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Mean square
"... varIatIon Deviation from linear regression (X 10) Deviation from quadratic ..."
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varIatIon Deviation from linear regression (X 10) Deviation from quadratic
Testing the Equality of Prediction Mean Square Errors
 INTERNATIONAL JOURNAL OF FORECASTING
, 1997
"... Given two sources of forecasts of the same quantity, it is possible to compare prediction records. In particular, it can be useful to test the hypothesis of equal accuracy in forecast performance. We analyse the behaviour of two possible tests, and of modifications of these tests designed to circumv ..."
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Cited by 286 (1 self)
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Given two sources of forecasts of the same quantity, it is possible to compare prediction records. In particular, it can be useful to test the hypothesis of equal accuracy in forecast performance. We analyse the behaviour of two possible tests, and of modifications of these tests designed to circumvent shortcomings in the original formulations. As a result of this analysis, a recommendation forone particular testing approach is made for practical applications.
Distributed average consensus with leastmeansquare deviation
 Journal of Parallel and Distributed Computing
, 2005
"... We consider a stochastic model for distributed average consensus, which arises in applications such as load balancing for parallel processors, distributed coordination of mobile autonomous agents, and network synchronization. In this model, each node updates its local variable with a weighted averag ..."
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Cited by 205 (4 self)
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average of its neighbors ’ values, and each new value is corrupted by an additive noise with zero mean. The quality of consensus can be measured by the total meansquare deviation of the individual variables from their average, which converges to a steadystate value. We consider the problem of finding
The Mean Square Discrepancy of Randomized Nets
, 1996
"... this article a formula for the mean square L ..."
Mutual information and minimum meansquare error in Gaussian channels
 IEEE TRANS. INFORM. THEORY
, 2005
"... This paper deals with arbitrarily distributed finitepower input signals observed through an additive Gaussian noise channel. It shows a new formula that connects the inputoutput mutual information and the minimum meansquare error (MMSE) achievable by optimal estimation of the input given the out ..."
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Cited by 288 (34 self)
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This paper deals with arbitrarily distributed finitepower input signals observed through an additive Gaussian noise channel. It shows a new formula that connects the inputoutput mutual information and the minimum meansquare error (MMSE) achievable by optimal estimation of the input given
MEAN SQUARE CHARACTERISATION
, 2008
"... Abstract. A geometric Brownian motion with delay is the solution of a stochastic differential equation where the drift and diffusion coefficient depend linearly on the past of the solution, i.e. a linear stochastic functional differential equation. In this work the asymptotic behavior in mean square ..."
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Abstract. A geometric Brownian motion with delay is the solution of a stochastic differential equation where the drift and diffusion coefficient depend linearly on the past of the solution, i.e. a linear stochastic functional differential equation. In this work the asymptotic behavior in mean
Convolutions and mean square estimates . . .
, 2005
"... We study the convolution function x C[f(x)]:= 1 f(y)f ( x dy y y when f(x) is a suitable numbertheoretic error term. Asymptotics and upper bounds for C[f(x)] are derived from mean square bounds for f(x). Some applications are given, in particular to ζ ( 1 2 + ix)2k and the classical Rankin–Selber ..."
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We study the convolution function x C[f(x)]:= 1 f(y)f ( x dy y y when f(x) is a suitable numbertheoretic error term. Asymptotics and upper bounds for C[f(x)] are derived from mean square bounds for f(x). Some applications are given, in particular to ζ ( 1 2 + ix)2k and the classical Rankin
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
MEANSQUARE 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 ..."
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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 realvalued function of the parameter θ. One criterion for choosing an estimator T = T(X) of g(θ) is to minimize the meansquared error (MSE) Eθ((T(X) − g
Root Mean Square:
"... In statistics, signal processing and mathematical finance, a time series is a sequence of data points, measured typically at successive times spaced at uniform time intervals. Here, I only focus on the discrete time series xi = x(ti), i = 1, · · · , N. The motivation of writing this methodology n ..."
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for timeseries x in below. The mean describes the central location of the data, and the standard deviation describes the spread. The standard deviation remains the most common measure of statistical dispersion, measuring how widely spread the values in a data set are. In mathematics, the root mean square
Results 1  10
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