H. Joe : Publications

Research profile at the Google Scholar


Books

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


Articles

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, in press.
https://doi.org/10.1002/sim.8306

Cooke R M, Joe H and Chang B (2019). Vine copula regression for observational studies. AStA Advances in Statistical Analysis, in press.
https://doi.org/10.1007/s10182-019-00353-5

Hadley D, Joe H and Nolde N (2019). On the selection of loss severity distributions to model operational risk. Journal of Operational Risk , accepted.

Chang B and Joe H (2019). Prediction based on conditional distributions of vine copulas. Computational Statistics and Data Analysis, 139, 45-63.
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.
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 with Fixed Marginals and Related Topics, eds. L. Rueschendorf, B. Schweizer and M.D. Taylor, IMS Lecture Notes-Monograph Series. Hayward, CA, pp. 120-141.
https://doi.org/10.1214/lnms/1215452614

Joe, H. and Hu, T. (1996). "Multivariate distributions from mixtures of max-infinitely divisible distributions." J Multivariate Analysis, 57, 240-265.
https://doi.org/10.1006/jmva.1996.0032

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.
https://doi.org/10.2307/3215348

Hu, T. and Joe, H. (1995). Monotonicity of positive dependence with time for stationary reversible Markov chains. Probability in the Engineering and Informational Sciences, 9, 227-237.
https://doi.org/10.1017/S026996480000382X

Joe, H. (1995). "Approximations to multivariate normal rectangle probabilites based on conditional expectations." J American Statistical Association, 90, 957-964.
[June 2006: code included in R package mprobit; see www.r-project.org],
[small correction for Tables 1 and 7]
https://doi.org/10.2307/2291331

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.
https://doi.org/10.1017/S026996480000348X

Joe, H. (1994). "Multivariate extreme value distributions and applications to environmental data." Canadian J Statistics, 22, 47-64.
https://doi.org/10.2307/3315822

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.
https://doi.org/10.1145/168173.168412

Joe, H. (1993). Multivariate dependence measures and data analysis. Computational Statistics & Data Analysis, 16, 279-297.
https://doi.org/10.1016/0167-9473(93)90130-L

Joe, H. (1993). Parametric families of multivariate distributions with given margins. J Multivariate Analysis, 46, 262-282.
https://doi.org/10.1006/jmva.1993.1061

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.
https://doi.org/10.1214/lnms/1215461949

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.
https://doi.org/10.1214/lnms/1215461950

Fang, Z. and Joe, H. (1992). Further developments on some dependence orderings for continuous bivariate distributions. Annals Institute Statistical Mathematics, 44, 501-517.
https://doi.org/10.1007/BF00050701

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.
https://doi.org/10.1007/978-1-4612-2738-0_3

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.
https://doi.org/10.1016/0047-259X(90)90013-8

Joe, H. (1990). A winning strategy for lotto games? Canadian J Statistics, 18, 233-244.
https://doi.org/10.2307/3315454

Joe, H. (1990). Majorization and divergence. J Mathematical Analysis and Applications, 148, 287-305.
https://doi.org/10.1016/0022-247X(90)90002-W

Joe, H. (1990). Extended use of paired comparison models, with application to chess rankings. Applied Statistics, 39, 85-93.
https://doi.org/10.2307/2347814

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.
https://doi.org/10.1016/0024-3795(85)90045-X

Joe, H. and Proschan, F. (1984). Percentile residual life functions. Operations Research 32, 668-678.
https://doi.org/10.1287/opre.32.3.668

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.
https://doi.org/10.2307/2530421