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Publications by Harry Joe

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.
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.

2015

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. 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.
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.
Brechmann EC, Joe H. Truncation of vine copulas using fit indices. Journal of Multivariate Analysis. Elsevier Inc; 2015;138:19-33.
Krupskii P, Joe H. Structured factor copula models: Theory, inference and computation. Journal of Multivariate Analysis. Elsevier Inc; 2015;138:53-73.
Krupskii P, Joe H. Tail-weighted measures of dependence. Journal of Applied Statistics. Taylor & Francis Ltd; 2015;42:614-629.
Nikoloulopoulos AK, Joe H. Factor copula models for item response data. Psychometrika. Springer; 2015;80:126-150.

2014

Ng CT, Joe H. Model comparison with composite likelihood information criteria. Bernoulli. Int Statistical Inst; 2014;20:1738-1764.
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.
Joe H. Dependence Modeling with Copulas [Internet]. Boca Raton, FL: Chapman & Hall/CRC; 2014. 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.
Maydeu-Olivares A, Joe H. Assessing approximate fit in categorical data analysis. Multivariate Behavioral Research. Routledge Journals, Taylor & Francis Ltd; 2014;49:305-328.
Hua L, Joe H. Strength of tail dependence based on conditional tail expectation. Journal of Multivariate Analysis. Elsevier Inc; 2014;123:143-159.

2013

Nolde N, Joe H. A Bayesian extreme value analysis of debris flows. Water Resources Research. Amer Geophysical Union; 2013;49:7009-7022.
Krupskii P, Joe H. Factor copula models for multivariate data. Journal of Multivariate Analysis. Elsevier Inc; 2013;120:85-101.
Rosco JF, Joe H. Measures of tail asymmetry for bivariate copulas. Statistical Papers. Springer; 2013;54:709-726.
Stoeber J, Joe H, Czado C. Simplified pair copula constructions: Limitations and extensions. Journal of Multivariate Analysis. Elsevier Inc; 2013;119:101-118.
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.

2012

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.
Joe H, Seshadri V, Arnold BC. Multivariate inverse Gaussian and skew-normal densities. Statistics & Probability Letters. Elsevier Science BV; 2012;82:2244-2251.
Hua L, Joe H. Tail comonotonicity and conservative risk measures. ASTIN Bulletin. Peeters; 2012;42:601-629.
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.
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.
Hua L, Joe H. Tail comonotonicity: Properties, constructions, and asymptotic additivity of risk measures. Insurance Mathematics & Economics. Elsevier Science BV; 2012;51:492-503.

2011

El-Shaarawi AH, Zhu R, Joe H. Modelling species abundance using the Poisson-Tweedie family. Environmetrics. Wiley-Blackwell; 2011;22:152-164.
Ng CT, Joe H, Karlis D, Liu J. Composite likelihood for time series models with a latent autoregressive process. Statistica Sinica [Internet]. {Statistica Sinica, TAIWAN; 2011;21:279-305. http://www3.stat.sinica.edu.tw/statistica/j21n1/J21N112/J21N112.html
Kurowicka D, Joe H. Dependence Modeling: Vine Copula Handbook [Internet]. Singapore: World Scientific; 2011. http://www.worldscibooks.com/economics/7699.html
Cooke RM, Joe H, Aas K. Vines arise. In: Kurowicka D, Joe H. Dependence Modeling: Vine Copula Handbook. Singapore: World Scientific Publishing Company; 2011. pp. 37–71.
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.
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.
Joe H. Tail dependence in vine copulae. In: Kurowicka D, Joe H. Dependence Modeling: Vine Copula Handbook. Singapore: World Scientific; 2011. pp. 165–187.
Joe H, Li H. Tail risk of multivariate regular variation. Methodology and Computing in Applied Probability. Springer; 2011;13:671-693.
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.
Hua L, Joe H. Second order regular variation and conditional tail expectation of multiple risks. Insurance Mathematics & Economics. Elsevier Science BV; 2011;49:537-546.
Hua L, Joe H. Tail order and intermediate tail dependence of multivariate copulas. Journal of Multivariate Analysis. Elsevier Inc; 2011;102:1454-1471.
Nikoloulopoulos AK, Joe H, Chaganty NR. Weighted scores method for regression models with dependent data. Biostatistics. Oxford Univ Press; 2011;12:653-665.
Baser ME, Friedman JM, Joe H, Shenton A, Wallace AJ, Ramsden RT, et al. Empirical development of improved diagnostic criteria for neurofibromatosis 2. Genetics in Medicine. Nature Publishing Group; 2011;13:576-581.

2010

Joe H, Maydeu-Olivares A. A general family of limited information goodness-of-fit statistics for multinomial data [Internet]. Kurowicka D, Joe H. Dependence Modeling: Vine Copula Handbook. Singapore: Springer; 2010. pp. 393-419. {http://www.worldscibooks.com/economics/7699.html doi = 10.1142/9789814299886, @InCollectionCooke.Joe.ea2011
Ng CT, Joe H. Generating random AR(p) and MA(q) Toeplitz correlation matrices. Journal of Multivariate Analysis. Elsevier Inc; 2010;101:1532-1545.
Zhu R, Joe H. Negative binomial time series models based on expectation thinning operators. Journal of Statistical Planning and Inference. Elsevier Science BV; 2010;140:1874-1888.
Joe H, Li H, Nikoloulopoulos AK. Tail dependence functions and vine copulas. Journal of Multivariate Analysis. Elsevier Inc; 2010;101:252-270.
Zhu R, Joe H. Count data time series models based on expectation thinning. Stochastic Models. Taylor & Francis Inc; 2010;26:PII 925211404.

2009

Lewandowski D, Kurowicka D, Joe H. Generating random correlation matrices based on vines and extended onion method. Journal of Multivariate Analysis. Elsevier Inc; 2009;100:1989-2001.
Zhu R, Joe H. Modelling heavy-tailed count data using a generalised Poisson-inverse Gaussian family. Statistics & Probability Letters. Elsevier Science BV; 2009;79:1695-1703.
Nikoloulopoulos AK, Joe H, Li H. Extreme value properties of multivariate t copulas. Extremes. Springer; 2009;12:129-148.
Joe H, Lee Y. On weighting of bivariate margins in pairwise likelihood. Journal of Multivariate Analysis. Elsevier Inc; 2009;100:670-685.
Willems G, Joe H, Zamar R. Diagnosing multivariate outliers detected by robust estimators. Journal of Computational and Graphical Statistics. Amer Statistical Assoc; 2009;18:73-91.

2008

Joe H. Accuracy of Laplace approximation for discrete response mixed models. Computational Statistics & Data Analysis. Elsevier Science BV; 2008;52:5066-5074.

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