Effect of influential observation in genomic prediction using LASSO diagnostic
Detection of influential observation is one of the crucial steps of pre-processing to identify suspicious elements of data that may be due to error or some other unknown source. Several statistical measures are developed for detection of influential observation but still challenges are there to dete...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Indian Council of Agricultural Research
2020-09-01
|
| Series: | The Indian Journal of Agricultural Sciences |
| Subjects: | |
| Online Access: | https://epubs.icar.org.in/index.php/IJAgS/article/view/104789 |
| _version_ | 1811164748447744000 |
|---|---|
| author | Neeraj Budhlakoti Anil Rai D C Mishra |
| author_facet | Neeraj Budhlakoti Anil Rai D C Mishra |
| author_sort | Neeraj Budhlakoti |
| collection | DOAJ |
| description | Detection of influential observation is one of the crucial steps of pre-processing to identify suspicious elements of data that may be due to error or some other unknown source. Several statistical measures are developed for detection of influential observation but still challenges are there to detect a true influential observation for high dimension data like gene expression, genotyping data. In this article we have demonstrated the effect of influential observation on genomic prediction accuracy by using recently proposed LASSO diagnostic, i.e. Df-Model, Df-Regpath, Df-Cvpath, Df-Lambda and Influence-LASSO. The effect of influential observation on genomic prediction accuracy was explored by observing the change in estimated and true accuracies for dataset with and without influential observation scenario. For this purpose we have used wheat and maize datasets which are available in public domain. It has been observed that influential observation had significant effects on the genomic prediction accuracy. In this study it has been shown that by implementing efficient diagnostic measure for influential observation detection, accuracy of genomic prediction can be improved. |
| first_indexed | 2024-04-10T15:26:27Z |
| format | Article |
| id | doaj.art-fb3a67ea0cd7443dbcec7c69faffcf2e |
| institution | Directory Open Access Journal |
| issn | 0019-5022 2394-3319 |
| language | English |
| last_indexed | 2024-04-10T15:26:27Z |
| publishDate | 2020-09-01 |
| publisher | Indian Council of Agricultural Research |
| record_format | Article |
| series | The Indian Journal of Agricultural Sciences |
| spelling | doaj.art-fb3a67ea0cd7443dbcec7c69faffcf2e2023-02-14T08:55:31ZengIndian Council of Agricultural ResearchThe Indian Journal of Agricultural Sciences0019-50222394-33192020-09-0190610.56093/ijas.v90i6.104789Effect of influential observation in genomic prediction using LASSO diagnosticNeeraj Budhlakoti0Anil Rai1D C Mishra2ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IndiaICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IndiaICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IndiaDetection of influential observation is one of the crucial steps of pre-processing to identify suspicious elements of data that may be due to error or some other unknown source. Several statistical measures are developed for detection of influential observation but still challenges are there to detect a true influential observation for high dimension data like gene expression, genotyping data. In this article we have demonstrated the effect of influential observation on genomic prediction accuracy by using recently proposed LASSO diagnostic, i.e. Df-Model, Df-Regpath, Df-Cvpath, Df-Lambda and Influence-LASSO. The effect of influential observation on genomic prediction accuracy was explored by observing the change in estimated and true accuracies for dataset with and without influential observation scenario. For this purpose we have used wheat and maize datasets which are available in public domain. It has been observed that influential observation had significant effects on the genomic prediction accuracy. In this study it has been shown that by implementing efficient diagnostic measure for influential observation detection, accuracy of genomic prediction can be improved.https://epubs.icar.org.in/index.php/IJAgS/article/view/104789GEBVsGenomic predictionInfluential observationLASSOMSEPrediction accuracy |
| spellingShingle | Neeraj Budhlakoti Anil Rai D C Mishra Effect of influential observation in genomic prediction using LASSO diagnostic The Indian Journal of Agricultural Sciences GEBVs Genomic prediction Influential observation LASSO MSE Prediction accuracy |
| title | Effect of influential observation in genomic prediction using LASSO diagnostic |
| title_full | Effect of influential observation in genomic prediction using LASSO diagnostic |
| title_fullStr | Effect of influential observation in genomic prediction using LASSO diagnostic |
| title_full_unstemmed | Effect of influential observation in genomic prediction using LASSO diagnostic |
| title_short | Effect of influential observation in genomic prediction using LASSO diagnostic |
| title_sort | effect of influential observation in genomic prediction using lasso diagnostic |
| topic | GEBVs Genomic prediction Influential observation LASSO MSE Prediction accuracy |
| url | https://epubs.icar.org.in/index.php/IJAgS/article/view/104789 |
| work_keys_str_mv | AT neerajbudhlakoti effectofinfluentialobservationingenomicpredictionusinglassodiagnostic AT anilrai effectofinfluentialobservationingenomicpredictionusinglassodiagnostic AT dcmishra effectofinfluentialobservationingenomicpredictionusinglassodiagnostic |