Discover the importance of homoskedasticity in regression models, where error variance is constant, and explore examples that illustrate this key concept.
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We review recent results for high-dimensional sparse linear regression in the practical case of unknown variance. Different sparsity settings are covered, including coordinate-sparsity, group-sparsity ...
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