Publications

A list of my publications is also available on ArXiv, Google Scholar, ORCID, and MathSciNet.

Preprints

[29] Heiny, J., Hu, X., and Seo, F.
Phase transitions for the trace of squared sample correlation matrices in high dimension. (2026).

[28] Heiny, J., Jiang, T., Pham, T., and Qi, Y.
Maximum of sparsely equicorrelated Gaussian fields and applications. (2026).
arXiv:2603.05306

[27] Dong, Z., Heiny, J., and Yao, J.
Quadratic form of heavy‑tailed self‑normalized random vector with applications in α‑heavy Marchenko–Pastur law. (2026).
arXiv:2603.07132

[26] Dörnemann, N., Fleermann, M., and Heiny, J.
Ties, tails and spectra: on rank‑based dependency measures in high dimensions. (2025).
arXiv:2508.14992

[25] Heiny, J., and Kleemann, C.
Asymptotic independence of point process and Frobenius norm of a large sample covariance matrix. (2023).
arXiv:2302.13914

Peer‑reviewed publications

[24] Dörnemann, N., and Heiny, J.
Limiting spectral distribution for large sample correlation matrices.
Ann. Appl. Probab. 35 (2025), no. 4, 2570–2603.
arXiv:2208.14948

[23] Heiny, J., and Kleemann, C.
Maximum interpoint distance of high‑dimensional random vectors.
Bernoulli 31 (2025), no. 1, 537–560.
arXiv:2302.06965

[22] Dörr, P., and Heiny, J.
Joint extremes of inversions and descents of random permutations.
Journal of Theoretical Probability 38 (2025), no. 2, Paper No. 42, 37 pp.
arXiv:2309.17314

[21] Bastian, P., Dette, H., and Heiny, J.
Testing for practically significant dependencies in high dimensions via bootstrapping maxima of U‑statistics.
Annals of Statistics 52 (2024), no. 2, 628–653.
arXiv:2210.17439

[20] Parolya, N., Heiny, J., and Kurowicka, D.
Logarithmic law of large random correlation matrices.
Bernoulli 30 (2024), no. 1, 346–370.
arXiv:2103.13900

[19] Heiny, J., and Kleemann, C.
Point process convergence for symmetric functions of high‑dimensional random vectors.
Extremes 27 (2024), no. 2, 185–217.
arXiv:2303.15804

[18] Heiny, J., and Parolya, N.
Log determinant of large correlation matrices under infinite fourth moment.
Ann. Inst. Henri Poincaré Probab. Stat. 60 (2024), no. 2, 1048–1076.
arXiv:2112.15388

[17] Gusakova, A., Heiny, J., and Thäle, C.
The volume of random simplices from elliptical distributions in high dimension.
Stochastic Process. Appl. 164 (2023), 357–382.
arXiv:2206.00514

[16] Fleermann, M., and Heiny, J.
Large sample covariance matrices of Gaussian observations with uniform correlation decay.
Stochastic Process. Appl. 162 (2023), 456–480.
arXiv:2203.04057

[15] Heiny, J., and Yao, J.
Limiting distributions for eigenvalues of sample correlation matrices from heavy‑tailed populations.
Annals of Statistics 50 (2022), no. 6, 3249–3280.
arXiv:2003.03857

[14] Heiny, J.
Large sample correlation matrices: A comparison theorem and its applications.
Electronic Journal of Probability 27 (2022), Paper No. 94, 20 pp.
arXiv:2201.00916

[13] Gösmann, J., Stoehr, C., Heiny, J., and Dette, H.
Sequential change point detection in high‑dimensional time series.
Electronic Journal of Statistics 16 (2022), no. 1, 3608–3671.
arXiv:2006.00636

[12] Heiny, J., Johnston, S., and Prochno, J.
Thin‑shell theory for rotationally invariant random simplices.
Electronic Journal of Probability 27 (2022), no. 2, 1–41.
arXiv:2103.11872

[11] Heiny, J., Mikosch, T., and Yslas, J.
Point process convergence for the off‑diagonal entries of sample covariance matrices.
Ann. Appl. Probab. 31 (2021), no. 2, 538–560.
arXiv:2002.07771

[10] Heiny, J., and Podolskij, M.
On estimation of quadratic variation for multivariate pure jump semimartingales.
Stochastic Process. Appl. 138 (2021), 234–254.
arXiv:2009.02786

[9] Basrak, B., Cho, Y., Heiny, J., and Jung, P.
Extreme eigenvalue statistics of m‑dependent heavy‑tailed matrices.
Ann. Inst. Henri Poincaré Probab. Stat. 57 (2021), 2100–2127.
arXiv:1910.08511

[8] Heiny, J., and Mikosch, T.
Large sample autocovariance matrices of linear processes with heavy tails.
Stochastic Process. Appl. 141 (2021), 344–375.
arXiv:2001.05056

[7] Fleermann, M., and Heiny, J.
High‑dimensional sample covariance matrices with Curie–Weiss entries.
ALEA Lat. Am. J. Probab. Math. Stat. 17 (2020), 857–876.
arXiv:1910.12332

[6] Heiny, J., and Mikosch, T.
The eigenstructure of sample covariance matrices of high‑dimensional stochastic volatility models with heavy tails.
Bernoulli 25 (2019), no. 4B, 3590–3622.
arXiv:2001.04964

[5] Heiny, J.
Random matrix theory for heavy‑tailed time series.
Journal of Mathematical Sciences 237 (2019), no. 5.
pdf

[4] Heiny, J., and Mikosch, T.
Almost sure convergence of the largest and smallest eigenvalues of high‑dimensional sample correlation matrices.
Stochastic Process. Appl. 128 (2018), 2779–2815.
arXiv:2001.11459

[3] Heiny, J., and Mikosch, T.
Eigenvalues and eigenvectors of heavy‑tailed sample covariance matrices with general growth rates: the iid case.
Stochastic Process. Appl. 127 (2017), 2179–2207.
arXiv:1608.06977

[2] Davis, R. A., Heiny, J., Mikosch, T., and Xie, X.
Extreme value analysis for the sample autocovariance matrices of heavy‑tailed multivariate time series.
Extremes 19 (2016), 517–547.
arXiv:1604.07750

[1] Heiny, J.
Random matrix models with heavy tails.
19th European Young Statisticians Meeting, Conference Proceedings (2015).
pdf

Thesis

Heiny, J. Extreme eigenvalues of sample covariance and correlation matrices.
PhD Thesis, University of Copenhagen (2017). pdf

My coauthors

I have had the privilege of collaborating with the following excellent researchers.