Langrock, Roland

[["dc.bibliographiccitation.firstpage","703"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Biometrics"],["dc.bibliographiccitation.lastpage","713"],["dc.bibliographiccitation.volume","69"],["dc.contributor.author","Borchers, D. L."],["dc.contributor.author","Zucchini, Walter"],["dc.contributor.author","Heide-Jorgensen, M. P."],["dc.contributor.author","Canadas, A."],["dc.contributor.author","Langrock, Roland"],["dc.date.accessioned","2018-11-07T09:20:07Z"],["dc.date.available","2018-11-07T09:20:07Z"],["dc.date.issued","2013"],["dc.description.abstract","We develop estimators for line transect surveys of animals that are stochastically unavailable for detection while within detection range. The detection process is formulated as a hidden Markov model with a binary state-dependent observation model that depends on both perpendicular and forward distances. This provides a parametric method of dealing with availability bias when estimates of availability process parameters are available even if series of availability events themselves are not. We apply the estimators to an aerial and a shipboard survey of whales, and investigate their properties by simulation. They are shown to be more general and more flexible than existing estimators based on parametric models of the availability process. We also find that methods using availability correction factors can be very biased when surveys are not close to being instantaneous, as can estimators that assume temporal independence in availability when there is temporal dependence."],["dc.identifier.doi","10.1111/biom.12049"],["dc.identifier.isi","000329285700018"],["dc.identifier.pmid","23848543"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/28805"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Wiley-blackwell"],["dc.relation.issn","1541-0420"],["dc.relation.issn","0006-341X"],["dc.title","Using Hidden Markov Models to Deal with Availability Bias on Line Transect Surveys"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","147"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Journal of Empirical Finance"],["dc.bibliographiccitation.lastpage","161"],["dc.bibliographiccitation.volume","19"],["dc.contributor.author","Langrock, Roland"],["dc.contributor.author","MacDonald, Ian L."],["dc.contributor.author","Zucchini, Walter"],["dc.date.accessioned","2018-11-07T09:15:46Z"],["dc.date.available","2018-11-07T09:15:46Z"],["dc.date.issued","2012"],["dc.description.abstract","We introduce a number of nonstandard stochastic volatility (SV) models and examine their performance when applied to the series of daily returns on several stocks listed on the New York Stock Exchange. The nonstandard models under investigation extend both the observation process and the volatility-generating process of basic SV models. In particular, we consider dependent as well as independent mixtures of autoregressive components as the log-volatility process, and include in the observation equation a lower bound on the volatility. We also consider an experimental SV model that is based on conditionally gamma-distributed volatilities. Our estimation method is based on the fact that an SV model can be approximated arbitrarily accurately by a hidden Markov model (HMM), whose likelihood is easy to compute and to maximize. The method is close, but not identical, to those of Fridman and Harris (1998), Bartolucci and De Luca (2001,2003) and Clements et al. (2006), and makes explicit the useful link between HMMs and the methods of those authors. Likelihood-based estimation of the parameters of SV models is usually regarded as challenging because the likelihood is a high-dimensional multiple integral. The HMM approximation is easy to implement and particularly convenient for fitting experimental extensions and variants of SV models such as those we introduce here. In addition, and in contrast to the case of SV models themselves, simple formulae are available for the forecast distributions of HMMs, for computing appropriately defined residuals, and for decoding, i.e. estimating the volatility of the process. (C) 2011 Elsevier B.V. All rights reserved."],["dc.identifier.doi","10.1016/j.jempfin.2011.09.003"],["dc.identifier.isi","000300073500009"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/27777"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.notes.submitter","Najko"],["dc.relation.issn","1879-1727"],["dc.relation.issn","0927-5398"],["dc.title","Some nonstandard stochastic volatility models and their estimation using structured hidden Markov models"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","715"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Computational Statistics & Data Analysis"],["dc.bibliographiccitation.lastpage","724"],["dc.bibliographiccitation.volume","55"],["dc.contributor.author","Langrock, Roland"],["dc.contributor.author","Zucchini, Walter"],["dc.date.accessioned","2018-11-07T09:01:02Z"],["dc.date.available","2018-11-07T09:01:02Z"],["dc.date.issued","2011"],["dc.description.abstract","A hidden Markov model (HMM) with a special structure that captures the 'semi'-property of hidden semi-Markov models (HSMMs) is considered. The proposed model allows arbitrary dwell-time distributions in the states of the Markov chain. For dwell-time distributions with finite support the HMM formulation is exact while for those that have infinite support, e.g. the Poisson, the distribution can be approximated with arbitrary accuracy. A benefit of using the HMM formulation is that it is easy to incorporate covariates, trend and seasonal variation particularly in the hidden component of the model. In addition, the formulae and methods for forecasting, state prediction, decoding and model checking that exist for ordinary HMMs are applicable to the proposed class of models. An HMM with explicitly modeled dwell-time distributions involving seasonality is used to model daily rainfall occurrence for sites in Bulgaria. (C) 2010 Elsevier B.V. All rights reserved."],["dc.identifier.doi","10.1016/j.csda.2010.06.015"],["dc.identifier.isi","000283017900060"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/24312"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Elsevier Science Bv"],["dc.relation.issn","1872-7352"],["dc.relation.issn","0167-9473"],["dc.title","Hidden Markov models with arbitrary state dwell-time distributions"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]