Matches in SemOpenAlex for { <https://semopenalex.org/work/W4381436710> ?p ?o ?g. }
- W4381436710 abstract "In this paper, we propose deep partial least squares for the estimation of high-dimensional nonlinear instrumental variable regression. As a precursor to a flexible deep neural network architecture, our methodology uses partial least squares for dimension reduction and feature selection from the set of instruments and covariates. A central theoretical result, due to Brillinger (2012) Selected Works of Daving Brillinger. 589-606, shows that the feature selection provided by partial least squares is consistent and the weights are estimated up to a proportionality constant. We illustrate our methodology with synthetic datasets with a sparse and correlated network structure and draw applications to the effect of childbearing on the mother's labor supply based on classic data of Chernozhukov et al. Ann Rev Econ. (2015b):649–688. The results on synthetic data as well as applications show that the deep partial least squares method significantly outperforms other related methods. Finally, we conclude with directions for future research." @default.
- W4381436710 created "2023-06-21" @default.
- W4381436710 creator A5001018458 @default.
- W4381436710 creator A5012467860 @default.
- W4381436710 creator A5086643947 @default.
- W4381436710 date "2023-06-19" @default.
- W4381436710 modified "2023-09-26" @default.
- W4381436710 title "Deep partial least squares for instrumental variable regression" @default.
- W4381436710 cites W1886275324 @default.
- W4381436710 cites W1955857676 @default.
- W4381436710 cites W1966342871 @default.
- W4381436710 cites W1967734070 @default.
- W4381436710 cites W1970102874 @default.
- W4381436710 cites W1976251851 @default.
- W4381436710 cites W1981204440 @default.
- W4381436710 cites W1995410581 @default.
- W4381436710 cites W2026357499 @default.
- W4381436710 cites W2042335306 @default.
- W4381436710 cites W2049537980 @default.
- W4381436710 cites W2051801965 @default.
- W4381436710 cites W2062579211 @default.
- W4381436710 cites W2084840427 @default.
- W4381436710 cites W2119144792 @default.
- W4381436710 cites W2119862467 @default.
- W4381436710 cites W2128728535 @default.
- W4381436710 cites W2149353214 @default.
- W4381436710 cites W2152388911 @default.
- W4381436710 cites W2158863190 @default.
- W4381436710 cites W2163162137 @default.
- W4381436710 cites W2170007391 @default.
- W4381436710 cites W2489777820 @default.
- W4381436710 cites W2528305538 @default.
- W4381436710 cites W2542768043 @default.
- W4381436710 cites W2593182953 @default.
- W4381436710 cites W2698898 @default.
- W4381436710 cites W2803186959 @default.
- W4381436710 cites W2899370105 @default.
- W4381436710 cites W2947207002 @default.
- W4381436710 cites W2949148940 @default.
- W4381436710 cites W2980069674 @default.
- W4381436710 cites W3003423125 @default.
- W4381436710 cites W3004404638 @default.
- W4381436710 cites W3042897771 @default.
- W4381436710 cites W3121176003 @default.
- W4381436710 cites W3122304594 @default.
- W4381436710 cites W3123068006 @default.
- W4381436710 cites W3123885147 @default.
- W4381436710 cites W3124338191 @default.
- W4381436710 cites W3124352069 @default.
- W4381436710 cites W3125407111 @default.
- W4381436710 cites W3135649730 @default.
- W4381436710 cites W3174751776 @default.
- W4381436710 cites W4210542665 @default.
- W4381436710 cites W4224009453 @default.
- W4381436710 cites W4248619905 @default.
- W4381436710 cites W4248764197 @default.
- W4381436710 cites W4256619705 @default.
- W4381436710 cites W2030269849 @default.
- W4381436710 cites W2047256519 @default.
- W4381436710 doi "https://doi.org/10.1002/asmb.2787" @default.
- W4381436710 hasPublicationYear "2023" @default.
- W4381436710 type Work @default.
- W4381436710 citedByCount "0" @default.
- W4381436710 crossrefType "journal-article" @default.
- W4381436710 hasAuthorship W4381436710A5001018458 @default.
- W4381436710 hasAuthorship W4381436710A5012467860 @default.
- W4381436710 hasAuthorship W4381436710A5086643947 @default.
- W4381436710 hasBestOaLocation W43814367101 @default.
- W4381436710 hasConcept C105795698 @default.
- W4381436710 hasConcept C11413529 @default.
- W4381436710 hasConcept C119043178 @default.
- W4381436710 hasConcept C119857082 @default.
- W4381436710 hasConcept C134306372 @default.
- W4381436710 hasConcept C148483581 @default.
- W4381436710 hasConcept C154945302 @default.
- W4381436710 hasConcept C162144332 @default.
- W4381436710 hasConcept C167928553 @default.
- W4381436710 hasConcept C169241690 @default.
- W4381436710 hasConcept C177264268 @default.
- W4381436710 hasConcept C182365436 @default.
- W4381436710 hasConcept C185429906 @default.
- W4381436710 hasConcept C199360897 @default.
- W4381436710 hasConcept C202444582 @default.
- W4381436710 hasConcept C22354355 @default.
- W4381436710 hasConcept C33676613 @default.
- W4381436710 hasConcept C33923547 @default.
- W4381436710 hasConcept C41008148 @default.
- W4381436710 hasConcept C45923927 @default.
- W4381436710 hasConcept C48921125 @default.
- W4381436710 hasConcept C58489278 @default.
- W4381436710 hasConcept C83546350 @default.
- W4381436710 hasConcept C9936470 @default.
- W4381436710 hasConceptScore W4381436710C105795698 @default.
- W4381436710 hasConceptScore W4381436710C11413529 @default.
- W4381436710 hasConceptScore W4381436710C119043178 @default.
- W4381436710 hasConceptScore W4381436710C119857082 @default.
- W4381436710 hasConceptScore W4381436710C134306372 @default.
- W4381436710 hasConceptScore W4381436710C148483581 @default.
- W4381436710 hasConceptScore W4381436710C154945302 @default.
- W4381436710 hasConceptScore W4381436710C162144332 @default.