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According âln Amari applied arbitrary assertion Assume assumption Asymptotic bias canonical gradient choose chosen compact compute condition consider consistent constant continuous converges countable defined denote depending derivative differentiable distribution Edited efficient empir estimating equations estimating function examples exists fact function given gradient Hence implies improvement procedure influence function integrable leads Lemma limiting linear m.l. estimator mean deviation measure minimizing models Moreover neighborhood normal Observe obtain occurring op(nș optimal orthogonal p-measures parameter particular path permutation Pfanzagl preliminary problem Proof Proposition prove regularity conditions relation Remark replaced respect sample sample mean sequence simulation space Statistical stochastically sufficient supporting points theory usually variance bound