Sure Independence Screening for Ultra
... is an n-vector of i.i.d. random errors. When dimension p is high, it is often assumed that only a small number of predictors among X1 , · · · , Xp contribute to the response, which amounts to assuming ideally that the parameter vector β is sparse. With sparsity, variable selection can improve estima ...
... is an n-vector of i.i.d. random errors. When dimension p is high, it is often assumed that only a small number of predictors among X1 , · · · , Xp contribute to the response, which amounts to assuming ideally that the parameter vector β is sparse. With sparsity, variable selection can improve estima ...
quadratic model
... Use each point to write a system of equations to find a, b, and c in f(x) = ax2 + bx + c. (x, y) ...
... Use each point to write a system of equations to find a, b, and c in f(x) = ax2 + bx + c. (x, y) ...
Clusterpath: An Algorithm for Clustering using Convex
... For a given Z, the minimum of H in α is obtained by α = Z, leading to a norm minimization over the polytope Z. This problem can be solved efficiently by using the FrankWolfe algorithm (Frank & Wolfe, 1956). This algorithm to minimize a quadratic function over a polytope may be used as soon as it is ...
... For a given Z, the minimum of H in α is obtained by α = Z, leading to a norm minimization over the polytope Z. This problem can be solved efficiently by using the FrankWolfe algorithm (Frank & Wolfe, 1956). This algorithm to minimize a quadratic function over a polytope may be used as soon as it is ...
