Performance comparison of model selection criteria by generated experimental data
In Bioinformatics and other areas the model selection is a process of choosing a model from set of candidate models of different classes which will provide the best balance between goodness of fitting of the data and complexity of the model. There are many criteria for evaluation of mathematical mod...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2018-01-01
|
| Series: | ITM Web of Conferences |
| Online Access: | https://doi.org/10.1051/itmconf/20181602006 |
| _version_ | 1818677137901617152 |
|---|---|
| author | Mavrevski Radoslav Milanov Peter Traykov Metodi Pencheva Nevena |
| author_facet | Mavrevski Radoslav Milanov Peter Traykov Metodi Pencheva Nevena |
| author_sort | Mavrevski Radoslav |
| collection | DOAJ |
| description | In Bioinformatics and other areas the model selection is a process of choosing a model from set of candidate models of different classes which will provide the best balance between goodness of fitting of the data and complexity of the model. There are many criteria for evaluation of mathematical models for data fitting. The main objectives of this study are: (1) to fitting artificial experimental data with different models with increasing complexity; (2) to test whether two known criteria as Akaike’s information criterion (AIC) and Bayesian information criterion (BIC) can correctly identify the model, used to generate the artificial data and (3) to assess and compare empirically the performance of AIC and BIC. |
| first_indexed | 2024-12-17T08:54:36Z |
| format | Article |
| id | doaj.art-3541831921504751902c472a97747fc5 |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-12-17T08:54:36Z |
| publishDate | 2018-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-3541831921504751902c472a97747fc52022-12-21T21:55:59ZengEDP SciencesITM Web of Conferences2271-20972018-01-01160200610.1051/itmconf/20181602006itmconf_amcse2018_02006Performance comparison of model selection criteria by generated experimental dataMavrevski RadoslavMilanov PeterTraykov MetodiPencheva NevenaIn Bioinformatics and other areas the model selection is a process of choosing a model from set of candidate models of different classes which will provide the best balance between goodness of fitting of the data and complexity of the model. There are many criteria for evaluation of mathematical models for data fitting. The main objectives of this study are: (1) to fitting artificial experimental data with different models with increasing complexity; (2) to test whether two known criteria as Akaike’s information criterion (AIC) and Bayesian information criterion (BIC) can correctly identify the model, used to generate the artificial data and (3) to assess and compare empirically the performance of AIC and BIC.https://doi.org/10.1051/itmconf/20181602006 |
| spellingShingle | Mavrevski Radoslav Milanov Peter Traykov Metodi Pencheva Nevena Performance comparison of model selection criteria by generated experimental data ITM Web of Conferences |
| title | Performance comparison of model selection criteria by generated experimental data |
| title_full | Performance comparison of model selection criteria by generated experimental data |
| title_fullStr | Performance comparison of model selection criteria by generated experimental data |
| title_full_unstemmed | Performance comparison of model selection criteria by generated experimental data |
| title_short | Performance comparison of model selection criteria by generated experimental data |
| title_sort | performance comparison of model selection criteria by generated experimental data |
| url | https://doi.org/10.1051/itmconf/20181602006 |
| work_keys_str_mv | AT mavrevskiradoslav performancecomparisonofmodelselectioncriteriabygeneratedexperimentaldata AT milanovpeter performancecomparisonofmodelselectioncriteriabygeneratedexperimentaldata AT traykovmetodi performancecomparisonofmodelselectioncriteriabygeneratedexperimentaldata AT penchevanevena performancecomparisonofmodelselectioncriteriabygeneratedexperimentaldata |