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Title:Parametric estimation: Finite sample theory PDF Logo
Authors:Spokoiny, Vladimir
Issue Date:2011
Series/Report no.:SFB 649 discussion paper 2011-081
Abstract:The paper aims at reconsidering the famous Le Cam LAN theory. The main features of the approach which make it different from the classical one are: (1) the study is non-asymptotic, that is, the sample size is fixed and does not tend to infinity; (2) the parametric assumption is possibly misspecified and the underlying data distribution can lie beyond the given parametric family. The main results include a large deviation bounds for the (quasi) maximum likelihood and the local quadratic majorization of the log-likelihood process. The latter yields a number of important corollaries for statistical inference: concentration, confidence and risk bounds, expansion of the maximum likelihood estimate, etc. All these corollaries are stated in a non-classical way admitting a model misspecification and finite samples. However, the classical asymptotic results including the efficiency bounds can be easily derived as corollaries of the obtained non-asymptotic statements. The general results are illustrated for the i.i.d. set-up as well as for generalized linear and median estimation. The results apply for any dimension of the parameter space and provide a quantitative lower bound on the sample size yielding the root-n accuracy.
Subjects:maximum likelihood
local quadratic approximation
concentration
coverage
deficiency
JEL:C13
C14
Document Type:Working Paper
Appears in Collections:SFB 649 Discussion Papers, HU Berlin

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