Results 151 to 160 of about 6,198,544 (193)
Local asymptotic normality of multivariate ARMA processes with a linear trend
Annals of the Institute of Statistical Mathematics, 1995 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Marc Hallin
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Local asymptotic normality and asymptotical minimax efficiency of the MLE under random censorship
Science in China Series A: Mathematics, 2000 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bingyi Jing
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A uniform condition of local asymptotic normality
Journal of Soviet Mathematics, 1986Translation from Zap. Nauchn. Semin. Leningr. Otd. Mat. Inst. Steklova 74, 108-117 (Russian) (1977; Zbl 0416.60020).
I A Ibragimov, R Z Khas’Minskii
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Local Asymptotic Normality in Extreme Value Index Estimation
Annals of the Institute of Statistical Mathematics, 1997 The author studied the estimation of an exteme value index. A local asymptotic normality result is established for estimation of the extreme value index in local extreme value models, i.e. in the \(\delta\)-neighborhood of generalized Pareto distributions.
Frank Marohn
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Local asymptotic normality for stationary sequences of observations
Journal of Soviet Mathematics, 1983The property of local asymptotic normality is established for stationary sequences under certain assumptions.
N K Bakirov
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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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Locally Asymptotically Normal Families
1990The 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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2010
Let X 1, …, X n be a random sample of size n from an underlying parametric statistical model. Then the basic statistical problem may be stated as follows: On the basis of a random sample, whose probability law depends on a parameter θ, discriminate between two values θ and θ ∗ (θ≠θ∗).
George G. Roussas, Debasis Bhattacharya
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Let X 1, …, X n be a random sample of size n from an underlying parametric statistical model. Then the basic statistical problem may be stated as follows: On the basis of a random sample, whose probability law depends on a parameter θ, discriminate between two values θ and θ ∗ (θ≠θ∗).
George G. Roussas, Debasis Bhattacharya
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LOCAL ASYMPTOTIC NORMALITY OF TRUNCATION MODELS
Statistics & Risk Modeling, 1999Summary: We consider iid random elements \(X_1, \dots, X_n\) with values in some measurable space \((S,{\mathcal B})\). Suppose that we are only interested in those observations among \(X_1, \dots, X_n\) which fall into some subset \(D\in {\mathcal B}\) having but a small probability of occurence.
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Local Asymptotic Normality for Stochastic Processes
2000Lucien LeCam established the most important and sophisticated foundation of the general statistical asymptotic theory. He introduced the concept of local asymptotic normality (LAN) for the likelihood ratio of general statistical models. Once LAN is proved, the asymptotic optimality of estimators and tests is described in terms of the LAN property.
Masanobu Taniguchi, Yoshihide Kakizawa
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