Maximum likelihood estimation of mark–recapture–recovery models in the presence of continuous covariates
Langrock R, King R (2013)
Ann. Appl. Stat. 7(3): 1709-1732.
Zeitschriftenaufsatz
| Veröffentlicht | Englisch
Download
Es wurden keine Dateien hochgeladen. Nur Publikationsnachweis!
Autor*in
Langrock, RolandUniBi;
King, Ruth
Einrichtung
Erscheinungsjahr
2013
Zeitschriftentitel
Ann. Appl. Stat.
Band
7
Ausgabe
3
Seite(n)
1709-1732
ISSN
1932-6157
Page URI
https://pub.uni-bielefeld.de/record/2902624
Zitieren
Langrock R, King R. Maximum likelihood estimation of mark–recapture–recovery models in the presence of continuous covariates. Ann. Appl. Stat. 2013;7(3):1709-1732.
Langrock, R., & King, R. (2013). Maximum likelihood estimation of mark–recapture–recovery models in the presence of continuous covariates. Ann. Appl. Stat., 7(3), 1709-1732. doi:10.1214/13-aoas644
Langrock, Roland, and King, Ruth. 2013. “Maximum likelihood estimation of mark–recapture–recovery models in the presence of continuous covariates”. Ann. Appl. Stat. 7 (3): 1709-1732.
Langrock, R., and King, R. (2013). Maximum likelihood estimation of mark–recapture–recovery models in the presence of continuous covariates. Ann. Appl. Stat. 7, 1709-1732.
Langrock, R., & King, R., 2013. Maximum likelihood estimation of mark–recapture–recovery models in the presence of continuous covariates. Ann. Appl. Stat., 7(3), p 1709-1732.
R. Langrock and R. King, “Maximum likelihood estimation of mark–recapture–recovery models in the presence of continuous covariates”, Ann. Appl. Stat., vol. 7, 2013, pp. 1709-1732.
Langrock, R., King, R.: Maximum likelihood estimation of mark–recapture–recovery models in the presence of continuous covariates. Ann. Appl. Stat. 7, 1709-1732 (2013).
Langrock, Roland, and King, Ruth. “Maximum likelihood estimation of mark–recapture–recovery models in the presence of continuous covariates”. Ann. Appl. Stat. 7.3 (2013): 1709-1732.
Export
Markieren/ Markierung löschen
Markierte Publikationen
Web of Science
Dieser Datensatz im Web of Science®Suchen in