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A review of methods for the assessment of prediction errors in conservation presence/absence models.
 Environmental Conservation
, 1997
"... Summary Predicting the distribution of endangered species from habitat data is frequently perceived to be a useful technique. Models that predict the presence or absence of a species are normally judged by the number of prediction errors. These may be of two types: false positives and false negativ ..."
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Cited by 463 (1 self)
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Summary Predicting the distribution of endangered species from habitat data is frequently perceived to be a useful technique. Models that predict the presence or absence of a species are normally judged by the number of prediction errors. These may be of two types: false positives and false
Surprise Beyond Prediction Error
"... r r Abstract: Surprise drives learning. Various neural “prediction error ” signals are believed to underpin surprisebased reinforcement learning. Here, we report a surprise signal that reflects reinforcement learning but is neither un/signed reward prediction error (RPE) nor un/signed state predic ..."
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r r Abstract: Surprise drives learning. Various neural “prediction error ” signals are believed to underpin surprisebased reinforcement learning. Here, we report a surprise signal that reflects reinforcement learning but is neither un/signed reward prediction error (RPE) nor un/signed state
The prediction error of BornhuetterFerguson
 Astin Bulletin
, 2008
"... of the most popular claims reserving methods. Whereas a formula for the prediction error of the CL method has been published already in 1993, there is still nothing equivalent available for the BF method. On the basis of the BF reserve formula, this paper develops a stochastic model for the BF metho ..."
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Cited by 3 (0 self)
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of the most popular claims reserving methods. Whereas a formula for the prediction error of the CL method has been published already in 1993, there is still nothing equivalent available for the BF method. On the basis of the BF reserve formula, this paper develops a stochastic model for the BF
Comparing Predictive Accuracy
 JOURNAL OF BUSINESS AND ECONOMIC STATISTICS, 13, 253265
, 1995
"... We propose and evaluate explicit tests of the null hypothesis of no difference in the accuracy of two competing forecasts. In contrast to previously developed tests, a wide variety of accuracy measures can be used (in particular, the loss function need not be quadratic, and need not even be symmetri ..."
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Cited by 1346 (23 self)
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be symmetric), and forecast errors can be nonGaussian, nonzero mean, serially correlated, and contemporaneously correlated. Asymptotic and exact finite sample tests are proposed, evaluated, and illustrated.
Prediction Error Computation on a Grid
"... This paper presents a distributed algorithm for prediction error computation. ..."
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This paper presents a distributed algorithm for prediction error computation.
Generalizations of the Normalized Prediction Error
, 1999
"... The Normalized Prediction Error, or NPE, can be used for the evaluation of the fit of AR models, which are estimated from signals that are generated by an AR process. The NPE does not only provide a measure of the time domain fit, but also of the frequency domain fit of an estimated model. Therefore ..."
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Cited by 1 (1 self)
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The Normalized Prediction Error, or NPE, can be used for the evaluation of the fit of AR models, which are estimated from signals that are generated by an AR process. The NPE does not only provide a measure of the time domain fit, but also of the frequency domain fit of an estimated model
OF PREDICTIVE ERROR FRAMES
"... In this paper, we develop a framework for efficiently encoding predictive error frames (PEF) as part of a rate scalable, waveletbased video compression algorithm. We investigate the use of ratedistortion analysis to determine the significance of coefficients in the wavelet decomposition. Based on ..."
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In this paper, we develop a framework for efficiently encoding predictive error frames (PEF) as part of a rate scalable, waveletbased video compression algorithm. We investigate the use of ratedistortion analysis to determine the significance of coefficients in the wavelet decomposition. Based
New results in linear filtering and prediction theory
 TRANS. ASME, SER. D, J. BASIC ENG
, 1961
"... A nonlinear differential equation of the Riccati type is derived for the covariance matrix of the optimal filtering error. The solution of this "variance equation " completely specifies the optimal filter for either finite or infinite smoothing intervals and stationary or nonstationary sta ..."
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Cited by 607 (0 self)
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A nonlinear differential equation of the Riccati type is derived for the covariance matrix of the optimal filtering error. The solution of this "variance equation " completely specifies the optimal filter for either finite or infinite smoothing intervals and stationary or nonstationary
Improved prediction of signal peptides  SignalP 3.0
 J. MOL. BIOL.
, 2004
"... We describe improvements of the currently most popular method for prediction of classically secreted proteins, SignalP. SignalP consists of two different predictors based on neural network and hidden Markov model algorithms, where both components have been updated. Motivated by the idea that the cle ..."
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Cited by 654 (7 self)
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We describe improvements of the currently most popular method for prediction of classically secreted proteins, SignalP. SignalP consists of two different predictors based on neural network and hidden Markov model algorithms, where both components have been updated. Motivated by the idea
Decomposition of prediction error in multilevel models
, 2002
"... We present a decomposition of prediction error for the multilevel model in the context of predicting a future observable y∗j in the jth group of a hierarchical dataset. The multilevel prediction rule is used for prediction and the components of prediction error are estimated via a simulation study t ..."
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Cited by 3 (1 self)
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We present a decomposition of prediction error for the multilevel model in the context of predicting a future observable y∗j in the jth group of a hierarchical dataset. The multilevel prediction rule is used for prediction and the components of prediction error are estimated via a simulation study
Results 1  10
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20,454