C. Czado

发表

Bayesian Inference for Semiparametric Binary Regression

M. Newton, C. Czado, R. Chappell, 1996 .

Pair-copula constructions of multiple dependence

A. Frigessi, C. Czado, K. Aas, 2009 .

Modeling Longitudinal Data Using a Pair-Copula Decomposition of Serial Dependence

C. Czado, M. Smith, A. Min, 2010 .

Nonparametric validation of similar distributions and assessment of goodness of fit

C. Czado, A. Munk, 1998 .

A nonparametric test for similarity of marginals—With applications to the assessment of population bioequivalence

C. Czado, A. Munk, G. Freitag, 2007 .

Sequential Bayesian Model Selection of Regular Vine Copulas

Lutz F. Gruber, C. Czado, 2015, 1512.00976.

Testing for zero-modification in count regression models.

Claudia Czado, Aleksey Min, C. Czado, 2010 .

Locating Multiple Interacting Quantitative Trait Loci with the Zero-Inflated Generalized Poisson Regression

Claudia Czado, Malgorzata Bogdan, M. Bogdan, 2010, Statistical applications in genetics and molecular biology.

Zero-inflated generalized Poisson models with regression effects on the mean, dispersion and zero-inflation level applied to patent outsourcing rates

C. Czado, A. Min, Stefan Wagner, 2007 .

Regime Switching Vine Copula Models for Global Equity and Volatility Indices

Claudia Czado, Holger Fink, C. Czado, 2016, 1604.05598.

Detecting regime switches in the dependence structure of high dimensional financial data

Claudia Czado, C. Czado, Jakob Stoeber, 2012, 1202.2009.

Dependence modeling for recurrent event times subject to right‐censoring with D‐vine copulas

P. Janssen, C. Czado, N. Barthel, 2017, Biometrics.

Flexible Dependence Modeling of Operational Risk Losses and Its Impact on Total Capital Requirements

C. Czado, S. Paterlini, E. Brechmann, 2013 .

Total loss estimation using copula-based regression models

C. Czado, Nicole Kraemer, E. Brechmann, 2012, 1209.5356.

Modeling dependent yearly claim totals including zero claims in private health insurance

C. Czado, Vinzenz Erhardt, 2012 .

A mixed copula model for insurance claims and claim sizes

C. Czado, A. Min, E. Brechmann, 2012 .

A method for approximately sampling high-dimensional count variables with prespecified Pearson correlation.

C. Czado, Vinzenz Erhardt, 2010 .

Sampling Count Variables with Specified Pearson Correlation: A Comparison Between a Naive and a C-Vine Sampling Approach

C. Czado, Vinzenz Erhardt, 2010 .

A mixed autoregressive probit model for ordinal longitudinal data.

C. Varin, C. Czado, 2010, Biostatistics.

A Mixed Probit Model for the Analysis of Pain Severity Diaries

C. Varin, C. Czado, 2008 .

Spatial modelling of claim frequency and claim size in non-life insurance

C. Czado, Susanne Gschlößl, 2007 .

Modelling count data with overdispersion and spatial effects

C. Czado, Susanne Gschlößl, 2008 .

Truncated regular vines in high dimensions with application to financial data

C. Czado, K. Aas, E. Brechmann, 2012 .

Application of survival analysis methods to long-term care insurance

C. Czado, F. Rudolph, 2002 .

Bankruptcy prediction in Norway: a comparison study

C. Czado, Daniel Berg, R. Dakovic, 2010 .

Evaluation of time series models under non-stationarity with application to the comparison of regional climate models

C. Czado, T. Thorarinsdottir, T. Erhardt, 2017, 1702.00728.

Stochastic volatility models for ordinal-valued time series with application to finance

Claudia Czado, Gernot Müller, C. Czado, 2009 .

Pair-Copula Constructions of Multivariate Copulas

Claudia Czado, C. Czado, 2010 .

Maximum likelihood estimation of mixed C-vines with application to exchange rates

Claudia Czado, Ulf Schepsmeier, Aleksey Min, 2012 .

Multivariate option pricing using copulae

C. Czado, C. Bernard, 2012 .

Bayesian inference for D-vines: estimation and model selection

C. Czado, A. Min, 2011 .

Pair Copula Constructions for Multivariate Discrete Data

Claudia Czado, Harry Joe, Anastasios Panagiotelis, 2012 .

Risk management with high-dimensional vine copulas: An analysis of the Euro Stoxx 50

Claudia Czado, C. Czado, E. Brechmann, 2013 .

Block-Maxima of Vines

Claudia Czado, Matthias Killiches, C. Czado, 2015, 1504.02887.

Using model distances to investigate the simplifying assumption, model selection and truncation levels for vine copulas

Claudia Czado, Matthias Killiches, C. Czado, 2016, 1610.08795.

Examination and visualisation of the simplifying assumption for vine copulas in three dimensions

Claudia Czado, Matthias Killiches, C. Czado, 2016, 1602.05795.

Growing simplified vine copula trees: improving Di{\ss}mann's algorithm

Claudia Czado, C. Czado, Daniel Kraus, 2017, 1703.05203.

A Bayesian Non-linear State Space Copula Model to Predict Air Pollution in Beijing

C. Czado, A. Kreuzer, L. D. Valle, 2019, 1903.08421.

Bayesian multivariate nonlinear state space copula models

C. Czado, A. Kreuzer, L. D. Valle, 2019, Comput. Stat. Data Anal..

A Bayesian linear model for the high-dimensional inverse problem of seismic tomography

Claudia Czado, Karin Sigloch, Ran Zhang, 2013, 1312.2686.

Choosing the link function and accounting for link uncertainty in generalized linear models using Bayes factors

Adrian E. Raftery, Claudia Czado, A. Raftery, 2006 .

Modeling Transport Mode Decisions Using Hierarchical Binary Spatial Regression Models with Cluster Effects

Claudia Czado, C. Czado, S. Prokopenko, 2004 .

Multivariate regression analysis of panel data with binary outcomes applied to unemployment data

C. Czado, 2000 .

Assessing the similarity of distributions - finite sample performance of the empirical mallows distance

Claudia Czado, Axel Munk, C. Czado, 1998 .

Validating linear restrictions in linear regression models with general error structure

C. Czado, A. Min, H. Holzmann, 2006 .

Selection of Vine Copulas

Eike Christian Brechmann, Claudia Czado, Lutz F. Gruber, 2013 .

Bayesian Inference for Pair-copula Constructions of Multiple Dependence

C. Czado, A. Min, 2007 .

Modeling migraine severity with autoregressive ordered probit models

C. Czado, Gernot Müller, A. Heyn, 2005 .

Bayesian Inference for Latent Factor Copulas and Application to Financial Risk Forecasting

Lutz F. Gruber, C. Czado, B. Schamberger, 2017 .

Chapter 2 Selection of Vine Copulas

Lutz F. Gruber, C. Czado, E. Brechmann, 2013 .

Introducing and evaluating a Gibbs sampler for spatial Poisson regression models

C. Czado, Susanne Gschlößl, 2005 .

Modeling high dimensional time-varying dependence using D-vine SCAR models

C. Czado, Carlos Almeida, H. Manner, 2012, 1202.2008.

Modelling temporal dependence of realized variances with vines

C. Czado, Y. Okhrin, E. Ivanov, 2019, Econometrics and Statistics.

Vine copula based post-processing of ensemble forecasts for temperature

Claudia Czado, Annette Moller, Ludovica Spazzini, 2018, 1811.02255.

Spatial modelling of claim frequency and claim size in insurance

C. Czado, Susanne Gschlößl, 2005 .

Flexible dynamic vine copula models for multivariate time series data

Elif F. Acar, C. Czado, Martin Lysy, 2019, Econometrics and Statistics.

Stress Testing German Industry Sectors: Results from a Vine Copula Based Quantile Regression

C. Czado, M. Fischer, Daniel Kraus, 2017, 1704.00953.

Extending the CAPM using pair copulas: The Regular Vine Market Sector model

C. Czado, E. Brechmann, 2011 .

Analyzing Dependent Data with Vine Copulas

C. Czado, 2019, Lecture Notes in Statistics.

An Application of D-vine Regression for the Identification of Risky Flights in Runway Overrun

C. Czado, H. Alnasser, 2022, 2205.04591.

Bivariate vine copula based quantile regression

C. Czado, Marija Tepegjozova, 2022 .

ESGM: ESG scores and the Missing pillar

C. Czado, S. Paterlini, Özge Sahin, 2021, SSRN Electronic Journal.

A fractionally integrated ECOGARCH process

C. Czado, S. Haug, 2006 .

Theoretical Foundations of Autoregressive Models for Time Series on Acyclic Directed Graphs

C. Czado, Ralph Högn, 2003 .

Noncanonical Links in Generalized Linear Models When is the E(cid:11)ort Justi(cid:12)ed?

C. Czado, A. Munk, 2012 .

Reproducing Kernel Hilbert Space for Some Non-Gaussian Processes

Claudia Czado, Murad S. Taqqu, M. Taqqu, 1985 .

High-dimensional sparse vine copula regression with application to genomic prediction

C. Czado, Ozge Sahin, 2022, 2208.12383.

Vine Copula Based Portfolio Level Conditional Risk Measure Forecasting

C. Czado, K. Bax, Emanuel Sommer, 2022, SSRN Electronic Journal.

Bayesian inference for multivariate copulas using pair-copula constructions.

C. Czado, A. Min, 2010 .

Statistical Dependence Analyses of Operational Flight Data Used for Landing Reconstruction Enhancement

F. Holzapfel, C. Czado, T. Nagler, 2022, 2206.09809.

Bayesian model selection for D‐vine pair‐copula constructions

C. Czado, A. Min, 2011 .

Predictive Model Assessment for Count Data

Claudia Czado, Leonhard Held, Tilmann Gneiting, 2009, Biometrics.

Heavy tailed spatial autocorrelation models

C. Czado, A. Kreuzer, T. Erhardt, 2017, 1707.03165.

Using model distances to investigate the simplifying assumption, goodness-of-fit and truncation levels for vine copulas

Claudia Czado, Matthias Killiches, C. Czado, 2016 .

Conditional copula simulation for systemic risk stress testing

Eike Christian Brechmann, Claudia Czado, C. Czado, 2013 .

On the Observability of Gaussian Models using Discrete Density Approximations

C. Czado, Ariane Hanebeck, 2022, 2022 25th International Conference on Information Fusion (FUSION).

Two‐part D‐vine copula models for longitudinal insurance claim data

C. Czado, Lu Yang, 2022, Scandinavian Journal of Statistics.

Vine Copula Based Dependence Modeling in Sustainable Finance

C. Czado, A. Min, S. Paterlini, 2022, SSRN Electronic Journal.

Non nested model selection for spatial count regression models with application to health insurance

C. Czado, Vinzenz Erhardt, Holger Schabenberger, 2013, Statistical Papers.

The pitfalls of (non-definitive) Environmental, Social, and Governance scoring methodology

C. Czado, S. Paterlini, Özge Sahin, 2022, SSRN Electronic Journal.

On selecting parametric link transformation families in generalized linear models

Claudia Czado, C. Czado, 1997 .

Norm restricted maximum likelihood estimators for binary regression models with parametric link

C. Czado, 1993 .

The effect of link misspecification on binary regression inference

Claudia Czado, Thomas J. Santner, T. Santner, 1992 .

Orthogonalizing parametric link transformation families in binary regression analysis

T. Santner, C. Czado, 1992 .

Bayesian spatial modelling for high dimensional seismic inverse problems

Claudia Czado, Karin Sigloch, Ran Zhang, 2016 .

Vine copula mixture models and clustering for non-Gaussian data

Claudia Czado, Özge Sahin, C. Czado, 2021, Econometrics and Statistics.

Mixed effect models for absolute log returns of ultra high frequency data

C. Czado, S. Haug, 2006 .

Nonparametric C- and D-vine-based quantile regression

Claudia Czado, Gerda Claeskens, Marija Tepegjozova, 2021, Dependence Modeling.

Conditional quantiles and tail dependence

Claudia Czado, Carole Bernard, C. Czado, 2015, J. Multivar. Anal..

D-vine copula based quantile regression

Claudia Czado, Daniel Kraus, C. Czado, 2015, Comput. Stat. Data Anal..

State space mixed models for longitudinal observations with binary and binomial responses

C. Czado, P. X. Song, P. Song, 2008 .

An Autoregressive Ordered Probit Model With Application to High-Frequency Financial Data

Claudia Czado, Gernot Müller, C. Czado, 2005 .

Representing Sparse Gaussian DAGs as Sparse R-Vines Allowing for Non-Gaussian Dependence

Claudia Czado, Dominik Müller, C. Czado, 2016, 1604.04202.

Evading the curse of dimensionality in nonparametric density estimation with simplified vine copulas

Claudia Czado, Thomas Nagler, C. Czado, 2015, J. Multivar. Anal..

Nonparametric estimation of simplified vine copula models: comparison of methods

Claudia Czado, Christian Schellhase, Thomas Nagler, 2017, 1701.00845.

Bayesian Risk Analysis

Eike Christian Brechmann, Claudia Czado, C. Czado, 2014 .

Individual Migraine Risk Management using Binary State Space Mixed Models

C. Czado, 2001 .

Modeling individual migraine severity with autoregressive ordered probit models

Claudia Czado, Anette Heyn, Gernot Müller, 2011, Stat. Methods Appl..

Simplified pair copula constructions - Limitations and extensions

Claudia Czado, Harry Joe, Jakob Stöber, 2013, J. Multivar. Anal..

Calculation of LTC Premiums Based on Direct Estimates of Transition Probabilities

C. Czado, Susanne Gschlößl, F. Helms, 2005, ASTIN Bulletin.

Bayesian Poisson log-bilinear mortality projections

Claudia Czado, Michel Denuit, C. Czado, 2005 .

Environmental, Social, Governance scores and the Missing pillar—Why does missing information matter?

C. Czado, S. Paterlini, Özge Sahin, 2021, Corporate Social Responsibility and Environmental Management.

ESG, Risk, and (Tail) Dependence

C. Czado, S. Paterlini, Özge Sahin, 2021, SSRN Electronic Journal.

On Link Selection in Generalized Linear Models

Claudia Czado, C. Czado, 1992 .

University of WisconsinDepartment of Statistics Bayesian inference for semiparametric binary regression

C. Czado, R. Chappell, 1996 .

SCOMDY models based on pair-copula constructions with application to exchange rates

Claudia Czado, Aleksey Min, C. Czado, 2014, Comput. Stat. Data Anal..

Model selection strategies for identifying most relevant covariates in homoscedastic linear models

Hajo Holzmann, Claudia Czado, Aleksey Min, 2010, Comput. Stat. Data Anal..

Selecting and estimating regular vine copulae and application to financial returns

Claudia Czado, Dorota Kurowicka, J. Dißmann, 2012, Comput. Stat. Data Anal..

Analysis of Australian electricity loads using joint Bayesian inference of D-Vines with autoregressive margins

Claudia Czado, F. Gaertner, Aleksey Min, 2011 .

Efficient Bayesian inference for stochastic time-varying copula models

Claudia Czado, Carlos Almeida, C. Czado, 2012, Comput. Stat. Data Anal..

Computational Statistics and Data Analysis Regime Switches in the Dependence Structure of Multidimensional Financial Data

Claudia Czado, Jakob Stöber, C. Czado, 2022 .

Vine Copula Based Modeling

C. Czado, T. Nagler, 2021, Annual Review of Statistics and Its Application.

Standardized drought indices: A novel uni- and multivariate approach

Claudia Czado, Tobias M. Erhardt, C. Czado, 2015, 1508.06476.

Modelling transport mode decisions using hierarchical logistic regression models with spatial and cluster effects

Claudia Czado, Sergij Prokopenko, C. Czado, 2008 .

An Exponential Continuous-Time GARCH Process

C. Czado, S. Haug, 2007, Journal of Applied Probability.

Haug , Czado : An exponential continuous time GARCH process

C. Czado, S. Haug, 2008 .

Czado , Haug : A fractionally integrated ECOGARCH process

C. Czado, 2007 .

A partial correlation vine based approach for modeling and forecasting multivariate volatility time-series

Claudia Czado, Yarema Okhrin, Nicole Barthel, 2018, Comput. Stat. Data Anal..

Quasi maximum likelihood estimation and prediction in the compound Poisson ECOGARCH(1,1) model

C. Czado, S. Haug, 2006 .

Comorbidity of chronic diseases in the elderly: Patterns identified by a copula design for mixed responses

Claudia Czado, Pulak Ghosh, Jakob Stöber, 2015, Comput. Stat. Data Anal..

A Bayesian non‐linear state space copula model for air pollution in Beijing

C. Czado, A. Kreuzer, L. Dalla Valle, 2022, Journal of the Royal Statistical Society: Series C (Applied Statistics).

Spatial composite likelihood inference using local C-vines

Claudia Czado, Ulf Schepsmeier, Tobias Michael Erhardt, 2014, J. Multivar. Anal..

Non nested model selection for spatial count regression models with application to health insurance

C. Czado, Vinzenz Erhardt, Holger Schabenberger, 2014 .

Analyzing Dependent Data with Vine Copulas

Claudia Czado, P. Gather U. Diggle, S. Zeger, 2019, Lecture Notes in Statistics.

Model-based quantification of the volatility of options at transaction level with extended count regression models

C. Czado, A. Kolbe, 2007 .

Empirical Study of Intraday Option Price Changes using extended Count Regression Models

C. Czado, A. Kolbe, 2004 .

A D‐vine copula‐based model for repeated measurements extending linear mixed models with homogeneous correlation structure

Claudia Czado, Matthias Killiches, C. Czado, 2017, Biometrics.

Noncanonical links in generalized linear models – when is the effort justified?

Claudia Czado, Axel Munk, C. Czado, 2000 .

Comparing point and interval estimates in the bivariate t-copula model with application to financial data

C. Czado, R. Dakovic, 2011 .

Statistical Assessments of Systemic Risk Measures

Claudia Czado, Carole Bernard, C. Czado, 2012 .

Analysis of an interventional protein experiment using a vine copula based structural equation model

Claudia Czado, Sebastian Scharl, C. Czado, 2021, 2111.10113.

Pair-Copula Bayesian Networks

Claudia Czado, Alexander Bauer, C. Czado, 2012, 1211.5620.

Bivariate vine copula based regression, bivariate level and quantile curves

C. Czado, Marija Tepegjozova, 2022, 2205.02557.

Vine copula-based Bayesian classification for multivariate time series of electroencephalography eye states

C. Czado, Chunfang Zhang, 2023, Journal of the Royal Statistical Society Series C: Applied Statistics.

High-dimensional sparse vine copula regression with application to genomic prediction.

C. Czado, Ozge Sahin, Özge Sahin, 2022, Biometrics.

Vine Copula Based Portfolio Level Conditional Risk Measure Forecasting

C. Czado, K. Bax, Emanuel Sommer, 2022, SSRN Electronic Journal.

Standardized drought indices: a novel univariate and multivariate approach

Claudia Czado, Tobias Michael Erhardt, C. Czado, 2018 .

Selection of sparse vine copulas in high dimensions with the Lasso

C. Czado, D. Müller, 2018, Statistics and Computing.

Model distances for vine copulas in high dimensions

C. Czado, Daniel Kraus, Matthias Killiches, 2017, Statistics and computing.

Modeling high-dimensional time-varying dependence using dynamic D-vine models

Claudia Czado, Hans Manner, C. Czado, 2016 .

Dependence modelling with regular vine copula models: a case‐study for car crash simulation data

Claudia Czado, Ulf Schepsmeier, C. Czado, 2016 .

Model distances for vine copulas in high dimensions

Claudia Czado, Daniel Kraus, Matthias Killiches, 2015, Stat. Comput..

Preface to special issue on high-dimensional dependence and copulas

Jun Cai, Claudia Czado, Harry Joe, 2015, J. Multivar. Anal..

Multiresolution Analysis of Long Time Series with Applications to Finance

Claudia Czado, Ralph Högn, C. Czado, 2005 .

Bayesian inference for a single factor copula stochastic volatility model using Hamiltonian Monte Carlo

C. Czado, A. Kreuzer, 2018, Econometrics and Statistics.

Predictive Model Assessment for Count Data

L. Held, T. Gneiting, C. Czado, 2009, Biometrics.

Locating Multiple Interacting Quantitative Trait Loci with the Zero-inflated Generalized Poisson Regression

C. Czado, Vinzenz Erhardt, Małgorzata Bogdan, 2022 .

Locating Multiple Interacting Quantitative Trait Loci with the Zero-inflated Generalized Poisson Regression

C. Czado, Vinzenz Erhardt, Małgorzata Bogdan, 2022 .

Selection strategies for regular vine copulae

Claudia Czado, Stephan Jeske, Mathias W. Hofmann, 2013 .

Zero-inflated generalized Poisson regression models : Asymptotic theory and applications

C. Czado, A. Min, 2005 .

A vine-copula based adaptive MCMC sampler for efficient inference of dynamical systems

Fabian J Theis, C. Czado, S. Hug, 2013 .

Bootstrap methods for the nonparametric assessment of population bioequivalence and similarity of distributions

C. Czado, A. Munk, 2001 .

Ordinal stochastic volatility and stochastic volatility models for price changes: An empirical comparison.

C. Czado, Thi-Ngoc-Giau Nguyen, Gernot Müller, 2010 .

Model selection for discrete regular vine copulas

Claudia Czado, Harry Joe, Anastasios Panagiotelis, 2017, Comput. Stat. Data Anal..

Application of survival analysis methods to long-term care insurance

C. Czado, Florian Rudolph, 2002 .

Bayesian total loss estimation using shared random effects

Lutz F. Gruber, C. Czado, Carolin Baumgartner, 2015 .

Consistency and asymptotic normality of the maximum likelihood estimator in a zero-inflated generalized Poisson regression

Claudia Czado, Aleksey Min, C. Czado, 2005 .

R‐vine models for spatial time series with an application to daily mean temperature

Claudia Czado, Ulf Schepsmeier, Tobias Michael Erhardt, 2014, Biometrics.

Dependent censoring based on copulas

Claudia Czado, Ingrid Van Keilegom, I. Keilegom, 2021, 2104.06872.

Dependence modelling in ultra high dimensions with vine copulas and the Graphical Lasso

Claudia Czado, Dominik Müller, C. Czado, 2017, Comput. Stat. Data Anal..

Efficient Bayesian Inference for Nonlinear State Space Models With Univariate Autoregressive State Equation

C. Czado, A. Kreuzer, 2019, Journal of Computational and Graphical Statistics.

Validating linear restrictions in linear regression models with general error structure

C. Czado, A. Min, H. Holzmann, 2006 .

A Survey of Functional Laws of the Iterated Logarithm for Self-Similar Processes

Claudia Czado, Murad S. Taqqu, M. Taqqu, 1985 .

Assessing univariate and bivariate risks of late-frost and drought using vine copulas: A historical study for Bavaria

C. Czado, A. Rammig, Marija Tepegjozova, 2023, 2310.10324.

Multivariate Probit Analysis of Binary Time Series Data with Missing Responses

C. Czado, 1996 .

Ordinal- and Continuous-Response Stochastic Volatility Models for Price Changes: An Empirical Comparison

C. Czado, Gernot Müller, T. Nguyen, 2010 .

Linear Mixed Models – A Practical Guide Using Statistical Software. B. T. West, K. B. Welch and A. T. Galecki (2006). London: Chapman & Hall/CRC. ISBN: 978-1-584-88480-4

C. Czado, 2009 .

Spatial R-vine copula for streamflow scenario simulation

Claudia Czado, Guilherme Pereira, Alvaro Veiga, 2016, 2016 Power Systems Computation Conference (PSCC).