000			02191nab a22003257a 4500
001			68971
003			MX-TxCIM
005			20250620160437.0
008			250611s2024 -us|||p|op||| 00| 0 eng d
022			_a0006-341X
022			_a1541-0420 (Online)
024	8		_ahttps://doi.org/10.1093/biomtc/ujad042
040			_aMX-TxCIM
041			_aeng
100	1		_aSahin, Ö. _939386
245	1	0	_aHigh-dimensional sparse vine copula regression with application to genomic prediction
260			_aUnited Kingdom : _bOxford University Press, _c2024.
500			_aPeer review
520			_aHigh-dimensional data sets are often available in genome-enabled predictions. Such data sets include nonlinear relationships with complex dependence structures. For such situations, vine copula-based (quantile) regression is an important tool. However, the current vine copula-based regression approaches do not scale up to high and ultra-high dimensions. To perform high-dimensional sparse vine copula-based regression, we propose 2 methods. First, we show their superiority regarding computational complexity over the existing methods. Second, we define relevant, irrelevant, and redundant explanatory variables for quantile regression. Then, we show our method’s power in selecting relevant variables and prediction accuracy in high-dimensional sparse data sets via simulation studies. Next, we apply the proposed methods to the high-dimensional real data, aiming at the genomic prediction of maize traits. Some data processing and feature extraction steps for the real data are further discussed. Finally, we show the advantage of our methods over linear models and quantile regression forests in simulation studies and real data applications.
546			_aText in English
650		7	_aGenomics _91132 _2AGROVOC
650		7	_aForecasting _2AGROVOC _92701
650		7	_aData _2AGROVOC _99002
650		7	_aRegression analysis _2AGROVOC _95834
650		7	_aSelection _2AGROVOC _94749
650		7	_aBiometry _2AGROVOC _99446
700	1		_aCzado, C. _939387
773	0		_dUnited Kingdom : Oxford University Press, 2024. _gv. 80, no. 1, art. ujad042 _tBiometrics _x0006-341X
942			_2ddc _cJA _n0
999			_c68971 _d68963