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Titolo:
Efficient estimation of generalized additive nonparametric regression models
Autore:
Linton, OB;
Indirizzi:
Univ London London Sch Econ & Polit Sci, London WC2A 2AE, England Univ London London Sch Econ & Polit Sci London England WC2A 2AE England Yale Univ, New Haven, CT 06520 USA Yale Univ New Haven CT USA 06520Yale Univ, New Haven, CT 06520 USA
Titolo Testata:
ECONOMETRIC THEORY
fascicolo: 4, volume: 16, anno: 2000,
pagine: 502 - 523
SICI:
0266-4666(200008)16:4<502:EEOGAN>2.0.ZU;2-#
Fonte:
ISI
Lingua:
ENG
Soggetto:
MAXIMUM-LIKELIHOOD METHODS;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Social & Behavioral Sciences
Citazioni:
33
Recensione:
Indirizzi per estratti:
Indirizzo: Linton, OB Univ London London Sch Econ & Polit Sci, Houghton St, London WC2A 2AE, England Univ London London Sch Econ & Polit Sci Houghton St LondonEngland WC2A 2AE
Citazione:
O.B. Linton, "Efficient estimation of generalized additive nonparametric regression models", ECONOMET TH, 16(4), 2000, pp. 502-523

Abstract

We define new procedures for estimating generalized additive nonparametricregression models that are more efficient than the Linton and Hardle (1996, Biometrika 83, 529-540) integration-based method and achieve certain oracle bounds, We consider criterion functions based on the Linear exponential family, which includes many important special cases, We also consider the extension to multiple parameter models like the gamma distribution and to models for conditional heteroskedasticity.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 07/07/20 alle ore 05:25:28