Results 221 to 230 of about 373,332 (254)
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Matching behaviour is asymptotically normal
Combinatorica, 1981Ak-matching in a graphG is a set ofk edges, no two of which have a vertex in common. The number of these inG is writtenp(G, k). Using an idea due to L. H. Harper, we establish a condition under which these numbers are approximately normally distributed.
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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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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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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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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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Convergence rates and asymptotic normality for series estimators
Journal of Econometrics, 1997Whitney K. Newey
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Asymptotic Normality of the QMLE Estimator of ARCH in the Nonstationary Case
Econometrica, 2004Anders Rahbek
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