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*MEAN-SQUARE* *ERRORS* OF ESTIMATORS:

"... Suppose we have a parametric family of probability distributions with a likelihood function f(x, θ) for one observation, where f(x, θ) is a probability mass function for a discrete distribution or a probability density function for a continuous distribution. Let Eθ denote expectation, and Pθ probabi ..."

Abstract
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probability, when θ is the true value of the parameter. Let X = (X1,..., Xn) be a vector of i.i.d. observations with distribution Pθ. Suppose g = g(θ) is a real-valued function of the parameter θ. One criterion for choosing an estimator T = T(X) of g(θ) is to minimize the

*mean-squared**error*(MSE) Eθ((T(X) − g###
Attack-Resilient Minimum *Mean-Squared* *Error* Estimation

, 2014

"... Attack-resilient minimum mean-squared error estimation ..."

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Minimum *Mean-Square* *Error*

"... All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."

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All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.

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*Mean* *Square* *Error* Estimation

"... This paper studies the effect of parametric mismatch in minimum mean square error (MMSE) estimation. In particular, we consider the problem of estimating the input signal from the output of an additive white Gaussian channel whose gain is fixed, but unknown. The input distribution is known, and the ..."

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This paper studies the effect of parametric mismatch in minimum

*mean**square**error*(MMSE) estimation. In particular, we consider the problem of estimating the input signal from the output of an additive white Gaussian channel whose gain is fixed, but unknown. The input distribution is known###
Robust *Mean-Squared* *Error* Estimation of . . .

, 2004

"... This paper is a continuation of the work in [11] and [2] on the problem of estimating by a linear estimator, N unobservable input vectors, undergoing the same linear transformation, from noise-corrupted observable output vectors. Whereas in the aforementioned papers, only the matrix representing the ..."

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the linear transformation was assumed uncertain, here we are concerned with the case in which the second order statistics of the noise vectors (i.e., their covariance matrices) are also subjected to uncertainty. We seek a robust

*mean-squared**error*estimator immuned against both sources of uncertainty. We###
*Mean* *Square* *Error* Approximation for

, 2008

"... HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte p ..."

Abstract
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HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et a ̀ la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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*Mean* *Square* *Error* Estimation in Thresholding

"... Abstract—We present a novel approach to estimating the mean square error (MSE) associated with any given threshold level in both hard and soft thresholding. The estimate is provided by using only the data that is being thresholded. This adaptive approach provides probabilistic confidence bounds on t ..."

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Abstract—We present a novel approach to estimating the

*mean**square**error*(MSE) associated with any given threshold level in both hard and soft thresholding. The estimate is provided by using only the data that is being thresholded. This adaptive approach provides probabilistic confidence bounds###
*Mean-Square* *Error* Bounds for Reduced-Order Linear

, 1987

"... The mean-square error of reduced-order linear state estimators for continuous-time linear systems is investigated. Lower and upper bounds on the minimal mean-square error are presented. The bounds are readily computahle at each time-point and at steady state from the solutions to the Ricatti and the ..."

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The

*mean-square**error*of reduced-order linear state estimators for continuous-time linear systems is investigated. Lower and upper bounds on the minimal*mean-square**error*are presented. The bounds are readily computahle at each time-point and at steady state from the solutions to the Ricatti###
Conditional *Mean* *Square* *Error* of Prediction

"... We revisit the stochastic model of Alai et al. (2009) for the Bornhuetter-Ferguson claims reserving method, Bornhuetter and Ferguson (1972). We derive an estimator of its conditional mean square error of prediction (MSEP) using an approach that is based on generalized linear models and maximum likel ..."

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We revisit the stochastic model of Alai et al. (2009) for the Bornhuetter-Ferguson claims reserving method, Bornhuetter and Ferguson (1972). We derive an estimator of its conditional

*mean**square**error*of prediction (MSEP) using an approach that is based on generalized linear models and maximum