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� Estimation error
, 2007
"... statistical model: X = h(t; q) + ɛ � h: mathematical model such as ODE model, PDE model, algebraic model, etc. � ɛ: random variable with some probability distribution such as normal distribution. � X is a random variable. Under the assumption of ɛ being i.i.d N(0, σ2), we have probability distributi ..."
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statistical model: X = h(t; q) + ɛ � h: mathematical model such as ODE model, PDE model, algebraic model, etc. � ɛ: random variable with some probability distribution such as normal distribution. � X is a random variable. Under the assumption of ɛ being i.i.d N(0, σ2), we have probability distribution model: g(xθ) = 1 √ exp
Estimation Error of Expected Shortfall
"... The problem of estimation error of Expected Shortfall is analyzed, with a view of its introduction as a global regulatory risk measure. I. ..."
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The problem of estimation error of Expected Shortfall is analyzed, with a view of its introduction as a global regulatory risk measure. I.
DISTANCE ESTIMATION ERROR IN
"... The purpose of this study was to determine the accuracy of the typical adult’s ability to view, then estimate, distances along a roadway. A survey was conducted along a roadway, where 123 subjects were asked to look at, and then estimate the distance to common objects along the roadway. The entire p ..."
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population tended to underestimate distances to objects that were from 20 to 400 feet away. After none outliers were identified and removed, the average estimation error was –23.4%. The variation in performance among individuals was extremely large, with error extremes ranging from –96 % to +71
THE EFFECTS OF ESTIMATION ERROR
, 2001
"... This paper uses Monte Carlo simulations to assess the impact of noisy input parameters on the accuracy of estimated portfolio credit risk. Assumptions about input quality are derived from the distribution of historical sample statistics commonly used in default risk modelling. The resulting estimati ..."
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estimation error in the distribution of portfolio losses is considerable. Losses that are judged to occur with a probability of 0.3 % may actually occur with a probability of 1%. The paper also shows that estimation error leads to biases in VaR estimates and significance levels of backtests. The biases can
Minimum Error Rate Training in Statistical Machine Translation
, 2003
"... Often, the training procedure for statistical machine translation models is based on maximum likelihood or related criteria. A general problem of this approach is that there is only a loose relation to the final translation quality on unseen text. In this paper, we analyze various training cri ..."
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Cited by 663 (7 self)
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Often, the training procedure for statistical machine translation models is based on maximum likelihood or related criteria. A general problem of this approach is that there is only a loose relation to the final translation quality on unseen text. In this paper, we analyze various training criteria which directly optimize translation quality.
Estimation and Inference in Econometrics
, 1993
"... The astonishing increase in computer performance over the past two decades has made it possible for economists to base many statistical inferences on simulated, or bootstrap, distributions rather than on distributions obtained from asymptotic theory. In this paper, I review some of the basic ideas o ..."
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Cited by 1151 (3 self)
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The astonishing increase in computer performance over the past two decades has made it possible for economists to base many statistical inferences on simulated, or bootstrap, distributions rather than on distributions obtained from asymptotic theory. In this paper, I review some of the basic ideas of bootstrap inference. The paper discusses Monte Carlo tests, several types of bootstrap test, and bootstrap confidence intervals. Although bootstrapping often works well, it does not do so in every case.
Results 1  10
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1,856,093