@article{
 title = {BUSDM - An algorithm for the bottom-up search of departures from a model},
 type = {article},
 year = {2011},
 pages = {561-578},
 volume = {81},
 id = {a294ca36-84d1-3d2f-817f-75dd2f87edfd},
 created = {2019-11-19T13:01:22.588Z},
 file_attached = {false},
 profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},
 group_id = {17585b85-df99-3a34-98c2-c73e593397d7},
 last_modified = {2019-11-19T13:48:00.646Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {isg:1400},
 source_type = {article},
 private_publication = {false},
 abstract = {Searching for regions of input space where a statistical model is
inappropriate is useful in many applications. The study proposes an
algorithm for finding local departures from a regression-type
prediction model. The algorithm returns low-dimensional hypercubes
where the average prediction error clearly departs from zero. The
study describes the developed algorithm, and shows successful
applications on simulated data and real data from steel plate production. The algorithms that have been originally developed for searching regions of high response value from input space are reviewed and considered as alternative methods for locating model departures. The proposed algorithm succeeds in locating the model departure regions better than the compared alternatives. The algorithm can be utilized in sequential follow up of a model as time goes along and new data is observed.},
 bibtype = {article},
 author = {Juutilainen I Koskimäki H, Laurinen P Röning J},
 journal = {Journal of Statistical Computation and Simulation},
 number = {5}
}

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