Results 311 to 320 of about 634,254 (363)
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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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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.
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Asymptotic normality of Powell’s kernel estimator
Annals of the Institute of Statistical Mathematics, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kengo Kato
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Asymptotic Normality of Autoregressive Processes
Acta Applicandae Mathematicae, 2009Using an approximation method along with a central limit theorem for \(m\)-dependent random variables, this paper prove an asymptotic normality for autoregressive processes, and provide the central limit theorems of the least square estimate and the Yule-Walker estimate of the parameters of an autoregressive process.
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Asymptotically normal dynamical semigroups
Journal of Statistical Physics, 1987zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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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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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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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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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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