Assume that we have a Ridge regression problem with only one predictor, and the true model...

  

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Assume that we have a Ridge regression problem with only one predictor, and the true model is linear without an intercept, i.e. Y = X + e. Assume that we have In samples, (xi, y), (x2, Y2), . . ., (xn, Yn) and we want to find the L2 regularized least squares estimate from the data. (a) Formulate the objective function in terms of a candidate B and xi's and yi's, which are known. Assume that the regularization parameter is \. (b) Find B in terms of A and the data,. Assume that we have a Ridge regression problem with only one predictor, and the true model is linear without an intercept, i.e. Y = X + e. Assume that we have In samples, (xi, y), (x2, Y2), . . ., (xn, Yn) and we want to find the L2 regularized least squares estimate from the data. (a) Formulate the objective function in terms of a candidate B and xi's and yi's, which are known. Assume that the regularization parameter is \. (b) Find B in terms of A and the data,.