Description Usage Arguments Value References Examples
Prediction intervals based on the MVA model
1 |
train |
Training set as a matrix of size N times K+1. Each row describes an observation. Columns 1 to K are the explanatory variables, and column K+1 is the response variables. |
test |
Test set as a matrix of size N2 times K. Each row corresponds to an observation (but without the response variable). Columns 1 to K are the explanatory variables. |
epsilons |
Vector of several significance levels.
Each significance level |
ridge |
Ridge coefficient, a nonnegative number. The default value is 0; setting it to a small positive constant might lead to more stable results. |
The output is a list of three elements.
output[[1]] |
The matrix of lower bounds of prediction intervals.
Its size is N2 times Neps,
where N2 is the number of test observations
and Neps is the number of significance levels.
The element |
output[[2]] |
The matrix of upper bounds b,
with the same structure as |
output[[3]] |
The termination code: 0 = normal termination; 1 = illegal parameters (the training and test sets have different numbers of explanatory variables); 2 = too few observations. |
Vovk, V., Nouretdinov, I., and Gammerman, A. (2009) On-line predictive linear regression. Annals of Statistics 37, 1566 - 1590. The new arXiv version http://arxiv.org/abs/math/0511522 of this paper contains the description of this program and the algorithm that this program implements.
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