On a strongly consistent estimator of the squared L_2-norm of a function

Roman Różański

Displaying similar documents to “On a strongly consistent estimator of the squared L_2-norm of a function”

On a class of estimators in a multivariate RCA(1) model

Zuzana Prášková, Pavel Vaněček (2011)

Kybernetika

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This work deals with a multivariate random coefficient autoregressive model (RCA) of the first order. A class of modified least-squares estimators of the parameters of the model, originally proposed by Schick for univariate first-order RCA models, is studied under more general conditions. Asymptotic behavior of such estimators is explored, and a lower bound for the asymptotic variance matrix of the estimator of the mean of random coefficient is established. Finite sample properties are...

Contributions to the credibility theory.

Atanasiu, Virginia (2008)

APPS. Applied Sciences

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Note on unbiased estimability of the larger of two mean values

Lesław Gajek (1995)

Applicationes Mathematicae

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An unbiased estimator of the larger of two mean values is constructed provided that the number of observations is random.

Minimum distance estimator for a hyperbolic stochastic partial differentialequation

Vincent Monsan, Modeste N'zi (2000)

Applicationes Mathematicae

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We study a minimum distance estimator in $L_{2}$ -norm for a class ofnonlinear hyperbolic stochastic partial differential equations, driven by atwo-parameter white noise. The consistency and asymptotic normality of thisestimator are established under some regularity conditions on thecoefficients. Our results are applied to the two-parameterOrnstein-Uhlenbeck process.

Variance of estimator of a discrete parameter

Antonín Lukš, Stanislav Komenda (1980)

Kybernetika

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Estimators of the asymptotic variance of stationary point processes - a comparison

Michaela Prokešová (2011)

Kybernetika

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We investigate estimators of the asymptotic variance $σ^{2}$ of a $d$ –dimensional stationary point process $Ψ$ which can be observed in convex and compact sampling window $W_{n} = n W$ . Asymptotic variance of $Ψ$ is defined by the asymptotic relation $V a r (Ψ (W_{n})) \sim σ^{2} | W_{n} |$ (as $n \to \infty$ ) and its existence is guaranteed whenever the corresponding reduced covariance measure $γ_{red}^{(2)} (\cdot)$ has finite total variation. The three estimators discussed in the paper are the kernel estimator, the estimator based on the second order intesity of the point process...