Estimation problems for samples with measurement errors (Q1074972)

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scientific article; zbMATH DE number 3949490
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Estimation problems for samples with measurement errors
scientific article; zbMATH DE number 3949490

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    Estimation problems for samples with measurement errors (English)
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    1985
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    For \(x\in R\), let \(N_{\alpha}(x)=m\alpha\) iff \(x\in (\alpha m-\alpha /2\), \(\alpha m+\alpha /2]\). Also let \(X_ 1,...,X_ n\) be a random sample from the pdf \(f_ X(x)\), and set \[ \bar N_{\alpha}=(1/n)\sum^{n}_{i=1}N_{\alpha}(X_ i)\quad and\quad S^ 2_{\alpha}=[1/(n-1)]\sum^{n}_{i=1}[N_{\alpha}(X_ i)-\bar N_{\alpha}]^ 2. \] The author mainly studies the asymptotic properties of \(\bar N_{\alpha_ n}\) and \(S^ 2_{\alpha_ n}\), as \(n\to \infty.\) For example, if \(E(X^ 2)<\infty\), \(E(e^{itX})=o(| t|^{-k})\) (as \(| t| \to \infty)\) for some \(k\in {\mathbb{N}}\) and \(\alpha_ n=O(n^{-1/(2k+2)})\), or X is distributed as \(N(\theta,\sigma^ 2)\) and \(\alpha_ n\leq 2\pi \sigma (\log n)^{- 1/2}\), the author shows that \(n^{1/2}[\bar N_{\alpha_ n}-E(X)]\) is asymptotically normal. The author also considers problems of truncation and general maximum likelihood estimation from discrete scale measurements.
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    asymptotic normality
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    asymptotic unbiasedness
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    efficiency
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    measurement errors
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    truncation
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    maximum likelihood estimation
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    discrete scale measurements
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