Statistical estimation : asymptotic theory

By I. Ibragimov, R. Z. Khasʹminskiĭ. 1981

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Citations in the corpus

Analysis of a Random Forests Model (2010, background): “To understand this point, just recall that the rate n − 2 / ( d +2) is minimax optimal for the class F d (see for example Ibragimov and Khasminskii [28, 29, 30]), seen as a collection of regression functions over [0 , 1] d , not [0 , 1] S .”
On Choosing and Bounding Probability Metrics (2002, methodology): “It is used throughout Ibragmiov and Has’minskii (1981) to quantify the rate of convergence of sequences of consistent estimators to their parameter.”
Double/Debiased Machine Learning for Treatment and Causal Parameters (2016, background): “ally normal estimates for low-dimensional components with nuisance parameters estimated by conventional nonparametric estimators such as kernels or series. See, for example, the work by Levit (1975), Ibragimov and Hasminskii (1981), Bickel (1982), Robinson (1988), Newey (1990), van der Vaart (1991),”
Orthogonal series density estimation (2010, background): “Then the Parseval identity (6), together with Eq.”
Consistency of the structured total least squares estimator in a multivariate errors-in-variables model (2005, background): “We recall the following result on stochastic fields (Ibragimov and Hasminskii, 1981).”
Convergence of Gaussian quasi-likelihood random fields for ergodic Lévy driven SDE observed at high frequency (2013, background): “elou [5], Chan and Ing [6], Findley and Wei [8], Inagaki and Ogata [14], Jeganathan [16, 17], Ogata and Inagaki [35], Sieders and Dzhaparidze [39] and Uchida [42], as well as Ibragimov and Has’minski [13], Kutoyants [22, 23] and Yoshida [48]. See also the recent paper Uchida and Yoshida [43] for an ”
Information-theoretic lower bounds for distributed statistical estimation with communication constraints (2013, background): “[12, 22, 20, 19]), little of it characterizes the effects of limiting communication.”
Parameter Estimation for a Discretely Observed Integrated Diffusion Process (2006, methodology): “We will instead use theorem 20 in appendix 1 of Ibragimov & Khas’minskii (1981).”