Nonparametric inference in hidden Markov models using P-splines

Langrock R, Kneib T, Sohn A, DeRuiter SL (2015)
Biometrics 71(2): 520-528.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
Download
Es wurden keine Dateien hochgeladen. Nur Publikationsnachweis!
Autor*in
Langrock, RolandUniBi; Kneib, Thomas; Sohn, Alexander; DeRuiter, Stacy L.
Erscheinungsjahr
2015
Zeitschriftentitel
Biometrics
Band
71
Ausgabe
2
Seite(n)
520-528
ISSN
0006-341X
Page URI
https://pub.uni-bielefeld.de/record/2902630

Zitieren

Langrock R, Kneib T, Sohn A, DeRuiter SL. Nonparametric inference in hidden Markov models using P-splines. Biometrics. 2015;71(2):520-528.
Langrock, R., Kneib, T., Sohn, A., & DeRuiter, S. L. (2015). Nonparametric inference in hidden Markov models using P-splines. Biometrics, 71(2), 520-528. doi:10.1111/biom.12282
Langrock, Roland, Kneib, Thomas, Sohn, Alexander, and DeRuiter, Stacy L. 2015. “Nonparametric inference in hidden Markov models using P-splines”. Biometrics 71 (2): 520-528.
Langrock, R., Kneib, T., Sohn, A., and DeRuiter, S. L. (2015). Nonparametric inference in hidden Markov models using P-splines. Biometrics 71, 520-528.
Langrock, R., et al., 2015. Nonparametric inference in hidden Markov models using P-splines. Biometrics, 71(2), p 520-528.
R. Langrock, et al., “Nonparametric inference in hidden Markov models using P-splines”, Biometrics, vol. 71, 2015, pp. 520-528.
Langrock, R., Kneib, T., Sohn, A., DeRuiter, S.L.: Nonparametric inference in hidden Markov models using P-splines. Biometrics. 71, 520-528 (2015).
Langrock, Roland, Kneib, Thomas, Sohn, Alexander, and DeRuiter, Stacy L. “Nonparametric inference in hidden Markov models using P-splines”. Biometrics 71.2 (2015): 520-528.

6 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

An analysis of pilot whale vocalization activity using hidden Markov models.
Popov V, Langrock R, DeRuiter SL, Visser F., J Acoust Soc Am 141(1), 2017
PMID: 28147612
Hidden Markov models reveal complexity in the diving behaviour of short-finned pilot whales.
Quick NJ, Isojunno S, Sadykova D, Bowers M, Nowacek DP, Read AJ., Sci Rep 7(), 2017
PMID: 28361954
Estimation and simulation of foraging trips in land-based marine predators.
Michelot T, Langrock R, Bestley S, Jonsen ID, Photopoulou T, Patterson TA., Ecology 98(7), 2017
PMID: 28470722
Maximum penalized likelihood estimation in semiparametric mark-recapture-recovery models.
Michelot T, Langrock R, Kneib T, King R., Biom J 58(1), 2016
PMID: 26289495

26 References

Daten bereitgestellt von Europe PubMed Central.

Mixed hidden Markov models: An extension of the hidden Markov model to the longitudinal data setting
Altman, Journal of the American Statistical Association 24(), 2007
Nonparametric identification of hidden Markov models
Alexandrovich, arXiv (), 2014
Diel variation in beaked whale diving behavior
Baird, Marine Mammal Science 24(), 2008
Computational issues in parameter estimation for stationary hidden Markov models
Bulla, Computational Statistics 13(), 2008
Using hidden Markov models to deal with availability bias on line transect surveys.
Borchers DL, Zucchini W, Heide-Jorgensen MP, Canadas A, Langrock R., Biometrics 69(3), 2013
PMID: 23848543
Selecting hidden Markov model state number with cross-validated likelihood
Celeux, Computational Statistics 23(), 2008
Understanding the impacts of anthropogenic sounds on beaked whales
Cox, Journal of Cetacean Research and Management 7(), 2006
Semiparametric hidden Markov models
Dannemann, Journal of Computational and Graphical Statistics 21(), 2012

de, 1978

Durbin, 1998
Flexible smoothing with B-splines and penalties
Eilers, Statistical Science 11(), 1996
Finite state space non parametric Hidden Markov Models are in general identifiable
Gassiat, arXiv (), 2013
Hidden Markov models with state-dependent mixtures: Minimal representation, model testing and applications to clustering
Holzmann, Statistics and Computing (), 0
Diving and ranging behaviour of odontocetes: A methodological review and critique
Hooker, Mammal Review 31(), 2001
Simultaneous confidence bands for penalized spline estimators
Krivobokova, Journal of the American Statistical Association 105(), 2010
Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions.
Langrock R, King R, Matthiopoulos J, Thomas L, Fortin D, Morales JM., Ecology 93(11), 2012
PMID: 23236905
Combining hidden Markov models for comparing the dynamics of multiple sleep electroencephalograms.
Langrock R, Swihart BJ, Caffo BS, Punjabi NM, Crainiceanu CM., Stat Med 32(19), 2013
PMID: 23348835
Numerical maximisation of likelihood: A neglected alternative to EM
MacDonald, International Statistical Review 82(), 2014
A semiparametric approach to hidden Markov models under longitudinal observations
Maruotti, Statistics and Computing 19(), 2009

Ruppert, 2003
Density estimation and comparison with a penalized mixture approach
Schellhase, Computational Statistics 27(), 2012
Beaked whales respond to simulated and actual navy sonar.
Tyack PL, Zimmer WM, Moretti D, Southall BL, Claridge DE, Durban JW, Clark CW, D'Amico A, DiMarzio N, Jarvis S, McCarthy E, Morrissey R, Ward J, Boyd IL., PLoS ONE 6(3), 2011
PMID: 21423729
Bayesian non-parametric hidden Markov models with applications in genomics
Yau, Journal of the Royal Statistical Society, Series B 73(), 2011

Zucchini, 2009
Modeling time series of animal behavior by means of a latent-state model with feedback.
Zucchini W, Raubenheimer D, MacDonald IL., Biometrics 64(3), 2007
PMID: 18047533
Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Web of Science

Dieser Datensatz im Web of Science®
Quellen

PMID: 25586063
PubMed | Europe PMC

Suchen in

Google Scholar