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Publications by Harry Joe

2020

Cooke RM, Joe H, Chang B. Vine copula regression for observational studies. ASTA-Advances in Statistical Analysis. 2020; 104: 141-167. DOI: 10.1007/s10182-019-00353-5
Chang B, Joe H. Copula diagnostics for asymmetries and conditional dependence. Journal of Applied Statistics. 2020; 47: 1587-1615. DOI: 10.1080/02664763.2019.1685080, Early Access Date = NOV 2019
Krupskii P, Joe H. Flexible copula models with dynamic dependence and application to financial data. Econometrics and Statistics. 2020; 16: 148-167. DOI: 10.1016/j.ecosta.2020.01.005

2019

Joe H. Likelihood Inference for Generalized Integer Autoregressive Time Series Models. EconometricS. 2019; 7: 43. DOI: 10.3390/econometrics7040043
Chang B, Joe H. Prediction based on conditional distributions of vine copulas. Computational Statistics & Data Analysis. 2019; 139: 45-63. DOI: 10.1016/j.csda.2019.04.015
Fernandez-Fontelo A, Cabana A, Joe H, Puig P, Morina D. Untangling serially dependent underreported count data for gender-based violence. Statistics in Medicine. 2019; 38: 4404-4422. DOI: 10.1002/sim.8306, Early Access Date = JUL 2019
Krupskii P, Joe H. Nonparametric estimation of multivariate tail probabilities and tail dependence coefficients. Journal of Multivariate Analysis. Canadian Stat Sci Inst; 2019; 172: 147-161. DOI: 10.1016/j.jmva.2019.02.013
Joe H, Li H. Tail densities of skew-elliptical distributions. Journal of Multivariate Analysis. 2019; 171: 421-435. DOI: 10.1016/j.jmva.2019.01.009
Chang B, Pan S, Joe H. Vine copula structure learning via Monte Carlo tree search. In: Chaudhuri K, Sugiyama M. 22ND International Conference on Artificial Intelligence and Statistics, Vol 89. 2019. pp. 353-361.
Hadley D, Joe H, Nolde N. On the selection of loss severity distributions to model operational risk. Journal of Operational Risk. 2019; 14: 73-94. DOI: 10.21314/JOP.2019.229

2018

Joe H. Dependence properties of conditional distributions of some copula models. Methodology and Computing in Applied Probability. 2018; 20: 975-1001. DOI: 10.1007/s11009-017-9544-9 ISSN = 1387-5841
Lee D, Joe H. Multivariate extreme value copulas with factor and tree dependence structures. Extremes. 2018; 21: 147-176. DOI: 10.1007/s10687-017-0298-0 ISSN = 1386-1999
Lee D, Joe H. Efficient computation of multivariate empirical distribution functions at the observed values. Computational Statistics. 2018; 33: 1413-1428. DOI: 10.1007/s00180-017-0771-x ISSN = 0943-4062
Krupskii P, Joe H, Lee D, Genton M. Extreme-value limit of the convolution of exponential and multivariate normal distributions: Link to the Huesler-Reiss distribution. Journal of Multivariate Analysis. 2018; 163: 80-95. DOI: 10.1016/j.jmva.2017.10.006
Lee D, Joe H, Krupskii P. Tail-weighted dependence measures with limit being the tail dependence coefficient. Journal of Nonparametric Statistics. 2018; 30: 262-290. DOI: 10.1080/10485252.2017.1407414
Joe H. Parsimonious graphical dependence models constructed from vines. Canadian Journal of Statistics. 2018; 46: 532-555. DOI: 10.1002/cjs.11481

2017

Panagiotelis A, Czado C, Joe H, Stoeber J. Model selection for discrete regular vine copulas. COMPUTATIONAL STATISTICS & DATA ANALYSIS. 2017; 106: 138-152. DOI: 10.1016/j.csda.2016.09.007
Hua L, Joe H. Multivariate dependence modeling based on comonotonic factors. Journal of Multivariate Analysis. 2017; 155: 317-333. DOI: 10.1016/j.jmva.2017.01.008
Joe H. Parametric copula families for statistical models. In: Ubeda-Flores M, de Amo-Artero E, Durante F, Fernandez-Sanchez J. Copulas and Dependence Models with Applications: Contributions in Honor of Roger B. Nelsen [Internet]. Berlin: Springer; 2017. pp. 119–134. URL: https://link.springer.com/book/10.1007/978-3-319-64221-5

2016

Joe H, Sang P. Multivariate models for dependent clusters of variables with conditional independence given aggregation variables. Computational Statistics & Data Analysis. 2016; 97: 114-132. DOI: 10.1016/j.csda.2015.12.001
Ng CT, Joe H. Comparison of non-nested models under a general measure of distance. Journal of Statistical Planning and Inference. Elsevier Science BV; 2016; 170: 166-185. DOI: 10.1016/j.jspi.2015.10.004

2015

Krupskii P, Joe H. Tail-weighted measures of dependence. Journal of Applied Statistics. Taylor & Francis Ltd; 2015; 42: 614-629. DOI: 10.1080/02664763.2014.980787
Nikoloulopoulos AK, Joe H. Factor copula models for item response data. Psychometrika. Springer; 2015; 80: 126-150. DOI: 10.1007/s11336-013-9387-4
Joe H. Markov count time series models with covariates. In: Davis RA, Holan SH, Lund RB, Ravishanker N. Handbook of Discrete-Valued Time Series [Internet]. Boca Raton, FL: Chapman & Hall/CRC; 2015. pp. 29–49. URL: http://www.crcpress.com/product/isbn/9781466577732
Hexter A, Jones A, Joe H, Heap L, Smith MJ, Wallace AJ, et al. Clinical and molecular predictors of mortality in neurofibromatosis 2: a UK national analysis of 1192 patients. Journal of Medical Genetics. BMJ Publishing Group; 2015; 52: 699-705. DOI: 10.1136/jmedgenet-2015-103290
Joe H, Cai J, Czado C, Li H. Preface to special issue on high-dimensional dependence and copulas. Journal of Multivariate Analysis. Elsevier Inc; 2015; 138: 1-3. DOI: 10.1016/j.jmva.2015.03.002
Brechmann EC, Joe H. Truncation of vine copulas using fit indices. Journal of Multivariate Analysis. Elsevier Inc; 2015; 138: 19-33. DOI: 10.1016/j.jmva.2015.02.012
Krupskii P, Joe H. Structured factor copula models: Theory, inference and computation. Journal of Multivariate Analysis. Elsevier Inc; 2015; 138: 53-73. DOI: 10.1016/j.jmva.2014.11.002

2014

Ng CT, Joe H. Model comparison with composite likelihood information criteria. Bernoulli. Int Statistical Inst; 2014; 20: 1738-1764. DOI: 10.3150/13-BEJ539
Brechmann EC, Joe H. Parsimonious parameterization of correlation matrices using truncated vines and factor analysis. Computational Statistics & Data Analysis. Elsevier Science BV; 2014; 77: 233-251. DOI: 10.1016/j.csda.2014.03.002
Joe H. Dependence Modeling with Copulas [Internet]. Boca Raton, FL: Chapman & Hall/CRC; 2014. URL: http://www.crcpress.com/product/isbn/9781466583221
Hua L, Joe H, Li H. Relations between hidden regular variation and the tail order of copulas. Journal of Applied Probability. Applied Probability Trust; 2014; 51: 37-57. DOI: 10.1017/S0021900200010068
Maydeu-Olivares A, Joe H. Assessing approximate fit in categorical data analysis. Multivariate Behavioral Research. Routledge Journals, Taylor & Francis Ltd; 2014; 49: 305-328. DOI: 10.1080/00273171.2014.911075
Hua L, Joe H. Strength of tail dependence based on conditional tail expectation. Journal of Multivariate Analysis. Elsevier Inc; 2014; 123: 143-159. DOI: 10.1016/j.jmva.2013.09.001

2013

Nolde N, Joe H. A Bayesian extreme value analysis of debris flows. Water Resources Research. Amer Geophysical Union; 2013; 49: 7009-7022. DOI: 10.1002/wrcr.20494
Krupskii P, Joe H. Factor copula models for multivariate data. Journal of Multivariate Analysis. Elsevier Inc; 2013; 120: 85-101. DOI: 10.1016/j.jmva.2013.05.001
Rosco JF, Joe H. Measures of tail asymmetry for bivariate copulas. Statistical Papers. Springer; 2013; 54: 709-726. DOI: 10.1007/s00362-012-0457-y
Stoeber J, Joe H, Czado C. Simplified pair copula constructions: Limitations and extensions. Journal of Multivariate Analysis. Elsevier Inc; 2013; 119: 101-118. DOI: 10.1016/j.jmva.2013.04.014
Hua L, Joe H. Intermediate tail dependence: a review and some new results. In: Li H, Li X. Stochastic Orders in Reliability and Risk. New York: Springer; 2013. pp. 291-311. DOI: 10.1007/978-1-4614-6892-9_15

2012

Nikoloulopoulos AK, Joe H, Li H. Vine copulas with asymmetric tail dependence and applications to financial return data. Computational Statistics & Data Analysis. Elsevier Science BV; 2012; 56: 3659-3673. DOI: 10.1016/j.csda.2010.07.016
Panagiotelis A, Czado C, Joe H. Pair copula constructions for multivariate discrete data. Journal of the American Statistical Association. Amer Statistical Assoc; 2012; 107: 1063-1072. DOI: 10.1080/01621459.2012.682850
Hua L, Joe H. Tail comonotonicity: Properties, constructions, and asymptotic additivity of risk measures. Insurance Mathematics & Economics. Elsevier Science BV; 2012; 51: 492-503. DOI: 10.1016/j.insmatheco.2012.07.006
Joe H. Book Review of ``Inequalities: Theory of Majorization and Its Applications, by AW Marshall, I. Olkin and BC Arnold, Springer". Probability in the Engineering and Informational Sciences. Cambridge University Press; 2012; 26: 449–453. DOI: 10.1017/S0269964812000113
Joe H, Seshadri V, Arnold BC. Multivariate inverse Gaussian and skew-normal densities. Statistics & Probability Letters. Elsevier Science BV; 2012; 82: 2244-2251. DOI: 10.1016/j.spl.2012.08.004
Hua L, Joe H. Tail comonotonicity and conservative risk measures. ASTIN Bulletin. Peeters; 2012; 42: 601-629. DOI: 10.2143/AST42.2.2182810

2011

Cooke RM, Kousky C, Joe H. Micro correlations and tail dependence. In: Kurowicka D, Joe H. Dependence Modeling: Vine Copula Handbook. Singapore: World Scientific; 2011. pp. 89–112. DOI: 10.1142/9789814299886_0005
Joe H. Dependence comparisons of vine copulae in four or more variables. In: Kurowicka D, Joe H. Dependence Modeling: Vine Copula Handbook. Singapore: World Scientific; 2011. pp. 139–164. DOI: 10.1142/9789814299886_0007
Joe H. Tail dependence in vine copulae. In: Kurowicka D, Joe H. Dependence Modeling: Vine Copula Handbook. Singapore: World Scientific; 2011. pp. 165–187. DOI: 10.1142/9789814299886_0008
Joe H, Li H. Tail risk of multivariate regular variation. Methodology and Computing in Applied Probability. Springer; 2011; 13: 671-693. DOI: 10.1007/s11009-010-9183-x
Joe H, Cooke RM, Kurowicka D. Regular vines: generation algorithm and number of equivalence classes. In: Kurowicka D, Joe H. Dependence Modeling: Vine Copula Handbook. Singapore: World Scientific; 2011. pp. 219–231. DOI: 10.1142/9789814299886_0010

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