The problem of estimation of analytic density function using L_p minimax risk is considered. A kernel-type estimator of an unknown density function is proposed and the upper bound on its limiting local minimax risk is established. Our result is consistent with a conjecture of Guerre and Tsybakov (1998) and augments previous work in this area.
Estimation of means in graphical Gaussian models with symmetries
Estimation of Network structures from partially observed Markov random fields
A note on the U,V method of estimation
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