Langrock, Roland

[["dc.bibliographiccitation.firstpage","2955"],["dc.bibliographiccitation.issue","12"],["dc.bibliographiccitation.journal","Journal of Applied Statistics"],["dc.bibliographiccitation.lastpage","2970"],["dc.bibliographiccitation.volume","38"],["dc.contributor.author","Langrock, Roland"],["dc.date.accessioned","2018-11-07T09:01:31Z"],["dc.date.available","2018-11-07T09:01:31Z"],["dc.date.issued","2011"],["dc.description.abstract","Nonlinear and non-Gaussian state-space models (SSMs) are fitted to different types of time series. The applications include homogeneous and seasonal time series, in particular earthquake counts, polio counts, rainfall occurrence data, glacial varve data and daily returns on a share. The considered SSMs comprise Poisson, Bernoulli, gamma and Student-t distributions at the observation level. Parameter estimations for the SSMs are carried out using a likelihood approximation that is obtained after discretization of the state space. The approximation can be made arbitrarily accurate, and the approximated likelihood is precisely that of a finite-state hidden Markov model (HMM). The proposed method enables us to apply standard HMM techniques. It is easy to implement and can be extended to all kinds of SSMs in a straightforward manner."],["dc.identifier.doi","10.1080/02664763.2011.573543"],["dc.identifier.isi","000298924500020"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/24449"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Taylor & Francis Ltd"],["dc.relation.issn","0266-4763"],["dc.title","Some applications of nonlinear and non-Gaussian state-space modelling by means of hidden Markov models"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["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","259"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Statistics and Computing"],["dc.bibliographiccitation.lastpage","270"],["dc.bibliographiccitation.volume","27"],["dc.contributor.author","Langrock, Roland"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Glennie, Richard"],["dc.contributor.author","Michelot, Théo"],["dc.date.accessioned","2017-09-07T11:47:51Z"],["dc.date.available","2017-09-07T11:47:51Z"],["dc.date.issued","2017"],["dc.description.abstract","We consider Markov-switching regression models, i.e. models for time series regression analyses where the functional relationship between covariates and response is subject to regime switching controlled by an unobservable Markov chain. Building on the powerful hidden Markov model machinery and the methods for penalized B-splines routinely used in regression analyses, we develop a framework for nonparametrically estimating the functional form of the effect of the covariates in such a regression model, assuming an additive structure of the predictor. The resulting class of Markov-switching generalized additive models is immensely flexible, and contains as special cases the common parametric Markov-switching regression models and also generalized additive and generalized linear models. The feasibility of the suggested maximum penalized likelihood approach is demonstrated by simulation. We further illustrate the approach using two real data applications, modelling (i) how sales data depend on advertising spending and (ii) how energy price in Spain depends on the Euro/Dollar exchange rate."],["dc.identifier.doi","10.1007/s11222-015-9620-3"],["dc.identifier.gro","3149380"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13666"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/6050"],["dc.language.iso","en"],["dc.notes","Open Access"],["dc.notes.intern","Kneib Crossref Import"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","0960-3174"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Markov-switching generalized additive models"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","180"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Interface Focus"],["dc.bibliographiccitation.lastpage","189"],["dc.bibliographiccitation.volume","2"],["dc.contributor.author","Schliehe-Diecks, Susanne"],["dc.contributor.author","Kappeler, Peter"],["dc.contributor.author","Langrock, Roland"],["dc.date.accessioned","2017-09-07T11:48:36Z"],["dc.date.available","2017-09-07T11:48:36Z"],["dc.date.issued","2012"],["dc.description.abstract","Analysing behavioural sequences and quantifying the likelihood of occurrences of different behaviours is a difficult task as motivational states are not observable. Furthermore, it is ecologically highly relevant and yet more complicated to scale an appropriate model for one individual up to the population level. In this manuscript (mixed) hidden Markov models (HMMs) are used to model the feeding behaviour of 54 subadult grey mouse lemurs (Microcebus murinus), small nocturnal primates endemic to Madagascar that forage solitarily. Our primary aim is to introduce ecologists and other users to various HMM methods, many of which have been developed only recently, and which in this form have not previously been synthesized in the ecological literature. Our specific application of mixed HMMs aims at gaining a better understanding of mouse lemur behaviour, in particular concerning sex-specific differences. The model we consider incorporates random effects for accommodating heterogeneity across animals, i.e. accounts for different personalities of the animals. Additional subject- and time-specific covariates in the model describe the influence of sex, body mass and time of night."],["dc.identifier.doi","10.1098/rsfs.2011.0077"],["dc.identifier.gro","3150873"],["dc.identifier.pmid","23565332"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/7668"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.relation.issn","2042-8898"],["dc.subject","behavioural analysis; maximum likelihood; motivational states; random effects; state-space model; subject-specific covariate"],["dc.title","On the application of mixed hidden Markov models to multiple behavioural time series"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["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","435"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Statistical Methods & Applications"],["dc.bibliographiccitation.lastpage","449"],["dc.bibliographiccitation.volume","23"],["dc.contributor.author","Langrock, Roland"],["dc.contributor.author","Heidenreich, Nils-Bastian"],["dc.contributor.author","Sperlich, Stefan"],["dc.date.accessioned","2018-11-07T09:37:11Z"],["dc.date.available","2018-11-07T09:37:11Z"],["dc.date.issued","2014"],["dc.description.abstract","Conventional, parametric multinomial logit models are in general not sufficient for capturing the complex structures of electorates. In this paper, we use a semiparametric multinomial logit model to give an analysis of party preferences along individuals' characteristics using a sample of the German electorate in 2006. Germany is a particularly strong case for more flexible nonparametric approaches in this context, since due to the reunification and the preceding different political histories the composition of the electorate is very complex and nuanced. Our analysis reveals strong interactions of the covariates age and income, and highly nonlinear shapes of the factor impacts for each party's likelihood to be supported. Notably, we develop and provide a smoothed likelihood estimator for semiparametric multinomial logit models, which can be applied also in other application fields, such as, e.g., marketing."],["dc.description.sponsorship","Swiss National Science Foundation [100018-140295]"],["dc.identifier.doi","10.1007/s10260-014-0261-z"],["dc.identifier.isi","000339895900011"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/32781"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Springer"],["dc.publisher.place","Heidelberg"],["dc.relation.issn","1613-981X"],["dc.relation.issn","1618-2510"],["dc.title","Kernel-based semiparametric multinomial logit modelling of political party preferences"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","520"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Biometrics"],["dc.bibliographiccitation.lastpage","528"],["dc.bibliographiccitation.volume","71"],["dc.contributor.author","Langrock, Roland"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Sohn, Alexander"],["dc.contributor.author","Ruiter, Stacy L. de"],["dc.date.accessioned","2017-09-07T11:47:19Z"],["dc.date.available","2017-09-07T11:47:19Z"],["dc.date.issued","2015"],["dc.identifier.doi","10.1111/biom.12282"],["dc.identifier.gro","3149327"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5991"],["dc.notes.intern","Kneib Crossref Import"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.publisher","Wiley-Blackwell"],["dc.relation.issn","0006-341X"],["dc.title","Nonparametric inference in hidden Markov models using P-splines"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","222"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Biometrical Journal"],["dc.bibliographiccitation.lastpage","239"],["dc.bibliographiccitation.volume","58"],["dc.contributor.author","Michelot, Théo"],["dc.contributor.author","Langrock, Roland"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","King, Ruth"],["dc.date.accessioned","2017-09-07T11:47:16Z"],["dc.date.available","2017-09-07T11:47:16Z"],["dc.date.issued","2015"],["dc.identifier.doi","10.1002/bimj.201400222"],["dc.identifier.gro","3149309"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5971"],["dc.notes.intern","Kneib Crossref Import"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.publisher","Wiley-Blackwell"],["dc.relation.issn","0323-3847"],["dc.title","Maximum penalized likelihood estimation in semiparametric mark-recapture-recovery models"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","517"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Computational Statistics"],["dc.bibliographiccitation.lastpage","537"],["dc.bibliographiccitation.volume","30"],["dc.contributor.author","Langrock, Roland"],["dc.contributor.author","Michelot, Théo"],["dc.contributor.author","Sohn, Alexander"],["dc.contributor.author","Kneib, Thomas"],["dc.date.accessioned","2017-09-07T11:47:45Z"],["dc.date.available","2017-09-07T11:47:45Z"],["dc.date.issued","2014"],["dc.identifier.doi","10.1007/s00180-014-0547-5"],["dc.identifier.gro","3149348"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/6015"],["dc.notes.intern","Kneib Crossref Import"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.publisher","Springer Nature"],["dc.relation.issn","0943-4062"],["dc.title","Semiparametric stochastic volatility modelling using penalized splines"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["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"]]