Results 251 to 260 of about 78,936 (284)
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2000
The classical theory of asymptotics in statistics relies heavily on certain local quadratic approximations to the logarithms of likelihood ratios. Such approximations will be studied here but in a restricted framework.
Lucien Le Cam, Grace Lo Yang
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The classical theory of asymptotics in statistics relies heavily on certain local quadratic approximations to the logarithms of likelihood ratios. Such approximations will be studied here but in a restricted framework.
Lucien Le Cam, Grace Lo Yang
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Normal Approximations and Asymptotic Expansions.
Journal of the American Statistical Association, 1977Marius Iosifescu +2 more
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A Note on Asymptotic Normal Structure and Close-to-Normal Structure
Canadian Mathematical Bulletin, 1982AbstractA closed convex subset X of a Banach space E is said to have (i) asymptotic normal structure if for each bounded closed convex subset C of X containing more than one point and for each sequence in C satisfying ‖xn − xn + 1‖ → 0 as n → ∞, there is a point x ∈ C such that ; (ii) close-to-normal structure if for each bounded closed convex subset ...
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Asymptotic normality of the posterior in relative entropy
IEEE Transactions on Information Theory, 1999Summary: We show that the relative entropy between a posterior density formed from a smooth likelihood and prior and a limiting normal form tends to zero in the independent and identically distributed case. The mode of convergence is in probability and in mean. Applications to codelength in stochastic complexity and to sample size selection are briefly
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1986
This chapter elaborates properties of a certain widely applicable method of construction of estimates. The general idea is that one provides oneself with a well behaved auxiliary estimate of the parameter and that, in the vicinity of the estimated value, one refines the estimate using techniques adapted to the local structure of the experiment.
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This chapter elaborates properties of a certain widely applicable method of construction of estimates. The general idea is that one provides oneself with a well behaved auxiliary estimate of the parameter and that, in the vicinity of the estimated value, one refines the estimate using techniques adapted to the local structure of the experiment.
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Bootstrap and asymptotic normality
1992In this chapter consistency of bootstrap is compared with asymptotic normality. This is done for linear statistics of n i. i. d. observations. It is shown that bootstrap works asymptotically under the same assumptions as a normal approximation with estimated variance (Theorem 1).
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Asymptotic Normality of the QMLE Estimator of ARCH in the Nonstationary Case
Econometrica, 2004Anders Rahbek
exaly
On the asymptotic normality of the posterior distribution.
2001Summary: Let a random sample of size \(n\) be drawn from a variable with probability density depending on a vector of parameters, \(\pmb\theta= (\theta_1,\theta_2,\dots,\theta_r)^\top\), and let us suppose that the prior distribution of \(\pmb\theta\) has continuous, positive density \(p(\pmb\theta)\) on an open set \(H\).
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