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 multivariate deviated model


Minimax Optimal Rate for Parameter Estimation in Multivariate Deviated Models

Neural Information Processing Systems

The main challenges in deriving the convergence rate of the MLE mainly come from two issues: (1) The interaction between the function $h_{0}$ and the density function $f$; (2) The deviated proportion $\lambda^{\ast}$ can go to the extreme points of $[0,1]$ as the sample size tends to infinity. To address these challenges, we develop the \emph{distinguishability condition} to capture the linear independent relation between the function $h_{0}$ and the density function $f$. We then provide comprehensive convergence rates of the MLE via the vanishing rate of $\lambda^{\ast}$ to zero as well as the distinguishability of two functions $h_{0}$ and $f$.


Minimax Optimal Rate for Parameter Estimation in Multivariate Deviated Models

Neural Information Processing Systems

The main challenges in deriving the convergence rate of the MLE mainly come from two issues: (1) The interaction between the function h_{0} and the density function f; (2) The deviated proportion \lambda {\ast} can go to the extreme points of [0,1] as the sample size tends to infinity. To address these challenges, we develop the \emph{distinguishability condition} to capture the linear independent relation between the function h_{0} and the density function f . We then provide comprehensive convergence rates of the MLE via the vanishing rate of \lambda {\ast} to zero as well as the distinguishability of two functions h_{0} and f .