Results 201 to 210 of about 362,691 (237)
Some of the next articles are maybe not open access.
Asymptotic Efficiency of Inverse Estimators
Theory of Probability & Its Applications, 2000Summary: Inverse estimation concerns the recovery of an unknown input signal from blurred observations on a known transformation of that signal. The estimators considered in this paper are based on a regularized inverse of the transformation involved, employing a Hilbert space set-up. We focus on properties related to weak convergence. It is shown that
van Rooij, A. C. M. +2 more
openaire +1 more source
Asymptotics of Oja Median Estimate
Statistics & Probability Letters, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +1 more source
Asymptotic Normality of some Estimators
Calcutta Statistical Association Bulletin, 1981This paper uses martingale central limit theorem and continuous mapping theorem to establish asymptotic normality of log-likelihood ratio process, maximum likelihood estimators and the posterior distributions. Illustrative examples are given.
openaire +2 more sources
2019
In order to make use of the central limit theorems 3.2 and 3.6 the law of the limiting variables.
openaire +1 more source
In order to make use of the central limit theorems 3.2 and 3.6 the law of the limiting variables.
openaire +1 more source
Theory of Probability & Its Applications, 1993
See the review in Zbl 0774.62030.
openaire +1 more source
See the review in Zbl 0774.62030.
openaire +1 more source
On Asymptotic Deficiency of Estimators
Australian Journal of Statistics, 1981SummaryThe notion of deficiency was introduced by Hodges and Lehmann. It is known that best asymptotically normal (BAN) estimators are second order asymptotically efficient in the class A2 of all second order asymptotically median unbiased estimators.In this paper it is shown that the asymptotic deficiency of any two estimators in the restricted class ...
openaire +2 more sources
Asymptotic Estimates for Integrals
2015Mechanical problems can be described by differential equations, the solutions of which often cannot be expressed by elementary functions, but have an integral representation.
S. M. Bauer +4 more
openaire +1 more source
Deterministic Estimation and Asymptotic Stochastic Estimation
1981Let t → x(t) be a state process and consider observations t → y(t) of a signal t → h(x(t)) in the presence of additive white noise ẏ = h(x) + v. The stochastic filter is the map that associates to each observation record y(τ), 0 ≤ τ ≤ t, the conditional mean E( ∅ (x(t)) ∣ y(τ), 0 ≤ τ ≤ t).
openaire +1 more source