Matches in SemOpenAlex for { <https://semopenalex.org/work/W2952374274> ?p ?o ?g. }
Showing items 1 to 87 of
87
with 100 items per page.
- W2952374274 abstract "We introduce sparse random projection, an important dimension-reduction tool from machine learning, for the estimation of discrete-choice models with high-dimensional choice sets. Initially, high-dimensional data are compressed into a lower-dimensional Euclidean space using random projections. Subsequently, estimation proceeds using cyclic monotonicity moment inequalities implied by the multinomial choice model; the estimation procedure is semi-parametric and does not require explicit distributional assumptions to be made regarding the random utility errors. The random projection procedure is justified via the Johnson-Lindenstrauss Lemma -- the pairwise distances between data points are preserved during data compression, which we exploit to show convergence of our estimator. The estimator works well in simulations and in an application to a supermarket scanner dataset." @default.
- W2952374274 created "2019-06-27" @default.
- W2952374274 creator A5051153018 @default.
- W2952374274 creator A5069327640 @default.
- W2952374274 date "2016-01-01" @default.
- W2952374274 modified "2023-09-23" @default.
- W2952374274 title "Random Projection Estimation of Discrete-Choice Models with Large Choice Sets" @default.
- W2952374274 cites W1550812296 @default.
- W2952374274 cites W1551610462 @default.
- W2952374274 cites W1563690153 @default.
- W2952374274 cites W156781394 @default.
- W2952374274 cites W1577871831 @default.
- W2952374274 cites W1956544710 @default.
- W2952374274 cites W1982197631 @default.
- W2952374274 cites W2027123933 @default.
- W2952374274 cites W2037757210 @default.
- W2952374274 cites W2041836310 @default.
- W2952374274 cites W2042156630 @default.
- W2952374274 cites W2076228296 @default.
- W2952374274 cites W2088658556 @default.
- W2952374274 cites W2111084117 @default.
- W2952374274 cites W2116039321 @default.
- W2952374274 cites W2116877194 @default.
- W2952374274 cites W2133681842 @default.
- W2952374274 cites W2163162137 @default.
- W2952374274 cites W2184644469 @default.
- W2952374274 cites W2300397590 @default.
- W2952374274 cites W2506486438 @default.
- W2952374274 cites W2549301278 @default.
- W2952374274 cites W2593245224 @default.
- W2952374274 cites W2949883488 @default.
- W2952374274 cites W2951812754 @default.
- W2952374274 cites W3124272086 @default.
- W2952374274 cites W3124441429 @default.
- W2952374274 cites W3125357055 @default.
- W2952374274 cites W3125716315 @default.
- W2952374274 doi "https://doi.org/10.2139/ssrn.2764607" @default.
- W2952374274 hasPublicationYear "2016" @default.
- W2952374274 type Work @default.
- W2952374274 sameAs 2952374274 @default.
- W2952374274 citedByCount "2" @default.
- W2952374274 countsByYear W29523742742019 @default.
- W2952374274 crossrefType "journal-article" @default.
- W2952374274 hasAuthorship W2952374274A5051153018 @default.
- W2952374274 hasAuthorship W2952374274A5069327640 @default.
- W2952374274 hasBestOaLocation W29523742742 @default.
- W2952374274 hasConcept C105795698 @default.
- W2952374274 hasConcept C11413529 @default.
- W2952374274 hasConcept C149782125 @default.
- W2952374274 hasConcept C162324750 @default.
- W2952374274 hasConcept C163839840 @default.
- W2952374274 hasConcept C187736073 @default.
- W2952374274 hasConcept C190669063 @default.
- W2952374274 hasConcept C33923547 @default.
- W2952374274 hasConcept C41008148 @default.
- W2952374274 hasConcept C57493831 @default.
- W2952374274 hasConcept C96250715 @default.
- W2952374274 hasConceptScore W2952374274C105795698 @default.
- W2952374274 hasConceptScore W2952374274C11413529 @default.
- W2952374274 hasConceptScore W2952374274C149782125 @default.
- W2952374274 hasConceptScore W2952374274C162324750 @default.
- W2952374274 hasConceptScore W2952374274C163839840 @default.
- W2952374274 hasConceptScore W2952374274C187736073 @default.
- W2952374274 hasConceptScore W2952374274C190669063 @default.
- W2952374274 hasConceptScore W2952374274C33923547 @default.
- W2952374274 hasConceptScore W2952374274C41008148 @default.
- W2952374274 hasConceptScore W2952374274C57493831 @default.
- W2952374274 hasConceptScore W2952374274C96250715 @default.
- W2952374274 hasLocation W29523742741 @default.
- W2952374274 hasLocation W29523742742 @default.
- W2952374274 hasLocation W29523742743 @default.
- W2952374274 hasOpenAccess W2952374274 @default.
- W2952374274 hasPrimaryLocation W29523742741 @default.
- W2952374274 hasRelatedWork W1510377764 @default.
- W2952374274 hasRelatedWork W1536015819 @default.
- W2952374274 hasRelatedWork W2017008346 @default.
- W2952374274 hasRelatedWork W2102160045 @default.
- W2952374274 hasRelatedWork W2279108378 @default.
- W2952374274 hasRelatedWork W2319271101 @default.
- W2952374274 hasRelatedWork W2510224656 @default.
- W2952374274 hasRelatedWork W3001269217 @default.
- W2952374274 hasRelatedWork W4313182677 @default.
- W2952374274 hasRelatedWork W2181671361 @default.
- W2952374274 isParatext "false" @default.
- W2952374274 isRetracted "false" @default.
- W2952374274 magId "2952374274" @default.
- W2952374274 workType "article" @default.