Research profile at the Google Scholar
Joe, H. (2014). Dependence Modeling with Copulas
Chapman & Hall/CRC. Published June/July 2014.
Publisher's web page,
and http://copula.stat.ubc.ca:
accompanying software and code for the book.
Dependence Modeling: Vine Copula Handbook (eds D Kurowicka and H
Joe), World Scientific, published in January 2011.
Publisher's
page.
My chapters are:
Joe, H. (1997). Multivariate Models and Dependence Concepts. Chapman
& Hall, London. Table of contents in postscript,
Table of contents in pdf
[ISBN 0-412-07331-5].
Errata
Colonius H, Jahansa P, Joe H, Diederich A (2023).
Towards dependent race models for the stop-signal paradigm.
Computational Brain & Behavior. (13 pp)
https://doi.org/10.1007/s42113-023-00184-3
Fan X and Joe H (2024).
High-dimensional factor copula models with estimation of latent variables.
Journal of Multivariate Analysis, 201, #105263 (20 pp + 9 page supplement).
https://doi.org/10.1016/j.jmva.2023.105263.
Zhang L, Joe H and Nolde N (2024).
Margin-closed multivariate time series models.
Journal of Time Series Analysis, 45, 269-297.
https://doi.org/10.1111/jtsa.12712
Coia V, Joe H, Nolde N (2024).
Copula-based conditional tail indices.
Journal of Multivariate Analysis, 201, #105268 (14 pp).
https://doi.org/10.1016/j.jmva.2023.105268
Li X and Joe H (2023).
Estimation of multivariate tail quantities.
Computational Statistics and Data Analysis, 185, #107761. (30 pp)
https://doi.org/10.1016/j.csda.2023.107761
Pan S and Joe H (2022).
Predicting times to event based on vine copula models.
Computational Statistics and Data Analysis, 175, #107546. (23 pp)
https://doi.org/10.1016/j.csda.2022.107546
Krupskii P and Joe H (2022).
Approximate likelihood with proxy variables for parameter
estimation in high-dimensional factor copula models.
Statistical Papers, 63, 543-569.
https://doi.org/10.1007/s00362-021-01252-1
Pan S, Joe H, and Li G (2023).
Conditional inferences based on vine copulas with
applications to credit spread data of corporate bonds.
Journal of Financial Econometrics, 21 (3), 714-741,
https://doi.org/10.1093/jjfinec/nbab016
Davis RA, Fokianos K, Holan SH, Joe H, Livsey J, Lund R,
Pipiras V and Ravishankar N (2021).
Count Time Series: A Methodological Review.
Journal of the American Statistical Association,
116 (535), 1533-1547.
https://doi.org/10.1080/01621459.2021.1904957
Cooke R M, Joe H and Chang B. (2022).
Vine Regression with Bayes Nets: a critical comparison with traditional approaches based on a case study on the effects of breastfeeding on IQ.
Risk Analysis, 42 (6), 1294-1305.
Special issue "Bayesian networks for risk analysis and decision-support".
https://doi.org/10.1111/risa.13695
Cai Y, Joe H and Pan S (2021).
Estimating dependence among lumber strength properties with copula models.
Frontiers in Applied Mathematics and Statistics, #578614. (14 pp).
Special Issue
"Multivariate Probabilistic Modelling for Risk and Decisions Analysis"
https://doi.org/10.3389/fams.2020.578614
Data set used in the paper.
Coia V, Joe H and Nolde N (2021).
Tail behavior for bivariate distributions based on Pareto mixtures.
In Advances in Statistics: Theory and Applications -
Honoring the Contributions of Barry C. Arnold in Statistical Science
Springer, New York. Pages 207-228.
Editors: N Balakrishnan, I Ghosh, and H K T Ng.
Krupskii P and Joe H (2020).
Flexible copula models with dynamic dependence and application to financial data.
Econometrics and Statistics, 16, 148-167.
https://doi.org/10.1016/j.ecosta.2020.01.005
Cooke R M, Joe H and Chang B (2020).
Vine copula regression for observational studies.
AStA Advances in Statistical Analysis, 104, 141-167.
https://doi.org/10.1007/s10182-019-00353-5
Chang B and Joe H (2020).
Copula diagnostics for asymmetries and conditional dependence.
Journal of Applied Statistics, 47(9), 1587-1615.
https://doi.org/10.1080/02664763.2019.1685080
Joe H (2019). Likelihood inference for generalized integer
autoregressive time series models. Econometrics, 7 (4),
article number #43. (13 pp)
https://doi.org/10.3390/econometrics7040043
Fernandez-Fontelo A, Cabana A, Joe H, Puig P, and Morina D (2019).
Untangling serially dependent underreported count data
for gender-based violence.
Statistics in Medicine, 38, 4404-4422.
https://doi.org/10.1002/sim.8306
Hadley D, Joe H and Nolde N (2019).
On the selection of loss severity distributions to model operational risk.
Journal of Operational Risk , 14 (3), 73-94.
https://doi.org/10.21314/JOP.2019.229
Chang B and Joe H (2019).
Prediction based on conditional distributions of vine copulas.
Computational Statistics and Data Analysis, 139, 45-63.
(+ 22 page supplement)
https://doi.org/10.1016/j.csda.2019.04.015
Krupskii P and Joe H (2019).
Nonparametric estimation of multivariate tail probabilities and tail dependence
coefficients.
Journal of Multivariate Analysis, 172, 147-161.
https://doi.org/10.1016/j.jmva.2019.02.013
Joe H and Li H (2019). Tail densities of skew-elliptical distributions.
Journal of Multivariate Analysis, 171, 421-435.
https://doi.org/10.1016/j.jmva.2019.01.009
Joe H (2018). Parsimonious graphical dependence models
constructed from vines. Canadian Journal of Statistics 46(4),
532-555. (+ 4 page supplement)
http://dx.doi.org/10.1002/cjs.11481
Lee D, Joe H, and Krupskii P, (2018).
Tail-weighted dependence measures with limit being the tail dependence
coefficient.
Journal of Nonparametric Statistics, 30 (2), 262-290.
http://dx.doi.org/10.1080/10485252.2017.1407414
Krupskii P, Joe H, Lee D and Genton M (2018).
Extreme-value limit of the convolution of exponential and multivariate normal distributions: Link to the Huesler-Reiss distribution.
Journal of Multivariate Analysis, 163, 80-95.
http://dx.doi.org/10.1016/j.jmva.2017.10.006
Lee D and Joe H (2018).
Efficient computation of multivariate empirical distribution functions at
the observed values. Computational Statistics, 33, 1413-1428.
http://dx.doi.org/10.1007/s00180-017-0771-x
Joe H (2017).
Parametric copula families for statistical models.
In: Copulas and Dependence Models with Applications: Contributions in
Honor of Roger B. Nelsen (M. Ubeda-Flores, E. de Amo-Artero, F. Durante
and J. Fernandez-Sanchez, Eds.). Springer, Berlin, pp 119-134.
https://link.springer.com/book/10.1007/978-3-319-64221-5
Lee D and Joe H (2018). Multivariate extreme value copulas with
factor and tree dependence structures.
Extremes, 21, 147-176.
http://dx.doi.org/10.1007/s10687-017-0298-0
Joe H (2018). Dependence properties of conditional distributions of some
copula models. Methodology and Computing in Applied Probability
20, 975-1001.
http://dx.doi.org/10.1007/s11009-017-9544-9
Hua L and Joe H (2017). Multivariate dependence modeling based on
comonotonic factors.
Journal of Multivariate Analysis, 155, 317-333.
http://dx.doi.org/10.1016/j.jmva.2017.01.008
Panagiotelis A, Czado C, Joe H, Stoeber J (2017).
Model selection for discrete regular vine copulas.
Computational Statistics and Data Analysis,
106, 138-152.
http://dx.doi.org/10.1016/j.csda.2016.09.007
Joe H and Sang P (2016).
Multivariate models for dependent clusters of variables with conditional
independence given aggregation variables.
Computational Statistics and Data Analysis, 97, 114-132.
http://dx.doi.org/10.1016/j.csda.2015.12.001
Ng CT and Joe H (2016).
Comparison of non-nested models under a general measure of distance.
J Statistical Planning and Inference, 170, 166-185.
http://dx.doi.org/10.1016/j.jspi.2015.10.004
Hexter A, Jones A, Joe H, Heap L, Smith MJ,
Wallace AJ, Halliday D, Parry A, Taylor A, Raymond L,
Shaw A, Afridi S, Obholzer R, Axon P, King AT,
The English Specialist NF2 Research Group, Friedman JM, Evans DGR
(2015).
Clinical and molecular predictors of mortality
in neurofibromatosis 2: a UK national analysis of 1192 patients.
J Medical Genetics, 52, 699-705.
http://dx.doi.org/10.1136/jmedgenet-2015-103290
Joe H (2015). Markov count time series models with covariates. In Handbook of Discrete-Valued Time Series, edited by Davis RA, Holan SH, Lund RB and Ravishanker N, pp 29-49. Chapman & Hall/CRC. Boca Raton, FL.
Special issue on "High-Dimensional Dependence and Copulas" Journal of Multivariate Analysis, June 2015, volume 138; http://www.sciencedirect.com/science/journal/0047259X/138
Brechmann EC and Joe H (2015).
Truncation of vine copulas using fit indices.
J Multivariate Analysis, 138, 19-33.
http://dx.doi.org/10.1016/j.jmva.2015.02.012
Krupskii P and Joe H (2015).
Structured factor copula models: theory, inference and computation.
J Multivariate Analysis, 138, 53-73.
http://dx.doi.org/10.1016/j.jmva.2014.11.002
Krupskii P and Joe H (2015).
Tail-weighted measures of dependence.
J Applied Statistics, 42, 614-629.
http://dx.doi.org/10.1080/02664763.2014.980787
Nikoloulopoulos A and Joe H (2015)
Factor copula models for item response data.
Psychometrika, 80, 126-150.
http://dx.doi.org/10.1007/S11336-013-9387-4
Maydeu-Olivares A and Joe H (2014). Assessing approximate fit in categorical
data analysis. Multivariate Behavioral Research, 49, 305--328.
http://dx.doi.org/10.1080/00273171.2014.911075
Brechmann EC and Joe H (2014).
Parsimonious parameterization of correlation matrices using truncated
vines and factor analysis.
Computational Statistics and Data Analysis, 77, 233-251.
http://dx.doi.org/10.1016/j.csda.2014.03.002
Ng CT and Joe H (2014).
Model comparison with composite likelihood information criteria.
Bernoulli, 20(4), 1738--1764.
http://dx.doi.org/10.3150/13-BEJ539
Hua L, Joe H and Li H (2014).
Relations between hidden regular variation and the tail order of copulas.
J Applied Probability, 51(1), 37-57.
https://doi.org/10.1239/jap/1395771412
Hua L and Joe H (2014).
Strength of tail dependence based on conditional tail expectation.
J Multivariate Analysis 123, 143-159.
http://dx.doi.org/10.1016/j.jmva.2013.09.001
Nolde N and Joe H (2013).
A Bayesian extreme value analysis of debris flows.
Water Resources Research, 49, 7009-7022.
http://dx.doi.org/10.1002/wrcr.20494
Krupskii P and Joe H (2013).
Factor copula models for multivariate data,
J Multivariate Analysis, 120, 85-101.
http://dx.doi.org/10.1016/j.jmva.2013.05.001
Stoeber J, Joe H and Czado C (2013).
Simplified pair copula constructions -- limitations and extensions.
J Multivariate Analysis, 119, 101-118.
http://dx.doi.org/10.1016/j.jmva.2013.04.014
Hua L and Joe H (2013).
Intermediate tail dependence: a review and some new results.
In Stochastic Orders in Reliability and Risk:
In honor of Professor Moshe Shaked. Eds H. Li and X. Li.
Lecture Notes in Statistics, Springer, pp 291-311.
http://dx.doi.org/10.1007/978-1-4614-6892-9_15
Rosco JF and Joe H (2013).
Measures of tail asymmetry for bivariate copulas.
Statistical Papers, 54, 709-726.
http://dx.doi.org/10.1007/s00362-012-0457-y
Joe H, Seshadri V and Arnold BC (2012).
Multivariate inverse Gaussian and skew-normal densities.
Statistics & Probability Letters, 82, 2244-2251.
http://dx.doi.org/10.1016/j.spl.2012.08.004
Joe H (2012).
Book review of
"Inequalities: Theory of Majorization and Its Applications, 2nd edition
by A. W. Marshall, I. Olkin and B. C. Arnold, Springer".
Probability in the Engineering and Informational Sciences, 26, 449-453.
http://dx.doi.org/10.1017/S0269964812000113
Hua L and Joe H (2012).
Tail comonotonicity: properties, constructions, and asymptotic additivity of risk measures}
Insurance: Mathematics and Economics, 51, 492-503.
http://dx.doi.org/10.1016/j.insmatheco.2012.07.006
Hua L and Joe H (2012).
Tail comonotonicity and conservative risk measures.
ASTIN Bulletin, 42(2), 601-629.
http://dx.doi.org/10.2143/AST.42.2.2182810
Panagiotelis A, Czado C and Joe H (2012).
Pair copula constructions for multivariate discrete data.
J American Statistical Association, 107, 1063-1072.
http://dx.doi.org/10.1080/01621459.2012.682850
Joe H and Seshadri V (2012).
Infinitely divisible distributions arising from first crossing
times and related results. Sankhya A, 74, 222-248.
https://doi.org/10.1007/s13171-012-0002-z
Nikoloulopoulos AK, Joe H, Li H (2012).
Vine copulas with asymmetric tail dependence and
applications to financial return data.
Computational Statistics and Data Analysis, 56, 3659-3673.
https://doi.org/10.1016/j.csda.2010.07.016
Hua L and Joe H (2011).
Second order regular variation and conditional tail expectation of multiple risks
Insurance: Mathematics and Economics, 49, 537-546.
https://doi.org/10.1016/j.insmatheco.2011.08.013
Hua L and Joe H (2011).
Tail order and intermediate tail dependence of multivariate copulas.
J Multivariate Analysis, 102, 1454-1471.
https://doi.org/10.1016/j.jmva.2011.05.011
Nikoloulopoulos AK, Joe H, and Chaganty NR (2011).
Weighted scores method for regression models with dependent data,
Biostatistics, 12, 653-665.
https://doi.org/10.1093/biostatistics/kxr005
Baser ME, Friedman JM, Joe H, Shenton A, Wallace AJ, Ramsden RT, Evans DGR
(2011). Empirical development of diagnostic criteria for neurofibromatosis 2,
Genetics in Medicine, 13, 576-581.
https://doi.org/10.1097/GIM.0b013e318211faa9
Ng CT, Joe H, Karlis D and Liu J (2011). Composite likelihood for time series models with a latent autoregressive process. Statistica Sinica, 21, 279-305. [issue on composite likelihood]
El-Shaarawi A, Zhu R, Joe H (2011).
Modelling species abundance using the Tweedie-Poisson family,
Environmetrics, 22, 152-164.
https://doi.org/10.1002/env.1036
Joe H and Li H (2011).
Tail risk of multivariate regular variation.
Methodology and Computing in Applied Probability, 13, 671-693.
https://doi.org/10.1007/s11009-010-9183-x
Zhu R and Joe H (2010).
Count data time series models based on expectation thinning.
Stochastic Models, 26, 431-462.
http://dx.doi.org/10.1080/15326349.2010.498318
Ng CT and Joe H (2010).
Generating random AR(p) and MA(q) Toeplitz correlation matrices.
J Multivariate Analysis, 101, 1532-1545.
http://dx.doi.org/10.1016/j.jmva.2010.01.013
Zhu R and Joe H (2010).
Negative binomial time series models based on expectation thinning operators.
J Statistical Planning and Inference, 140, 1874-1888.
http://dx.doi.org/10.1016/j.jspi.2010.01.031
Joe H and Maydeu-Olivares A (2010).
A general family of limited information goodness-of-fit statistics for
multinomial data. Psychometrika, 75, 393-419.
http://dx.doi.org/10.1007/S11336-010-9165-5
Joe H, Li H, Nikoloulopoulos AK, (2010).
Tail dependence functions and vine copulas
J Multivariate Analysis, 101, 252-270.
http://dx.doi.org/10.1016/j.jmva.2009.08.002
Zhu R and Joe H (2009). Modelling heavy-tailed count data using
a generalized Poisson-inverse Gaussian family.
Statistics & Probability Letters, 79, 1695-1703.
http://dx.doi.org/10.1016/j.spl.2009.04.011
Lewandowski D, Kurowicka D and Joe H (2009).
Generating random correlation matrices based on vines and extended
Onion method. J Multivariate Analysis, 100, 1989-2001.
zip files with
code in (a) R and C, (b)
Matlab and Octave.
http://dx.doi.org/10.1016/j.jmva.2009.04.008
Willems G, Joe H and Zamar R (2009). Diagnosing multivariate
outliers detected by robust estimators.
J Computational and Graphical Statistics, 18, 73-91.
http://dx.doi.org/10.1198/jcgs.2009.0005
Nikoloulopoulos AK, Joe H, Li H (2009).
Extreme value properties of multivariate t-copulas.
Extremes, 12, 129-148.
http://dx.doi.org/10.1007/s10687-008-0072-4
Joe H and Lee Y (2009).
On weighting of bivariate margins in pairwise likelihood
J Multivariate Analysis, 100, 670-685.
http://dx.doi.org/10.1016/j.jmva.2008.07.004
Maydeu-Olivares A and Joe H (2008). An overview of limited information goodness-of-fit testing in multidimensional contingency tables. In K. Shigemasu, A. Okada, T. Imaizumi, & T. Hoshino (Eds.) New Trends in Psychometrics, (pp. 253--262). Tokyo: Universal Academy Press.
Joe, H (2008). Accuracy of Laplace approximation for discrete response
mixed models. Computational Statistics and Data Analysis, 52, 5066-5074.
http://dx.doi.org/10.1016/j.csda.2008.05.002
Zhao Y and Joe H (2008). Inferences for log odds ratio with dependent pairs.
Test, 17, 101-119.
http://dx.doi.org/10.1007/s11749-006-0025-7
Alwan S, Armstrong L, Joe H, Birch PH, Szudek J, Friedman JM
(2007). Associations of osseous lesions in Neurofibromatosis 1 (NF1).
American J Medical Genetics, 143A, 1326-1333.
http://dx.doi.org/10.1002/ajmg.a.31754
Maydeu-Olivares A and Joe H (2006). Limited information goodness-of-fit
testing in multidimensional contingency tables.
Psychometrika, 71, 713-732.
See R package named pln.
http://dx.doi.org/10.1007/s11336-005-1295-9
Joe, H (2006). Discussion of "Copulas: tales and facts", by Thomas
Mikosch. Extremes, 9, 37-41.
[Entire article with discussion and rejoinder pp 1-62.]
http://dx.doi.org/10.1007/s10687-006-0019-6
Qiu W and Joe H (2006). Generation of random clusters with
specified degree of separation.
J Classification, 23, 315-334.
http://dx.doi.org/10.1007/s00357-006-0018-y
Joe, H (2006). Paired comparison models and estimation for age-adjusted strengths of top chess players. In Appendix of Who was the strongest? Warriors of the Mind II, By Raymond Keene, Nathan Divinsky and Jeff Sonas. Hardinge Simpole Publishing. Aylesbeare, Devon, England.
Zhu R, Joe H (2006).
Modelling count data time series with Markov processes
based on binomial thinning. J Time Series Analysis,
27, 725-738.
http://dx.doi.org/10.1111/j.1467-9892.2006.00485.x
Chaganty NR and Joe H (2006). Range of correlation matrices for
dependent Bernoulli random variables. Biometrika, 93, 197-206.
https://doi.org/10.1093/biomet/93.1.197
Joe H (2006). Range of correlation matrices for dependent
random variables with given marginal distributions.
In Advances in Distribution Theory, Order Statistics and Inference,
in honor of Barry Arnold, eds N. Balakrishnan, E. Castillo, J.M. Sarabia.
Birkhauser, Boston; pp 125-142.
https://doi.org/10.1007/0-8176-4487-3_8
Joe H and Maydeu-Olivares A (2006). On the asymptotic distribution
of Pearson's X2 in cross-validation samples.
Psychometrika, 71, 587-592.
https://doi.org/10.1007/S11336-005-1284-Z
Joe H (2006). Generating random correlation matrices based on partial
correlations. J Multivariate Analysis, 97, 2177-2189.
https://doi.org/10.1016/j.jmva.2005.05.010
Qiu, W and Joe, H (2006). Separation index and partial membership for
clustering. Computational Statistics and Data Analysis, 50, 585-603.
https://doi.org/10.1016/j.csda.2004.09.009
Joe, H. and Zhu, R. (2005). Generalized Poisson distribution: the
property of mixture of Poisson and comparison with negative binomial
distribution. Biometrical J, 47, 219-229.
https://doi.org/10.1002/bimj.200410102
Baser ME, Kuramoto L, Woods R, Joe H, Friedman JM, Wallace AJ,
Ramsden RT, Olschwang S, Bijlsma E, Kalamarides M, Papi L, Kato R,
Carroll J, Lázaro C, Joncourt F, Parry DM, Rouleau GA, Evans DGR. (2005).
The location of constitutional neurofibromatosis 2 (NF2) splice-site
mutations is associated with the severity of NF2. J Medical Genetics,
42, 540-546.
https://doi.org/10.1136/jmg.2004.029504
Zhao, Y and Joe, H (2005). Composite likelihood estimation in multivariate
data analysis, Canadian J Statistics, 33, 335-356.
https://doi.org/10.1002/cjs.5540330303
Joe, H and Latif, A H Md M (2005). Computations for the familial analysis
of binary traits. Computational Statistics, 20, 439-448.
https://doi.org/10.1007/BF02741307
Maydeu-Olivares, A and Joe, H (2005). Limited and full information
estimation and goodness-of-fit testing in 2^n contingency tables: A
unified framework. J American Statistical Association, 100, 1009-1020.
https://doi.org/10.1198/016214504000002069
Joe, H (2005). Asymptotic efficiency of the two-stage estimation method
for copula-based models. J Multivariate Analysis, 94, 401-419.
https://doi.org/10.1016/j.jmva.2004.06.003
Baser ME, Kuramoto L, Joe H, Friedman JM, Wallace AJ,
Gillespie JE, Ramsden RT, Evans DGR (2004).
Genotype-phenotype correlations for nervous system tumors in
neurofibromatosis 2: a population-based study,
American J Human Genetics, 75, 231-239.
https://doi.org/10.1086/422700
Chaganty, NR and Joe, H (2004).
Efficiency of the generalised estimating equations for binary response.
J Royal Statistical Society B, 66, 851-860.
https://doi.org/10.1111/j.1467-9868.2004.05741.x
Palmer V, Szudek J, Joe H, Riccardi VM,
and Friedman JM (2004). Analysis of neurofibromatosis 1 (nf1) lesions
by body segment. American J Medical Genetics 125A (2), 157-161.
https://doi.org/10.1002/ajmg.a.20354
Baser ME, Kuramoto L, Joe H, Friedman JM, Wallace AJ, Ramsden RT,
Evans DGR (2003). Genotype-phenotype correlations for cataracts in
neurofibromatosis 2. J Medical Genetics 40, 758-760.
https://doi.org/10.1136/jmg.40.10.758
Joe, H and Nash, JC (2003). Numerical optimization and surface
estimation with imprecise function evaluations. Statistics and Computing
13, 277-286
https://doi.org/10.1023/A:1024226918553
Woods R, Friedman JM, Evans DGR, Baser ME, and Joe H
(2003). Exploring the `2-hit hypothesis' in NF2: Tests of 2-hit and
3-hit models of vestibular schwannoma development,
Genetic Epidemiology, 24, 265272.
https://doi.org/10.1002/gepi.10238
Zhu, R and Joe, H (2003). A new type of discrete self-decomposability
and its application to continuous-time Markov processes for modeling
count data time series. Stochastic Models, 19, 235-254.
https://doi.org/10.1081/STM-120020388
Baser ME, Friedman JM, Wallace AJ, Ramsden RT, Joe H, Evans DGR
(2002). Evaluation of clinical diagnostic criteria for neurofibromatosis
2. Neurology, 59(11), 1759-1765.
https://doi.org/10.1212/01.WNL.0000035638.74084.F4
Zhao Y, Kumar RA, Baser ME, Evans DGR, Wallace A, Kluwe L, Mautner
VF, Parry DM, Rouleau GA, Joe H, Friedman JM (2002). Intrafamilial
correlation of clinical manifestations in neurofibromatosis 2 (NF2).
Genetic Epidemiology, 23, 245-259.
https://doi.org/10.1002/gepi.10181
Baser ME, Friedman JM, Aeschliman D, Joe H, Wallace AJ, Ramsden RT, Evans
DGR (2002). Predictors of the risk of mortality in neurofibromatosis 2.
American J Human Genetics, 71, 715-723.
https://doi.org/10.1086/342716
Szudek J, Joe H and Friedman JM (2002).
Analysis of intra-familial phenotypic variation in neurofibromatosis 1
(Nf1). Genetic Epidemiology, 23, 150-164.
https://doi.org/10.1002/gepi.01129
Joe, H. (2002). Stochastic orderings in random utility models.
Mathematical Social Sciences, 43, 391-404
https://doi.org/10.1016/S0165-4896(02)00018-5
Joe, H. (2001). Discussion of ``Conditionally specified distributions: an introduction", by Arnold, Castillo and Sarabia, Statistical Science, 16, 270-271.
Joe, H. (2001). Majorization and stochastic orders. International Encyclopedia of the Social & Behavioral Sciences, 6, 9139-43.
Joe, H. (2001). Multivariate extreme value distributions and
coverage of ranking probabilities. J Mathematical Psychology,
45, 180-188.
https://doi.org/10.1006/jmps.1991.1294
Arnold, B.C. and Joe, H. (2000). Variability ordering of functions. International J Math Stat Sci, 9, 179-189.
Joe, H. (2000). Inequalities for random utility models,
with applications to ranking and subset choice data.
Methodology and Computing in Applied Probability, 2, 359-372.
https://doi.org/10.1023/A:1010058117460
Joe, H. and Ma, C. (2000). Multivariate survival functions with a
min-stable property. J Multivariate Analysis, 75, 13-35.
https://doi.org/10.1006/jmva.1999.1891
Regenwetter, M., Marley, A.A.J., and Joe, H. (1998).
Random utility threshold models of subset choice.
Australian J Psychology, 50, 175-185.
https://doi.org/10.1080/00049539808258794
Block, H. and Joe, H. (1997). Tail behavior of the failure rate functions
of mixtures. Lifetime Data Analysis. , 3, 269-288.
https://doi.org/10.1023/A:1009653032333
Joe, H. and Xu, J.J. (1996). "The estimation method of inference functions for margins for multivariate models." Technical Report no. 166, Department of Statistics, University of British Columbia. Available at UBC cIRcle. (The theory is also in Chapter 10 of the 1997 book).
Joe, H. (1996). "Families of m-variate distributions with given margins
and m(m-1)/2 bivariate dependence parameters." In Distributions
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Joe, H. and Hu, T. (1996). "Multivariate distributions from mixtures
of max-infinitely divisible distributions." J Multivariate Analysis, 57,
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Joe, H., Steyn, D.G. and Susko, E. (1996). "Analysis of trends
in tropospheric ozone in the lower Fraser Valley, British
Columbia." Atmospheric Environment, 30/20, 3413-3421.
https://doi.org/10.1016/1352-2310(96)00045-3
Joe, H. and Liu, Y. (1996). "A model for a multivariate binary response
with covariates based on compatible conditionally specified logistic
regressions." Statistics & Probability Letters, 31, 113-120.
https://doi.org/10.1016/S0167-7152(96)00021-1
Joe, H. (1996). "Time series models with univariate margins in the
convolution-closed infinitely divisible class." J Applied
Probability, 33, 664-677.
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Hu, T. and Joe, H. (1995). Monotonicity of positive dependence with time
for stationary reversible Markov chains. Probability in the Engineering
and Informational Sciences,
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Joe, H. (1995). "Approximations to multivariate
normal rectangle probabilites based on conditional
expectations." J American Statistical Association, 90, 957-964.
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Fang, Z., Hu, T. and Joe, H. (1994). "On the decrease in dependence with
lag for stationary Markov chains." Probabability in the Engineering
and Informational Sciences, 8, 385-401.
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Joe, H. (1994). "Multivariate extreme value distributions and applications
to environmental data." Canadian J Statistics, 22, 47-64.
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Clarkson, D.B., Fan, Y.-A. and Joe, H. (1993). A remark on
algorithm 643: FEXACT: An algorithm for performing Fisher's exact
test in rxc contingency tables. ACM Transaction on Mathematical
Software , 19, 484-488.
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Joe, H. (1993). Multivariate dependence measures and data analysis.
Computational Statistics & Data Analysis, 16, 279-297.
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Joe, H. (1993). Parametric families of multivariate distributions with
given margins. J Multivariate Analysis, 46, 262-282.
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Joe, H. (1993). Tests of uniformity for sets of lotto numbers.
Statistics & Probability Letters, 16, 181-188.
https://doi.org/10.1016/0167-7152(93)90141-5
Joe, H. (1993). Generalized majorization orderings and applications.
In "Stochastic Inequalities", edited by M. Shaked and Y. Tong, 145-158.
IMS Lecture Notes-Monograph Series, volume 22. Hayward, CA.
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Joe, H. and Verducci, J.S. (1993). Multivariate majorization by
positive combinations. In "Stochastic Inequalities", edited by M.
Shaked and Y. Tong, 159-181. IMS Lecture Notes-Monograph Series,
volume 22. Hayward, CA.
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Fang, Z. and Joe, H. (1992). Further developments on some dependence
orderings for continuous bivariate distributions.
Annals Institute Statistical Mathematics, 44, 501-517.
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Joe, H., Smith, R.L., and Weissman, I. (1992). Bivariate threshold
methods for extremes. J Royal Statistical Society B, 54, 171-183.
Joe, H. and Verducci, J.S. (1992). On the Babington Smith class of
models for rankings. In "Probability Models and Statistical
Analyses for Ranking Data", edited by M.A. Fligner and J.S. Verducci,
pp. 37-52. Lecture Notes in Statistics, Springer-Verlag, New York.
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Joe, H. (1991). Rating systems based on paired comparison models.
Statistics & Probability Letters, 11, 343--347.
https://doi.org/10.1016/0167-7152(91)90046-T
Joe, H. (1990). Multivariate concordance. J Multivariate Analysis
35, 12--30.
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Joe, H. (1990). A winning strategy for lotto games? Canadian J
Statistics, 18, 233-244.
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Joe, H. (1990). Majorization and divergence. J Mathematical Analysis and
Applications, 148, 287-305.
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Joe, H. (1990). Extended use of paired comparison models,
with application to chess rankings. Applied Statistics, 39, 85-93.
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Joe, H. (1990). Families of min-stable multivariate exponential
and multivariate extreme value distributions. Statistics &
Probability Letters, 9, 75-81.
https://doi.org/10.1016/0167-7152(90)90098-R
Joe, H. (1989). Estimation of entropy and other functionals of a
multivariate density. Annals Institute Statistical Mathematics, 41, 683-697.
https://doi.org/10.1007/BF00057735
Joe, H. (1989). Discussion of "Extreme value analysis of environmental time series: an application to trend detection in ground-level ozone", by R.L. Smith. Statistical Science, 4, 384-385.
Joe, H. (1989). Statistical inference for general-order-statistics
and nonhomogeneous-Poisson-processes software reliability models.
IEEE Transactions Software Engineering, SE 15, 1485-1490.
https://doi.org/10.1109/32.41340
Joe, H. (1989). Relative entropy measures of multivariate dependence.
J American Statistical Association, 84, 157-164.
https://doi.org/10.2307/2289859
Joe, H. (1988). Majorization, entropy and paired comparisons.
Annals of Statistics, 16, 915-925.
https://doi.org/10.1214/aos/1176350843
Joe, H. (1988). Extreme probabilities for contingency tables under
row and column independence, with application to Fisher's exact test.
Communications in Statistics A17 (No.11), 3677-3685.
https://doi.org/10.1080/03610928808829827
Joe, H. (1987). Majorization, randomness and dependence for
multivariate distributions. Annals of Probability 15, 1217-1225.
https://doi.org/10.1214/aop/1176992093
Joe, H. (1987). Estimation of quantiles of the maximum of N
observations. Biometrika 74, 347-354.
https://doi.org/10.1093/biomet/74.2.347
Joe, H. (1987). An ordering of dependence for distributions of
k-tuples, with applications to lotto games. Canadian J Statistics 15,
227-238.
https://doi.org/10.2307/3314913
Thompson, M.P., Joe, H. and Church, M. (1987). Statistical modelling of sediment concentration. Report for Sediment Section, Water Survey of Canada, Water Resources Branch, Inland Waters Directorate, Environment Canada. 60pp.
Joe, H. and Reid, N. (1985). Estimating the number of faults in a
system. J American Statistical Association 80, 222-226.
https://doi.org/10.2307/2288076
Joe, H. (1985). Characterizations of life distributions from
percentile residual lifetimes. Annals Institute Statistical
Mathematics, 37, 165-172.
https://doi.org/10.1007/BF02481089
Joe, H. (1985). An ordering of dependence for contingency tables.
Linear Algebra and its Applications, Special Statistics Issue 70,
89-103.
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Joe, H. and Proschan, F. (1984). Percentile residual life functions.
Operations Research 32, 668-678.
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Joe, H. and Proschan, F. (1984). Comparison of two life distributions
on the basis of their percentile residual life functions. Canadian J
Statistics 12, 91-97.
https://doi.org/10.2307/3315173
Joe, H., Koziol, J.A. and Petkau, A.J. (1981). Comparison of
procedures for testing the equality of survival distributions.
Biometrics 37, 327-340.
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