estimate of which variables to include, as well as their coefficients. (en) |
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