@inProceedings{
 title = {A comparison of methods for joint modelling of mean and dispersion.},
 type = {inProceedings},
 year = {2005},
 pages = {1499-1506},
 publisher = {Proc. XI International Symposium on Applied Stochastic Models and Data Analysis (ASMDA 2005), Brest, France},
 id = {9c22bd1d-52a5-38f8-aee3-f560702e5e2c},
 created = {2019-11-19T13:00:50.139Z},
 file_attached = {false},
 profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},
 group_id = {17585b85-df99-3a34-98c2-c73e593397d7},
 last_modified = {2019-11-19T13:46:23.715Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {isg:592},
 source_type = {inproceedings},
 private_publication = {false},
 abstract = {We considered the suitability of the methods for joint modelling of mean and dispersion for prediction based on large data sets under the assumption of normally distributed errors. Methods that seemed capable of handling a problem with 25 explanatory variables and 100000 observations were compared in predicting the strength of steel in a real data set collected from the production line of a steel plate mill. A neural network model for mean and dispersion gave the best prediction. The results indicate that neural networks are suitable for joint modelling of mean and dispersion in large data sets.},
 bibtype = {inProceedings},
 author = {J, Juutilainen I & Röning}
}

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