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Názov Double machine learning for sample selection models Aut.údaje Michela Bia ... [et al.] Autor Bia Michela (34%)
Spoluautori Huber Martin (33%)
Lafférs Lukáš 1986- (33%) UMBFP10 - Katedra matematiky
Zdroj.dok. Journal of business & economic statistics. Vol. 42, no. 3 (2024), pp. 958-969. - Philadelphia : Taylor & Francis, 2024 Kľúč.slová strojové učenie - machine learning odhadovanie Form.deskr. články - journal articles Jazyk dok. angličtina Krajina Spojené štáty Anotácia This article considers the evaluation of discretely distributed treatments when outcomes are only observed for a subpopulation due to sample selection or outcome attrition. For identification, we combine a selection-on-observables assumption for treatment assignment with either selection-on-observables or instrumental variable assumptions concerning the outcome attrition/sample selection process. We also consider dynamic confounding, meaning that covariates that jointly affect sample selection and the outcome may (at least partly) be influenced by the treatment. To control in a data-driven way for a potentially high dimensional set of pre- and/or post-treatment covariates, we adapt the double machine learning framework for treatment evaluation to sample selection problems. We make use of (a) Neyman-orthogonal, doubly robust, and efficient score functions, which imply the robustness of treatment effect estimation to moderate regularization biases in the machine learning-based estimation of the outcome, treatment, or sample selection models and (b) sample splitting (or cross-fitting) to prevent overfitting bias. We demonstrate that the proposed estimators are asymptotically normal and root-n consistent and investigate their finite sample properties in a simulation study. We also apply our proposed methodology to the Job Corps data. The estimator is available in the causalweight package for the statistical software R. URL Link na zdrojový dokument Kategória publikačnej činnosti ADC Číslo archívnej kópie 54594 Katal.org. BB301 - Univerzitná knižnica Univerzity Mateja Bela v Banskej Bystrici Báza dát xpca - PUBLIKAČNÁ ČINNOSŤ Odkazy PERIODIKÁ-Súborný záznam periodika 
článok
Názov Forecasting and Time Series Analysis Aut.údaje Douglas C. Montgomery, Lynwood A. Johnson, John S. Gardiner Autor Montgomery Douglas C.
Spoluautori Johnson Lynwood A.
Gardiner John S.
Vyd.údaje New York : McGraw-Hill Book Company , 1990. - 381 s. Vydanie 2.vyd. ISBN 0-07-042858-1 Kľúč.slová matematika - mathematics odhadovanie tabuĺky štatistické Jazyk dok. angličtina Krajina Spojené štáty Systematika 519.22=20 Katal.org. BB301 - Univerzitná knižnica Univerzity Mateja Bela v Banskej Bystrici Báza dát xkni - KNIHY Počet ex. 1, z toho voľných 0, prezenčne 1 
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