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Search: Susanne Gschlößl
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1.
Modelling count data with overdispersion and spatial effects
Title:
Modelling count data with overdispersion and spatial effects
Author:
Susanne Gschlößl
;
Claudia Czado
Susanne Gschlößl
;
Claudia Czado
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Description:
Bayesian inference, Count data, Overdispersion, Spatial regression models, Zero inflated models
Bayesian inference, Count data, Overdispersion, Spatial regression models, Zero inflated models
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Document Type:
article
URL:
http://hdl.handle.net/10.1007/s0036200600316
http://hdl.handle.net/10.1007/s0036200600316
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RePEc: Research Papers in Economics
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2.
Does a Gibbs sampler approach to spatial Poisson regression models outperform a single site MH sampler?
Open Access
Title:
Does a Gibbs sampler approach to spatial Poisson regression models outperform a single site MH sampler?
Author:
Susanne Gschlößl
;
Claudia Czado
Susanne Gschlößl
;
Claudia Czado
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Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20120417
Source:
http://wwwm4.mathematik.tumuenchen.de/m4/Papers/Czado/gschloesslczadogibbssampler.pdf
http://wwwm4.mathematik.tumuenchen.de/m4/Papers/Czado/gschloesslczadogibbssampler.pdf
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Document Type:
text
Language:
en
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
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URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.147.2128
http://wwwm4.mathematik.tumuenchen.de/m4/Papers/Czado/gschloesslczadogibbssampler.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.147.2128
http://wwwm4.mathematik.tumuenchen.de/m4/Papers/Czado/gschloesslczadogibbssampler.pdf
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3.
Spatial modelling of claim frequency and claim size in nonlife insurance
Open Access
Title:
Spatial modelling of claim frequency and claim size in nonlife insurance
Author:
Susanne Gschlößl
;
Claudia Czado
Susanne Gschlößl
;
Claudia Czado
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The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20120417
Source:
http://wwwm4.mathematik.tumuenchen.de/m4/Papers/Czado/gschloesslSAJ_final.pdf
http://wwwm4.mathematik.tumuenchen.de/m4/Papers/Czado/gschloesslSAJ_final.pdf
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Document Type:
text
Language:
en
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Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
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URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.147.1062
http://wwwm4.mathematik.tumuenchen.de/m4/Papers/Czado/gschloesslSAJ_final.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.147.1062
http://wwwm4.mathematik.tumuenchen.de/m4/Papers/Czado/gschloesslSAJ_final.pdf
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4.
Modelling count data with overdispersion and spatial effects
Open Access
Title:
Modelling count data with overdispersion and spatial effects
Author:
Susanne Gschlößl
;
Claudia Czado
Susanne Gschlößl
;
Claudia Czado
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Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20120417
Source:
http://wwwm4.ma.tum.de/Papers/Czado/paper08shortrevisedplain.pdf
http://wwwm4.ma.tum.de/Papers/Czado/paper08shortrevisedplain.pdf
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Document Type:
text
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en
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Metadata may be used without restrictions as long as the oai identifier remains attached to it.
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URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.178.3748
http://wwwm4.ma.tum.de/Papers/Czado/paper08shortrevisedplain.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.178.3748
http://wwwm4.ma.tum.de/Papers/Czado/paper08shortrevisedplain.pdf
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5.
Modelling count data with overdispersion and spatial effects
Open Access
Title:
Modelling count data with overdispersion and spatial effects
Author:
Susanne Gschlößl
;
Claudia Czado
Susanne Gschlößl
;
Claudia Czado
Minimize authors
Description:
In this paper we consider regression models for count data allowing for overdispersion in a Bayesian framework. We account for unobserved heterogeneity in the data in two ways. On the one hand, we consider more flexible models than a common Poisson model allowing for overdispersion in different ways. In particular, the negative binomial and the ...
In this paper we consider regression models for count data allowing for overdispersion in a Bayesian framework. We account for unobserved heterogeneity in the data in two ways. On the one hand, we consider more flexible models than a common Poisson model allowing for overdispersion in different ways. In particular, the negative binomial and the generalized Poisson distribution are addressed where overdispersion is modelled by an additional model parameter. Further, zeroinflated models in which overdispersion is assumed to be caused by an excessive number of zeros are discussed. On the other hand, extra spatial variability in the data is taken into account by adding spatial random effects to the models. This approach allows for an underlying spatial dependency structure which is modelled using a conditional autoregressive prior based on Pettitt et al. (2002). In an application the presented models are used to analyse the number of invasive meningococcal disease cases in Germany in the year 2004. Models are compared according to the deviance information criterion (DIC) suggested by Spiegelhalter et al. (2002) and using proper scoring rules, see for example Gneiting and Raftery (2004). We observe a rather high degree of overdispersion in the data which is captured best by the GP model when spatial effects are neglected. While the addition of spatial effects to the models allowing for overdispersion gives no or only little improvement, a spatial Poisson model is to be preferred over all other models according to the considered criteria.
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The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20120417
Source:
http://wwwm4.mathematik.tumuenchen.de/m4/Papers/Czado/paper08shortrevisedplain.ps
http://wwwm4.mathematik.tumuenchen.de/m4/Papers/Czado/paper08shortrevisedplain.ps
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Document Type:
text
Language:
en
DDC:
310 Collections of general statistics
(computed)
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Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
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URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.61.3972
http://wwwm4.mathematik.tumuenchen.de/m4/Papers/Czado/paper08shortrevisedplain.ps
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.61.3972
http://wwwm4.mathematik.tumuenchen.de/m4/Papers/Czado/paper08shortrevisedplain.ps
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6.
Does a Gibbs sampler approach to spatial Poisson regression models outperform a single site MH sampler?
Open Access
Title:
Does a Gibbs sampler approach to spatial Poisson regression models outperform a single site MH sampler?
Author:
Susanne Gschlößl
;
Claudia Czado
Susanne Gschlößl
;
Claudia Czado
Minimize authors
Description:
In this paper we present and evaluate a Gibbs sampler for a Poisson regression model including spatial e#ects. The approach is based on FruhwirthSchnatter and Wagner (2004b) who show that by data augmentation using the introduction of two sequences of latent variables a Poisson regression model can be transformed into an approximate normal line...
In this paper we present and evaluate a Gibbs sampler for a Poisson regression model including spatial e#ects. The approach is based on FruhwirthSchnatter and Wagner (2004b) who show that by data augmentation using the introduction of two sequences of latent variables a Poisson regression model can be transformed into an approximate normal linear model. We show how this methodology can be extended to spatial Poisson regression models and give details of the resulting Gibbs sampler. In particular, the influence of model parameterisation and di#erent update strategies on the mixing of the MCMC chains is discussed.
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Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20090419
Source:
http://wwwm4.ma.tum.de/Papers/Czado/gibbsaugmentationrevised.ps
http://wwwm4.ma.tum.de/Papers/Czado/gibbsaugmentationrevised.ps
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Document Type:
text
Language:
en
Subjects:
Key words ; block updates ; collapsing ; data augmentation ; Gibbs sampler ; model parameterisa
Key words ; block updates ; collapsing ; data augmentation ; Gibbs sampler ; model parameterisa
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Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
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URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.61.3980
http://wwwm4.ma.tum.de/Papers/Czado/gibbsaugmentationrevised.ps
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.61.3980
http://wwwm4.ma.tum.de/Papers/Czado/gibbsaugmentationrevised.ps
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7.
Does a Gibbs sampler approach to spatial Poisson regression models outperform a single site MH sampler?
Open Access
Title:
Does a Gibbs sampler approach to spatial Poisson regression models outperform a single site MH sampler?
Author:
Susanne Gschlößl
;
Claudia Czado
Susanne Gschlößl
;
Claudia Czado
Minimize authors
Description:
In this paper we present and evaluate a Gibbs sampler for a Poisson regression model including spatial effects. The approach is based on FrühwirthSchnatter and Wagner (2004b) who show that by data augmentation using the introduction of two sequences of latent variables a Poisson regression model can be transformed into an approximate normal lin...
In this paper we present and evaluate a Gibbs sampler for a Poisson regression model including spatial effects. The approach is based on FrühwirthSchnatter and Wagner (2004b) who show that by data augmentation using the introduction of two sequences of latent variables a Poisson regression model can be transformed into an approximate normal linear model. We show how this methodology can be extended to spatial Poisson regression models and give details of the resulting Gibbs sampler. In particular, the influence of model parameterisation and different update strategies on the mixing of the MCMC chains is discussed. The developed Gibbs samplers are analysed in two simulation studies and applied to model the expected number of claims for policyholders of a German car insurance company. The mixing of the Gibbs samplers depends crucially on the model parameterisation and the update schemes. The best mixing is achieved when collapsed algorithms are used, reasonable low autocorrelations for the spatial effects are obtained in this case. For the regression effects however, autocorrelations are rather high, especially for data with very low heterogeneity. For comparison a single component Metropolis Hastings algorithms is applied which displays very good mixing for all components. Although the Metropolis Hastings sampler requires a higher computational effort, it outperforms the Gibbs samplers which would have to be run considerably longer in order to obtain the same precision of the parameters.
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Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20080701
Source:
http://wwwm4.ma.tum.de/Papers/Czado/gibbsaugmentationrevised.pdf
http://wwwm4.ma.tum.de/Papers/Czado/gibbsaugmentationrevised.pdf
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Document Type:
text
Language:
en
Subjects:
Key words ; block updates ; collapsing ; data augmentation ; Gibbs sampler ; model parameterisation
Key words ; block updates ; collapsing ; data augmentation ; Gibbs sampler ; model parameterisation
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DDC:
310 Collections of general statistics
(computed)
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
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URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.69.7883
http://wwwm4.ma.tum.de/Papers/Czado/gibbsaugmentationrevised.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.69.7883
http://wwwm4.ma.tum.de/Papers/Czado/gibbsaugmentationrevised.pdf
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8.
Nonnested model comparison of GLM and GAM count regression models for life insurance data
Open Access
Title:
Nonnested model comparison of GLM and GAM count regression models for life insurance data
Author:
Claudia Czado
;
Julia Pfettner
;
Susanne Gschlößl
;
Frank Schiller
Claudia Czado
;
Julia Pfettner
;
Susanne Gschlößl
;
Frank Schiller
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Description:
Pricing and product development of life insurance contracts require the determination of company specific risk factors and their resulting risk rates. The paper shows how to use generalized linear models (GLM) and generalized additive models (GAM) to quantify the effect of risk factors by allowing for non linear and interaction effects. Nonneste...
Pricing and product development of life insurance contracts require the determination of company specific risk factors and their resulting risk rates. The paper shows how to use generalized linear models (GLM) and generalized additive models (GAM) to quantify the effect of risk factors by allowing for non linear and interaction effects. Nonnested model comparison between GLM and GAM based specifications are facilitated using nonrandomized probability integral transforms (see Czado, Gneiting, and Held (2009)) and proper scores (see Gneiting and Raftery (2007)) developed for count responses. These allow for the assessment of model fit and predictive capability of a model. For a life insurance portfolio it is shown that the computationally less demanding GLM specification performs similarly to a GAM specification.
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Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20100331
Source:
http://wwwm4.ma.tum.de/Papers/Czado/final_GS.pdf
http://wwwm4.ma.tum.de/Papers/Czado/final_GS.pdf
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Document Type:
text
Language:
en
Subjects:
Count regression ; GLM ; GAM ; prediction ; probability integral transform ; proper
Count regression ; GLM ; GAM ; prediction ; probability integral transform ; proper
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Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
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URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.158.8488
http://wwwm4.ma.tum.de/Papers/Czado/final_GS.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.158.8488
http://wwwm4.ma.tum.de/Papers/Czado/final_GS.pdf
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9.
Calculation of LTC Premiums based on direct estimates of transition probabilities
Open Access
Title:
Calculation of LTC Premiums based on direct estimates of transition probabilities
Author:
Florian Helms
;
Claudia Czado
;
Susanne Gschlößl
;
Technische Universität München
;
Zentrum Mathematik
Florian Helms
;
Claudia Czado
;
Susanne Gschlößl
;
Technische Universität München
;
Zentrum Mathematik
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Description:
In this paper we model the lifehistory of LTC patients using a Markovian multistate model in order to calculate premiums for a given LTCplan. Instead of estimating the transition intensities in this model we use the approach suggested by Andersen et al. (2003) for a direct estimation of the transition probabilities. Based on the AalenJohanse...
In this paper we model the lifehistory of LTC patients using a Markovian multistate model in order to calculate premiums for a given LTCplan. Instead of estimating the transition intensities in this model we use the approach suggested by Andersen et al. (2003) for a direct estimation of the transition probabilities. Based on the AalenJohansen estimator, an almost unbiased estimator for the transition matrix of a Markovian multistate model, we calculate socalled pseudovalues, known from Jackknife methods. Further, we assume that the relationship between these pseudovalues and the covariates of our data are given by a GLM with the logit as linkfunction. Since the GLMs do not allow for correlation between successive observations we use instead the ”Generalized Estimating Equations ” (GEEs) to estimate the parameters of our regression model. The approach is illustrated using a representative sample from a German LTC portfolio.
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Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20080701
Source:
http://wwwm4.mathematik.tumuenchen.de/m4/Papers/Czado/paper393.pdf
http://wwwm4.mathematik.tumuenchen.de/m4/Papers/Czado/paper393.pdf
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Document Type:
text
Language:
en
Subjects:
Markovian MultiState Model ; Transition Probabilities ; AalenJohansen Estimator ; PseudoValues ; GLM ; GEE ; LTC ; Premium
Markovian MultiState Model ; Transition Probabilities ; AalenJohansen Estimator ; PseudoValues ; GLM ; GEE ; LTC ; Premium
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DDC:
310 Collections of general statistics
(computed)
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
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URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.68.3046
http://wwwm4.mathematik.tumuenchen.de/m4/Papers/Czado/paper393.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.68.3046
http://wwwm4.mathematik.tumuenchen.de/m4/Papers/Czado/paper393.pdf
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10.
Introducing and evaluating a Gibbs sampler for spatial Poisson regression models
Open Access
Title:
Introducing and evaluating a Gibbs sampler for spatial Poisson regression models
Author:
Gschlößl, Susanne
;
Czado, Claudia
Gschlößl, Susanne
;
Czado, Claudia
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Description:
In this paper we present a Gibbs sampler for a Poisson model including spatial effects. FrühwirthSchnatter and Wagner (2004b) show that by data augmentation via the introduction of two sequences of latent variables a Poisson regression model can be transformed into a normal linear model. We show how this methodology can be extended to spatial P...
In this paper we present a Gibbs sampler for a Poisson model including spatial effects. FrühwirthSchnatter and Wagner (2004b) show that by data augmentation via the introduction of two sequences of latent variables a Poisson regression model can be transformed into a normal linear model. We show how this methodology can be extended to spatial Poisson regression models and give details of the resulting Gibbs sampler. In particular, the influence of model parameterisation and different update strategies on the mixing of the MCMC chains are discussed. The developed Gibbs samplers are analysed in two simulation studies and applied to model the expected number of claims for policyholders of a German car insurance data set. In general, both large and small simulated spatial effects are estimated accurately by the Gibbs samplers and reasonable low autocorrelations are obtained when the data variability is rather large. However, for data with very low heterogeneity, the autocorrelations resulting from the Gibbs samplers are very high, withdrawing the computational advantage over a Metropolis Hastings independence sampler which exhibits very low autocorrelations in all settings.
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Publisher:
Techn. Univ.; Sonderforschungsbereich 386, Statistische Analyse Diskreter Strukturen München
Year of Publication:
2005
Document Type:
doctype:workingPaper
Language:
eng
Subjects:
ddc:310 ; spatial Poisson count data ; Gibbs sampler ; data augmentation ; model parameterisation ; block updates ; collapsing
ddc:310 ; spatial Poisson count data ; Gibbs sampler ; data augmentation ; model parameterisation ; block updates ; collapsing
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DDC:
310 Collections of general statistics
;
310 Collections of general statistics
(computed)
Rights:
http://www.econstor.eu/dspace/Nutzungsbedingungen
http://www.econstor.eu/dspace/Nutzungsbedingungen
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Discussion paper // Sonderforschungsbereich 386 der LudwigMaximiliansUniversität München 434
URL:
http://hdl.handle.net/10419/31012
http://hdl.handle.net/10419/31012
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Author
(17) Gschlößl, Susanne
(16) Czado, Claudia
(9) Claudia Czado
(9) Susanne Gschlößl
(8) The Pennsylvania State University CiteSeerX...
(1) Czado, Claudia (Prof. Ph.D.)
(1) Denuit, Michel
(1) Florian Helms
(1) Frank Schiller
(1) Frigessi, Arnoldo (Prof.)
(1) Gschlössl, Susanne
(1) Helms, F.
(1) Helms, Florian
(1) Julia Pfettner
(1) Schoenmaekers, Pascal
(1) Technische Universität München
(1) UCL  SSH/IMAQ/ISBA  Institut de Statistique,...
(1) Zentrum Mathematik
Author:
Subject
(9) ddc 510
(8) sonderforschungsbereich 386
(7) ddc 310
(4) block updates
(4) collapsing
(4) data augmentation
(4) gibbs sampler
(3) glm
(3) model parameterisation
(2) aalen johansen estimator
(2) bayes statistik
(2) deutschland
(2) gee
(2) infektionskrankheit
(2) key words
(2) ltc
(2) markovian multi state model
(2) premium
(2) pseudo values
(2) regression
(2) räumliche verteilung
(2) spatial poisson count data
(2) statistische verteilung
(2) transition probabilities
(2) zähldatenmodell
(1) bayes inferenz markov ketten monte carlo...
(1) bayesian inference
(1) bayesian inference markov chain monte carlo...
(1) compound poisson model
(1) count regression
(1) ddc 330
(1) gam
(1) inception selection
(1) long term care insurance
(1) model parameterisa
(1) multistate model
(1) non life insurance
(1) prediction
(1) probability integral transform
(1) proper
(1) proper scoring rules
(1) semiparametric hazard model
(1) spatial regression models
(1) survival analysis
Subject:
Dewey Decimal Classification (DDC)
(17) Statistics [31*]
(1) Economics [33*]
(1) Mathematics [51*]
Dewey Decimal Classification (DDC):
Year of Publication
(8) 2005
(4) 2012
(2) 2003
(2) 2004
(2) 2006
(2) 2008
(1) 2002
(1) 2007
(1) 2009
(1) 2010
(1) 2011
Year of Publication:
Content Provider
(8) CiteSeerX
(8) Munich LMU: Open Access
(7) EconStor
(2) RePEc.org
(1) Munich TU: mediaTUM
(1) Louvain Univ. Académie: DIAL
Content Provider:
Language
(17) English
(10) Unknown
Language:
Document Type
(15) Reports, Papers, Lectures
(8) Text
(3) Article, Journals
(1) Theses
Document Type:
Access
(16) Open Access
(11) Unknown
Access:
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