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Finite-sample risk bounds for maximum likelihood estimation with arbitrary penalties
The MDL two-part coding $ \textit{index of resolvability} $ provides a finite-sample upper bound on the statistical risk of penalized likelihood estimators over countable models.
Estimating the Coefficients of a Mixture of Two Linear Regressions by Expectation Maximization
We also show that the population EM operator for mixtures of two regressions is anti-contractive from the target parameter vector if the cosine angle between the input vector and the target parameter vector is too small, thereby establishing the necessity of our conic condition.
Statistical Guarantees for Estimating the Centers of a Two-component Gaussian Mixture by EM
In that method, the basin of attraction for valid initialization is required to be a ball around the truth.
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