H. Joe : Publications

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].


Krupskii P and Joe H (2021). Approximate likelihood with proxy variables for parameter estimation in high-dimensional factor copula models. Statistical Papers, to appear.

Pan S, Joe H, and Li G (2021). Conditional inferences based on vine copulas with applications to credit spread data of corporate bonds. Journal of Financial Econometrics

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, in press.

Cooke R M, Joe H and Chang B. (2021). 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.
Special issue "Bayesian networks for risk analysis and decision-support".

Cai Y, Joe H and Pan S (2021). Estimating dependence among lumber strength properties with copula models. Frontiers in Applied Mathematics and Statistics. Special Issue "Multivariate Probabilistic Modelling for Risk and Decisions Analysis"
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 - On the Occasion of Barry C. Arnold's 80th Birthday. Springer, New York. to appear.
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.

Cooke R M, Joe H and Chang B (2020). Vine copula regression for observational studies. AStA Advances in Statistical Analysis, 104, 141-167.

Chang B and Joe H (2020). Copula diagnostics for asymmetries and conditional dependence. Journal of Applied Statistics, 47(9), 1587-1615.

Joe H (2019). Likelihood inference for generalized integer autoregressive time series models. Econometrics, 7 (4).

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.

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.

Chang B and Joe H (2019). Prediction based on conditional distributions of vine copulas. Computational Statistics and Data Analysis, 139, 45-63.

Krupskii P and Joe H (2019). Nonparametric estimation of multivariate tail probabilities and tail dependence coefficients. Journal of Multivariate Analysis, 172, 147-161.

Joe H and Li H (2019). Tail densities of skew-elliptical distributions. Journal of Multivariate Analysis, 171, 421-435.

Joe H (2018). Parsimonious graphical dependence models constructed from vines. Canadian Journal of Statistics 46(4), 532-555.

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.

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.

Lee D and Joe H (2018). Efficient computation of multivariate empirical distribution functions at the observed values. Computational Statistics, 33, 1413-1428.

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.

Lee D and Joe H (2018). Multivariate extreme value copulas with factor and tree dependence structures. Extremes, 21, 147-176.

Joe H (2018). Dependence properties of conditional distributions of some copula models. Methodology and Computing in Applied Probability 20, 975-1001.

Hua L and Joe H (2017). Multivariate dependence modeling based on comonotonic factors. Journal of Multivariate Analysis, 155, 317-333.

Panagiotelis A, Czado C, Joe H, Stoeber J (2017). Model selection for discrete regular vine copulas. Computational Statistics and Data Analysis, 106, 138-152.

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.

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.

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.

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.

Krupskii P and Joe H (2015). Structured factor copula models: theory, inference and computation. J Multivariate Analysis, 138, 53-73.

Krupskii P and Joe H (2015). Tail-weighted measures of dependence. J Applied Statistics, 42, 614-629.

Nikoloulopoulos A and Joe H (2015) Factor copula models for item response data. Psychometrika, 80, 126-150.

Maydeu-Olivares A and Joe H (2014). Assessing approximate fit in categorical data analysis. Multivariate Behavioral Research, 49, 305--328.

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.

Ng CT and Joe H (2014). Model comparison with composite likelihood information criteria. Bernoulli, 20(4), 1738--1764.

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.

Hua L and Joe H (2014). Strength of tail dependence based on conditional tail expectation. J Multivariate Analysis 123, 143-159.

Nolde N and Joe H (2013). A Bayesian extreme value analysis of debris flows. Water Resources Research, 49, 7009-7022.

Krupskii P and Joe H (2013). Factor copula models for multivariate data, J Multivariate Analysis, 120, 85-101.

Stoeber J, Joe H and Czado C (2013). Simplified pair copula constructions -- limitations and extensions. J Multivariate Analysis, 119, 101-118.

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.

Rosco JF and Joe H (2013). Measures of tail asymmetry for bivariate copulas. Statistical Papers, 54, 709-726.

Joe H, Seshadri V and Arnold BC (2012). Multivariate inverse Gaussian and skew-normal densities. Statistics & Probability Letters, 82, 2244-2251.

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.

Hua L and Joe H (2012). Tail comonotonicity: properties, constructions, and asymptotic additivity of risk measures} Insurance: Mathematics and Economics, 51, 492-503.

Hua L and Joe H (2012). Tail comonotonicity and conservative risk measures. ASTIN Bulletin, 42(2), 601-629.

Panagiotelis A, Czado C and Joe H (2012). Pair copula constructions for multivariate discrete data. J American Statistical Association, 107, 1063-1072.

Joe H and Seshadri V (2012). Infinitely divisible distributions arising from first crossing times and related results. Sankhya A, 74, 222-248.

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.

Hua L and Joe H (2011). Second order regular variation and conditional tail expectation of multiple risks Insurance: Mathematics and Economics, 49, 537-546.

Hua L and Joe H (2011). Tail order and intermediate tail dependence of multivariate copulas. J Multivariate Analysis, 102, 1454-1471.

Nikoloulopoulos AK, Joe H, and Chaganty NR (2011). Weighted scores method for regression models with dependent data, Biostatistics, 12, 653-665.

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.

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.

Joe H and Li H (2011). Tail risk of multivariate regular variation. Methodology and Computing in Applied Probability, 13, 671-693.

Zhu R and Joe H (2010). Count data time series models based on expectation thinning. Stochastic Models, 26, 431-462.

Ng CT and Joe H (2010). Generating random AR(p) and MA(q) Toeplitz correlation matrices. J Multivariate Analysis, 101, 1532-1545.

Zhu R and Joe H (2010). Negative binomial time series models based on expectation thinning operators. J Statistical Planning and Inference, 140, 1874-1888.

Joe H and Maydeu-Olivares A (2010). A general family of limited information goodness-of-fit statistics for multinomial data. Psychometrika, 75, 393-419.

Joe H, Li H, Nikoloulopoulos AK, (2010). Tail dependence functions and vine copulas J Multivariate Analysis, 101, 252-270.

Zhu R and Joe H (2009). Modelling heavy-tailed count data using a generalized Poisson-inverse Gaussian family. Statistics & Probability Letters, 79, 1695-1703.

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.

Willems G, Joe H and Zamar R (2009). Diagnosing multivariate outliers detected by robust estimators. J Computational and Graphical Statistics, 18, 73-91.

Nikoloulopoulos AK, Joe H, Li H (2009). Extreme value properties of multivariate t-copulas. Extremes, 12, 129-148.

Joe H and Lee Y (2009). On weighting of bivariate margins in pairwise likelihood J Multivariate Analysis, 100, 670-685.

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.

Zhao Y and Joe H (2008). Inferences for log odds ratio with dependent pairs. Test, 17, 101-119.

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.

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.


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.]

Qiu W and Joe H (2006). Generation of random clusters with specified degree of separation. J Classification, 23, 315-334.

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.

Chaganty NR and Joe H (2006). Range of correlation matrices for dependent Bernoulli random variables. Biometrika, 93, 197-206.

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.

Joe H and Maydeu-Olivares A (2006). On the asymptotic distribution of Pearson's X2 in cross-validation samples. Psychometrika, 71, 587-592.

Joe H (2006). Generating random correlation matrices based on partial correlations. J Multivariate Analysis, 97, 2177-2189.

Qiu, W and Joe, H (2006). Separation index and partial membership for clustering. Computational Statistics and Data Analysis, 50, 585-603.

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.

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.

Zhao, Y and Joe, H (2005). Composite likelihood estimation in multivariate data analysis, Canadian J Statistics, 33, 335-356.

Joe, H and Latif, A H Md M (2005). Computations for the familial analysis of binary traits. Computational Statistics, 20, 439-448.

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.

Joe, H (2005). Asymptotic efficiency of the two-stage estimation method for copula-based models. J Multivariate Analysis, 94, 401-419.

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.

Chaganty, NR and Joe, H (2004). Efficiency of the generalised estimating equations for binary response. J Royal Statistical Society B, 66, 851-860.

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.

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.

Joe, H and Nash, JC (2003). Numerical optimization and surface estimation with imprecise function evaluations. Statistics and Computing 13, 277-286

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.

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.

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.

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.

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.

Szudek J, Joe H and Friedman JM (2002). Analysis of intra-familial phenotypic variation in neurofibromatosis 1 (Nf1). Genetic Epidemiology, 23, 150-164.

Joe, H. (2002). Stochastic orderings in random utility models. Mathematical Social Sciences, 43, 391-404

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.

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.

Joe, H. and Ma, C. (2000). Multivariate survival functions with a min-stable property. J Multivariate Analysis, 75, 13-35.

Regenwetter, M., Marley, A.A.J., and Joe, H. (1998). Random utility threshold models of subset choice. Australian J Psychology, 50, 175-185.

Block, H. and Joe, H. (1997). Tail behavior of the failure rate functions of mixtures. Lifetime Data Analysis. , 3, 269-288.

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.

Joe, H. and Hu, T. (1996). "Multivariate distributions from mixtures of max-infinitely divisible distributions." J Multivariate Analysis, 57, 240-265.

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.

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.

Joe, H. (1996). "Time series models with univariate margins in the convolution-closed infinitely divisible class." J Applied Probability, 33, 664-677.

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.

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]

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.

Joe, H. (1994). "Multivariate extreme value distributions and applications to environmental data." Canadian J Statistics, 22, 47-64.

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.

Joe, H. (1993). Multivariate dependence measures and data analysis. Computational Statistics & Data Analysis, 16, 279-297.

Joe, H. (1993). Parametric families of multivariate distributions with given margins. J Multivariate Analysis, 46, 262-282.

Joe, H. (1993). Tests of uniformity for sets of lotto numbers. Statistics & Probability Letters, 16, 181-188.

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.

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.

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

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.

Joe, H. (1991). Rating systems based on paired comparison models. Statistics & Probability Letters, 11, 343--347.

Joe, H. (1990). Multivariate concordance. J Multivariate Analysis 35, 12--30.

Joe, H. (1990). A winning strategy for lotto games? Canadian J Statistics, 18, 233-244.

Joe, H. (1990). Majorization and divergence. J Mathematical Analysis and Applications, 148, 287-305.

Joe, H. (1990). Extended use of paired comparison models, with application to chess rankings. Applied Statistics, 39, 85-93.

Joe, H. (1990). Families of min-stable multivariate exponential and multivariate extreme value distributions. Statistics & Probability Letters, 9, 75-81.

Joe, H. (1989). Estimation of entropy and other functionals of a multivariate density. Annals Institute Statistical Mathematics, 41, 683-697.

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.

Joe, H. (1989). Relative entropy measures of multivariate dependence. J American Statistical Association, 84, 157-164.

Joe, H. (1988). Majorization, entropy and paired comparisons. Annals of Statistics, 16, 915-925.

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.

Joe, H. (1987). Majorization, randomness and dependence for multivariate distributions. Annals of Probability 15, 1217-1225.

Joe, H. (1987). Estimation of quantiles of the maximum of N observations. Biometrika 74, 347-354.

Joe, H. (1987). An ordering of dependence for distributions of k-tuples, with applications to lotto games. Canadian J Statistics 15, 227-238.

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.

Joe, H. (1985). Characterizations of life distributions from percentile residual lifetimes. Annals Institute Statistical Mathematics, 37, 165-172.

Joe, H. (1985). An ordering of dependence for contingency tables. Linear Algebra and its Applications, Special Statistics Issue 70, 89-103.

Joe, H. and Proschan, F. (1984). Percentile residual life functions. Operations Research 32, 668-678.

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.

Joe, H., Koziol, J.A. and Petkau, A.J. (1981). Comparison of procedures for testing the equality of survival distributions. Biometrics 37, 327-340.