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Most meter data management systems, on the market, integrate multiple collection systems, calculate billing determinates and do validation, estimation and editing of meter data.
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This algorithm can be embedded in both maximum likelihood and cross-validation estimation methods.
This work establishes the validity of this approach to provide watershed scale soil moisture estimates in this study region for the purposes of satellite validation with estimation errors as small as 3%.
Instead of a constant, we treat the RBF shape parameters as stochastic variables whose distribution were determined by a stochastic leave-one-out cross validation (LOOCV) estimation.
Bootstrap resampling for internal validation and estimation of the expected optimism was performed based on Harrell [ 6].
A one way analysis of variance test indicated no significant differences between the mean observed EQ-5D values of the validation and estimation samples across the 4 samples (F397,3 = 0.05, p = 0.985).
After running classification algorithms, we tested the cross-validation error estimation using 10-fold cross-validation with 10 iterations.
Magnitude of ΔK as a function of K and cross-validation error estimation to find the optimal K value for the population structure in STRUCTURE and ADMIXTURE.
For validation, these estimations were then applied to the control of movement kinematics on an experimental robot composed of a 6 dof robot arm carrying a 16 dof mechanical 4-finger hand.
Therefore in the present paper we used a 10-fold cross-validation as estimation method.
To achieve this we describe a new method that combines several statistical and computational strategies: robust normalization and expression signal calculation; correlation coefficients obtained by parametric and non-parametric methods; random cross-validations; and estimation of the statistical accuracy and coverage of the data.
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