Results 1 to 10 of about 1,354,279 (305)
Ensemble averaging and mean squared error [PDF]
In fields such as climate science, it is common to compile an ensemble of different simulators for the same underlying process. It is a striking observation that the ensemble mean often outperforms at least half of the ensemble members in mean squared ...
Rougier, Jonathan
core +6 more sources
Exact Mean Integrated Squared Error [PDF]
An exact and easily computable expression for the mean integrated squared error (MISE) for the kernel estimator of a general normal mixture density, is given for Gaussian kernels of arbitrary order. This provides a powerful new way of understanding density estimation which complements the usual tools of simulation and asymptotic analysis. The family of
J. S. Marron, M. P. Wand
semanticscholar +5 more sources
Optimal Mean Squared Error Imaging [PDF]
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77345/1/AIAA-2002-4952-350 ...
Suman Chakravorty+2 more
core +4 more sources
Mean Squared Error, Deconstructed [PDF]
AbstractAs science becomes increasingly cross‐disciplinary and scientific models become increasingly cross‐coupled, standardized practices of model evaluation are more important than ever. For normally distributed data, mean squared error (MSE) is ideal as an objective measure of model performance, but it gives little insight into what aspects of model
Timothy O. Hodson+2 more
openaire +3 more sources
Mean squared error of empirical predictor [PDF]
The term ``empirical predictor'' refers to a two-stage predictor of a linear combination of fixed and random effects. In the first stage, a predictor is obtained but it involves unknown parameters; thus, in the second stage, the unknown parameters are ...
Das, Kalyan+2 more
core +5 more sources
IX. On the Probable Error of Mean-Square Contingency [PDF]
n ...
J. BLAKEMAN, Karl Pearson
openalex +4 more sources
Bootstrap for estimating the mean squared error of the spatial EBLUP [PDF]
This work assumes that the small area quantities of interest follow a Fay-Herriot model with spatially correlated random area effects. Under this model, parametric and nonparametric bootstrap procedures are proposed for estimating the mean squared ...
Molina, Isabel+2 more
core +8 more sources
Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation [PDF]
Knowledge distillation (KD), transferring knowledge from a cumbersome teacher model to a lightweight student model, has been investigated to design efficient neural architectures.
Taehyeon Kim+4 more
semanticscholar +1 more source
Optimizing LSTM Models for EUR/USD Prediction in the context of reducing energy consumption: An Analysis of Mean Squared Error, Mean Absolute Error and R-Squared [PDF]
The purpose of this study was to develop and evaluate a Long Short-Term Memory (LSTM) model for Forex prediction. The data used was reprocessed and the LSTM model was developed and trained using a supervised learning approach with popular deep learning ...
Echrigui Rania, Hamiche Mhamed
doaj +1 more source
BREXIT Election:Forecasting a Conservative Party Victory through the Pound using ARIMA and Facebook\u27s Prophet [PDF]
On the 30th October, 2019, the markets watched as British Prime Minister, Boris Johnson, took a massive political gamble to call a general election to break the Withdrawal Agreement stalemate in the House of Commons to “Get BREXIT Done”.
Makridakis+4 more
core +2 more sources