Articles  

2024

• Aljovin 2., Jara M., Xiang Y.  (2024) Thermalization And Convergence To Equilibrium Of The Noisy Voter Model. https://arxiv.org/abs/2409.05722 

• Auld G., Neammanee K. (2024)  A nonuniform local limit theorem for Poisson binomial random variables via Stein’s method. Journal of Inequalities and Applications, 2024(1), 10.

• Auld G., Neammanee K. (2024) Explicit constants in the nonuniform local limit theorem for Poisson binomial random variables. Journal of Inequalities and Applications, 2024(1), 67.

• Balasubramanian, K., Goldstein, L., Ross, N., & Salim, A. (2024). Gaussian random field approximation via Stein's method with applications to wide random neural networks. Applied and Computational Harmonic Analysis, 72, 101668.

• Barbour A.D., Ross N., Zheng G. (2024) Stein’s method, smoothing and functional approximation. Electronic Journal of Probability 29: (1-29)

• Barman K, Upadhye N.S. (2024). On Stein factors for Laplace approximation and their application to random sums. Statistics & Probability Letters, 206, 109996.

• Burman K., Upadhye N.S., Vellaisamy P. (2024).  Approximations Related to Tempered Stable Distributions. https://arxiv.org/abs/2408.09487 

• Braverman A., Dai J.G., Fang X. (2024) High-Order Steady-State Diffusion Approximations.  Operations Research, 72(2), 604-616.

• Braverman A., Scully Z. (2024).  Stein's method and general clocks: diffusion approximation of the G/G/1 workload. https://arxiv.org/abs/2407.12716  

• Daly, F. (2024) On Stein’s method for stochastically monotone single-birth chains. Statistics & Probability Letters, 206, 109993 

• Fischer A., Gaunt R.E., Swan Y. (2024) Stein's Method of Moments on the Sphere. https://arxiv.org/abs/2407.02299 

• Fulman J. (2024) Beta approximation for the two alleles Moran model by Stein’s method. Statistics & Probability Letters, 208, 110051.

• Gaunt R. (2024) On the product of correlated normal random variables and the noncentral chi-square difference distribution. https://arxiv.org/abs/2408.04101  

• Gaunt, R., Sutcliffe, H. (2024) Improved Bounds in Stein’s Method for Functions of Multivariate Normal Random Vectors. Journal of Theoretical Probability, 37(1), 642-670. 

• Gaunt R., Li S., Sutcliffe H. (2024) A Stein characterisation of the distribution of the product of correlated normal random variables. https://arxiv.org/abs/2402.02264 

• Jaramillo A., Yang X. (2024). Approximation of Smooth Numbers for Harmonic Samples A Stein method Approach. https://arxiv.org/abs/2409.16527 

• Kubokawa T. (2024) Stein’s identities and the related topics: an instructive explanation on shrinkage, characterization, normal approximation and goodness-of-fit. Japanese Journal of Statistics and Data Science, 1-45.

• Kumar A.N., Kumar P. (2024) A negative binomial approximation to the distribution of the sum of maxima of indicator random variables. Statistics & Probability Letters, 208, 110040.

• Leung D., Shao Q.M., Zhang L. (2024). Another Look at Stein’s Method for Studentized Nonlinear Statistics with an Application to U-Statistics. Journal of Theoretical Probability, 1-63.

• Martinez-Taboada D., Ramdas A. (2024) Sequential Kernelized Stein Discrepancy. https://arxiv.org/abs/2409.17505 

• Nik S., Weiss C. (2024) Generalized Moment Estimators Based on Stein Identities. Journal of Statistical Theory and Applications, 1-35. 

• Srikant R. (2024) Rates of Convergence in the Central Limit Theorem for Markov Chains, with an Application to TD Learning. https://arxiv.org/abs/2401.15719 

2023

• Chen P., Nourdin I., Xu L., Yang X. (2023) Multivariate Stable Approximation by Stein’s Method. Journal of Theoretical Probability, 37(1), 446-488 

• Favaro S., Hanin B., Marinucci D., Nourdin I., Peccati G. (2023) Quantitative CLTs in Deep Neural Networks. https://arxiv.org/abs/2307.06092 

2022

• Arenas-Velilla, S., & Joly, E. (2022). On the links between Stein transforms and concentration inequalities for dependent random variables. arXiv preprint arXiv:2211.13211.

• Azmoodeh, E., Eichelsbacher, P., & Thäle, C. (2022). Optimal Variance–Gamma approximation on the second Wiener chaos. Journal of Functional Analysis, 282(11), 109450.

• Azmoodeh, E., Gasbarra, D., & Gaunt, R. E. (2021). An asymptotic approach to proving sufficiency of Stein characterisations. arXiv preprint arXiv:2109.08579.

• Barp, A., Oates, C. J., Porcu, E., & Girolami, M. (2022). A Riemann–Stein kernel method. Bernoulli, 28(4), 2181-2208.

• Betsch, S., Ebner, B., & Nestmann, F. (2022). Characterizations of non-normalized discrete probability distributions and their application in statistics. Electronic Journal of Statistics, 16(1), 1303-1329.

• Bhattacharjee, C., & Schulte, M. (2022). Dickman Approximation of weighted random sums in the Kolmogorov distance. arXiv preprint arXiv:2211.10171.

• Braverman, A. (2022). The prelimit generator comparison approach of Stein’s method. Stochastic Systems, 12(2), 181-204.

• Braverman, A., Dai, J. G., & Fang, X. (2022). High-Order Steady-State Diffusion Approximations. Operations Research.

• Chen, P., Nourdin, I., Xu, L., Yang, X., & Zhang, R. (2022). Non-integrable stable approximation by Stein’s method. Journal of Theoretical Probability, 35(2), 1137-1186.

• Ernst, M., & Swan, Y. (2022). Distances between distributions via Stein’s method. Journal of Theoretical Probability, 35(2), 949-987.

• Fathi, M., Gentil, I., & Serres, J. (2022). Stability estimates for the sharp spectral gap bound under a curvature-dimension condition. arXiv preprint arXiv:2202.03769.

• Fatima, A., & Reinert, G. (2022). Stein's Method for Poisson-Exponential Distributions. arXiv preprint arXiv:2212.09615.

• Fang, X., & Liu, S. H. (2022). Edgeworth Expansion by Stein's Method. arXiv preprint arXiv:2211.04174.

• Fischer, A., Gaunt, R. E., Reinert, G., & Swan, Y. (2022). Normal approximation for the posterior in exponential families. arXiv preprint arXiv:2209.08806.

• Gaunt, R. E. (2022). Stein factors for variance-gamma approximation in the Wasserstein and Kolmogorov distances. Journal of Mathematical Analysis and Applications, 514(1), 126274.

• Gaunt, R. E., & Sutcliffe, H. (2022). Improved bounds in Stein's method for functions of multivariate normal random vectors. arXiv preprint arXiv:2209.09495.

• Houdré, C., & Kerchev, G. (2022). Normal approximation for functions of hidden Markov models. Advances in Applied Probability, 54(2), 536-569.

• Kumar, A. N., Upadhye, N. S., & Vellaisamy, P. (2022). Approximations related to the sums of m-dependent random variables. Brazilian Journal of Probability and Statistics, 36(2), 349-368.

• Lindemulder, N., & Lorist, E. (2022). Stein interpolation for the real interpolation method. Banach Journal of Mathematical Analysis, 16(1), 1-18.

• Liu, X., Gong, K., & Ying, L. (2022). Steady‐state analysis of load balancing with Coxian‐2 distributed service times. Naval Research Logistics (NRL), 69(1), 57-75.

• Mikulincer, D. (2022). A CLT in Stein’s distance for generalized Wishart matrices and higher-order tensors. International Mathematics Research Notices, 2022(10), 7839-7872.

• Shi, J., Zhou, Y., Hwang, J., Titsias, M. K., & Mackey, L. (2022). Gradient Estimation with Discrete Stein Operators. arXiv preprint arXiv:2202.09497.

• Su, Z., & Wang, X. (2022). Approximation of Sums of Locally Dependent Random Variables via Perturbation of Stein Operator. arXiv preprint arXiv:2209.09770.

• Xavier, T. (2022). Goodness of fit tests for Rayleigh distribution. arXiv preprint arXiv:2208.08698.

• Weiß, C. H., & Aleksandrov, B. (2022). Computing (bivariate) Poisson moments using Stein–Chen identities. The American Statistician, 76(1), 10-15.

• Zhu, J. (2022, April). Hessian Estimation via Stein’s Identity in Black-Box Problems. In Mathematical and Scientific Machine Learning (pp. 1161-1178). PMLR.

2021

Lots of articles were published, I was just lagging behind. Sorry about that! 

2020

• Anastasiou, A., & Gaunt, R. E. (2020). Multivariate normal approximation of the maximum likelihood estimator via the delta method. Brazilian Journal of Probability and Statistics, 34(1), 136-149.

• Braverman, A. (2020). Steady-state diffusion approximations of Markov chains: error analysis via the discrete Poisson equation. arXiv preprint arXiv:2001.11151.

• Chen, L. H., Röllin, A., & Xia, A. (2020). Palm theory, random measures and Stein couplings. arXiv preprint arXiv:2004.05026.

• Chen, P., Nourdin, I., & Xu, L. Stein’s Method for Asymmetric α α-stable Distributions, with Application to the Stable CLT. Journal of Theoretical Probability, 1-26.

• Chen, X., & Dey, P. (2020). A note on Stein equation for weighted sums of independent $\chi^{2} $ distributions. arXiv preprint arXiv:2002.09484.

• Cong, T., Xia, A., & Zhang, F. (2020). A large sample property in approximating the superposition of iid finite point processes. Stochastic Processes and their Applications.

• Du, W. (2020). Constructing exchangeable pairs by diffusion on manifolds and its application. arXiv preprint arXiv:2006.09460.

• Fang, X., Gan, H. L., Holmes, S., Huang, H., Peköz, E., Röllin, A., & Tang, W. (2020). Arcsine laws for random walks generated from random permutations with applications to genomics. arXiv preprint arXiv:2001.08857.

• Fang, X., & Koike, Y. (2020). High-dimensional Central Limit Theorems by Stein's Method. arXiv preprint arXiv:2001.10917.

• Fang, X., & Koike, Y. (2020). New error bounds in multivariate normal approximations via exchangeable pairs with applications to Wishart matrices and fourth moment theorems. arXiv preprint arXiv:2004.02101.

• Fathi, M., Goldstein, L., Reinert, G., & Saumard, A. (2020). Relaxing the Gaussian assumption in Shrinkage and SURE in high dimension. arXiv preprint arXiv:2004.01378.

• Gaunt, R. E., & Walton, N. (2020). Stein’s method for the single server queue in heavy traffic. Statistics & Probability Letters, 156, 108566.

• Hurtado-Lange, D., & Maguluri, S. T. (2020). Load balancing system under Join the Shortest Queue: Many-Server-Heavy-Traffic Asymptotics. arXiv preprint arXiv:2004.04826.

• Jin, X., Li, X., & Lu, J. (2020). A kernel bound for non-symmetric stable distribution and its applications. Journal of Mathematical Analysis and Applications, 124063.

• Konzou, E., Koudou, E., & Gneyou, K. E. (2020). Rate of convergence of generalized inverse Gaussian and Kummer distributions to the gamma distribution via Stein’s method. Statistics & Probability Letters, 159, 108683.

• Le, H., Lewis, A., Bharath, K., & Fallaize, C. (2020). A diffusion approach to Stein's method on Riemannian manifolds. arXiv preprint arXiv:2003.11497.

• Lin, W., Li, X., & Wong, A. (2020). Accurate Inference for the Mean of the Poisson-Exponential Distribution. Journal of The Iranian Statistical Society, 19(1), 1-19.

• Mikulincer, D. (2020). A CLT in Stein's distance for generalized Wishart matrices and higher order tensors. arXiv preprint arXiv:2002.10846.

• Thompson, J. (2020). Approximation of Riemannian measures by Stein's method. arXiv preprint arXiv:2001.09910.

• Privault, N., & Serafin, G. (2020). Berry-Esseen bounds for functionals of independent random variables. arXiv preprint arXiv:2010.04387.

• Upadhye, N. S., & Barman, K. (2020). A Unified Approach to Stein's Method for Stable Distributions. arXiv preprint arXiv:2004.07593.

• Xu, W., & Matsuda, T. (2020). A Stein Goodness-of-fit Test for Directional Distributions. arXiv preprint arXiv:2002.06843.

• Zhou, X., & Shroff, N. (2020). A Note on Stein's Method for Heavy-Traffic Analysis. arXiv preprint arXiv:2003.06454.

2019

• Arras, B., & Houdré, C. (2019). On Stein’s method for multivariate self-decomposable laws. Electronic Journal of Probability, 24.

• Azmoodeh, E., Gasbarra, D., & Gaunt, R. E. (2019). On algebraic Stein operators for Gaussian polynomials. arXiv preprint arXiv:1912.04605.

• Barbour, A. D., Röllin, A., & Ross, N. (2019). Error bounds in local limit theorems using Stein’s method. Bernoulli, 25(2), 1076-1104.

• Barbour, A. D., & Xia, A. (2019). Multivariate approximation in total variation using local dependence. Electronic Journal of Probability, 24.

• Betken, C., Döring, H., & Ortgiese, M. (2019). Fluctuations in a general preferential attachment model via Stein's method. Random Structures & Algorithms, 55(4), 808-830.

• Betsch, S., & Ebner, B. (2019). A new characterization of the Gamma distribution and associated goodness-of-fit tests. Metrika, 82(7), 779-806.

• Betsch, S., & Ebner, B. (2019). Fixed point characterizations of continuous univariate probability distributions and their applications. Annals of the Institute of Statistical Mathematics, 1-29.

• Bhattacharjee, C., & Goldstein, L. (2019). Dickman approximation in simulation, summations and perpetuities. Bernoulli, 25(4A), 2758-2792.

• Bourguin, S., & Campese, S. (2019). Approximation of Hilbert-valued Gaussian measures on Dirichlet structures. arXiv preprint arXiv:1905.05127.

• Bresler, G., & Nagaraj, D. (2019). Stein’s method for stationary distributions of Markov chains and application to Ising models. The Annals of Applied Probability, 29(5), 3230-3265.

• Cébron, G., Fathi, M., & Mai, T. (2019). A note on existence of free Stein kernels. Proceedings of the American Mathematical Society.

• Chen, P., Nourdin, I., Xu, L., & Yang, X. (2019). Multivariate stable approximation in Wasserstein distance by Stein's method. arXiv preprint arXiv:1911.12917.

• Chen, P., Nourdin, I., Xu, L., Yang, X., & Zhang, R. (2019). Non-integrable stable approximation by Stein's method. arXiv preprint arXiv:1903.12315.

• Chen, W. Y., Barp, A., Briol, F. X., Gorham, J., Girolami, M., Mackey, L., & Oates, C. (2019). Stein point markov chain monte carlo. arXiv preprint arXiv:1905.03673.

• Courtade, T. A., Fathi, M., & Pananjady, A. (2019). Existence of Stein kernels under a spectral gap, and discrepancy bounds. In Annales de l'Institut Henri Poincaré, Probabilités et Statistiques (Vol. 55, No. 2, pp. 777-790). Institut Henri Poincaré.

• Ernst, M., Reinert, G., & Swan, Y. (2019). First order covariance inequalities via Stein's method. arXiv preprint arXiv:1906.08372.

• Fang, X., Shao, Q. M., & Xu, L. (2019). Multivariate approximations in Wasserstein distance by Stein’s method and Bismut’s formula. Probability Theory and Related Fields, 174(3-4), 945-979.

• Fathi, M. (2019). Stein kernels and moment maps. The Annals of Probability, 47(4), 2172-2185.

• Gaunt, R. E. (2019). Stein’s method and the distribution of the product of zero mean correlated normal random variables. Communications in Statistics-Theory and Methods, 1-6.

• Gaunt, R. E., Mijoule, G., & Swan, Y. (2019). An algebra of Stein operators. Journal of Mathematical Analysis and Applications, 469(1), 260-279.

• Gaunt, R. E., Mijoule, G., & Swan, Y. (2019). Some new Stein operators for product distributions. arXiv preprint arXiv:1901.11460.

• Goldstein, L., & Wei, X. (2019). Non-Gaussian observations in nonlinear compressed sensing via Stein discrepancies. Information and Inference: A Journal of the IMA, 8(1), 125-159.

• Gorham, J., Duncan, A. B., Vollmer, S. J., & Mackey, L. (2019). Measuring sample quality with diffusions. The Annals of Applied Probability, 29(5), 2884-2928.

• Oates, C. J., Cockayne, J., Briol, F. X., & Girolami, M. (2019). Convergence rates for a class of estimators based on Stein’s method. Bernoulli, 25(2), 1141-1159.

• Reinert, G., & Ross, N. (2019). Approximating stationary distributions of fast mixing Glauber dynamics, with applications to exponential random graphs. The Annals of Applied Probability, 29(5), 3201-3229.

• Shao, Q. M., & Zhang, Z. S. (2019). Berry–Esseen bounds of normal and nonnormal approximation for unbounded exchangeable pairs. The Annals of Probability, 47(1), 61-108.

• Saumard, A. (2019). Weighted Poincaré inequalities, concentration inequalities and tail bounds related to Stein kernels in dimension one. Bernoulli, 25(4B), 3978-4006.

• Xu, L. (2019). Approximation of stable law in Wasserstein-1 distance by Stein’s method. The Annals of Applied Probability, 29(1), 458-504.

2018

• Barbour, A. D., Luczak, M. J., & Xia, A. (2018). Multivariate approximation in total variation, I: Equilibrium distributions of Markov jump processes. The Annals of Probability, 46(3), 1351-1404.

• Barbour, A. D., Luczak, M. J., & Xia, A. (2018). Multivariate approximation in total variation, II: Discrete normal approximation. The Annals of Probability, 46(3), 1405-1440.

• Besançon, E., Decreusefond, L., & Moyal, P. (2018). Stein's method for diffusive limit of Markov processes. arXiv preprint arXiv:1805.01691.

• Chen, W. Y., Mackey, L., Gorham, J., Briol, F. X., & Oates, C. J. (2018). Stein points. arXiv preprint arXiv:1803.10161.

• Decreusefond, L., & Vasseur, A. (2018). Stein's method and Papangelou intensity for Poisson or Cox process approximation. arXiv preprint arXiv:1807.02453.

• Ebner, B., Henze, N., Klatt, M. A., & Mecke, K. (2018). Goodness-of-fit tests for complete spatial randomness based on Minkowski functionals of binary images. Electronic Journal of Statistics, 12(2), 2873-2904.

• Fathi, M. (2018). Higher-order Stein kernels for Gaussian approximation. arXiv preprint arXiv:1812.02703.

• Gallouët, T., Mijoule, G., & Swan, Y. (2018). Regularity of solutions of the Stein equation and rates in the multivariate central limit theorem. arXiv preprint arXiv:1805.01720.

• Gaunt, R. E. (2018). A note on Stein’s method on the third and fourth Wiener chaoses. arXiv preprint arXiv:1805.08830.

• Gaunt, R. E. (2018). Products of normal, beta and gamma random variables: Stein operators and distributional theory. Brazilian Journal of Probability and Statistics, 32(2), 437-466.

• Gaunt, R. E. (2018). Wasserstein and Kolmogorov error bounds for variance-gamma approximation via Stein’s method I. Journal of Theoretical Probability, 1-41.

• Goldstein, L. (2018). Non-asymptotic distributional bounds for the Dickman approximation of the running time of the Quickselect algorithm. Electronic Journal of Probability, 23.

• Goldstein, L., & Wiroonsri, N. (2018). Stein’s method for positively associated random variables with applications to the Ising and voter models, bond percolation, and contact process. In Annales de l'Institut Henri Poincaré, Probabilités et Statistiques (Vol. 54, No. 1, pp. 385-421). Institut Henri Poincaré.

• Privault, N., & Serafin, G. (2018). Stein approximation for functionals of independent random sequences. Electronic Journal of Probability, 23.

2017

• Anastasiou, A., & Reinert, G. (2017). Bounds for the normal approximation of the maximum likelihood estimator. Bernoulli, 23(1), 191-218.

• Arras, B., Azmoodeh, E., Poly, G., & Swan, Y. (2017). Stein characterizations for linear combinations of gamma random variables. arXiv preprint arXiv:1709.01161.

• Braverman, A., Dai, J. G., & Feng, J. (2017). Stein’s method for steady-state diffusion approximations: an introduction through the Erlang-A and Erlang-C models. Stochastic Systems, 6(2), 301-366.

• Chatterjee, S., & Sen, S. (2017). Minimal spanning trees and Stein’s method. The Annals of Applied Probability, 27(3), 1588-1645.

• Döbler, C., Gaunt, R. E., & Vollmer, S. J. (2017). An iterative technique for bounding derivatives of solutions of Stein equations. Electronic Journal of Probability, 22.

• Fathi, M., & Nelson, B. (2017). Free Stein kernels and an improvement of the free logarithmic Sobolev inequality. Advances in Mathematics, 317, 193-223.

• Gan, H. L., Röllin, A., & Ross, N. (2017). Dirichlet approximation of equilibrium distributions in Cannings models with mutation. Advances in Applied Probability, 49(3), 927-959.

• Gaunt, R. E. (2017). A Stein characterisation of the generalized hyperbolic distribution. ESAIM: Probability and Statistics, 21, 303-316.

• Gaunt, R. E. (2017). On Stein’s method for products of normal random variables and zero bias couplings. Bernoulli, 23(4B), 3311-3345.

• Gorham, J., & Mackey, L. (2017, August). Measuring sample quality with kernels. In Proceedings of the 34th International Conference on Machine Learning-Volume 70 (pp. 1292-1301). JMLR. org.

• Gaunt, R. E., Pickett, A. M., & Reinert, G. (2017). Chi-square approximation by Stein’s method with application to Pearson’s statistic. The Annals of Applied Probability, 27(2), 720-756.

• Goldstein, L., Nourdin, I., & Peccati, G. (2017). Gaussian phase transitions and conic intrinsic volumes: Steining the Steiner formula. The Annals of Applied Probability, 27(1), 1-47.

• Kasprzak, M. J. (2017). Multivariate functional approximations with Stein's method of exchangeable pairs. arXiv preprint arXiv:1710.09263.

• Kasprzak, M. J. (2017). Stein's method for multivariate Brownian approximations of sums under dependence. arXiv preprint arXiv:1708.02521.

• Kasprzak, M. J., Duncan, A. B., & Vollmer, S. J. (2017). Note on A. Barbour’s paper on Stein’s method for diffusion approximations. Electronic Communications in Probability, 22.

• Kumar, A. N., & Upadhye, N. S. (2017). On perturbations of Stein operator. Communications in Statistics-Theory and Methods, 46(18), 9284-9302.

• Peköz, E., Röllin, A., & Ross, N. (2017). Joint degree distributions of preferential attachment random graphs. Advances in Applied Probability, 49(2), 368-387.

• Röllin, A. (2017). Kolmogorov bounds for the normal approximation of the number of triangles in the Erdos-Rényi random graph. arXiv preprint arXiv:1704.00410.

• Upadhye, N. S., Čekanavičius, V., & Vellaisamy, P. (2017). On Stein operators for discrete approximations. Bernoulli, 23(4A), 2828-2859.

• Yang, Z., Balasubramanian, K., Wang, Z., & Liu, H. (2017). Learning non-gaussian multi-index model via second-order stein’s method. Advances in Neural Information Processing Systems, 30, 6097-6106.

• Ying, L. (2017). Stein's method for mean field approximations in light and heavy traffic regimes. Proceedings of the ACM on Measurement and Analysis of Computing Systems, 1(1), 1-27.

2016

• Arras, B., Azmoodeh, E., Poly, G., & Swan, Y. (2016). Stein's method on the second Wiener chaos: 2-Wasserstein distance. arXiv preprint arXiv:1601.03301.

• Bhattacharjee, C., & Goldstein, L. (2016). On strong embeddings by Stein’s method. Electronic Journal of Probability, 21.

• Coulson, M., Gaunt, R. E., & Reinert, G. (2016). Poisson approximation of subgraph counts in stochastic block models and a graphon model. ESAIM: Probability and Statistics, 20, 131-142.

• Gaunt, R. E. (2016). Rates of convergence in normal approximation under moment conditions via new bounds on solutions of the Stein equation. Journal of Theoretical Probability, 29(1), 231-247.

• Ley, C., & Swan, Y. (2016). Parametric Stein operators and variance bounds. Brazilian Journal of Probability and Statistics, 30(2), 171-195.

• Liu, Q., & Wang, D. (2016). Stein variational gradient descent: A general purpose bayesian inference algorithm. In Advances in neural information processing systems (pp. 2378-2386).

• Liu, Q., Lee, J., & Jordan, M. (2016, June). A kernelized Stein discrepancy for goodness-of-fit tests. In International conference on machine learning (pp. 276-284).

• Peköz, E. A., Röllin, A., & Ross, N. (2016). Generalized gamma approximation with rates for urns, walks and trees. The Annals of Probability, 44(3), 1776-1816.

• Shao, Q. M., & Zhang, Z. S. (2016). Identifying the limiting distribution by a general approach of Stein’s method. Science China Mathematics, 59(12), 2379-2392.

2010-2015

• Bonis, T. (2015). Rates in the Central Limit Theorem and diffusion approximation via Stein's Method. arXiv preprint arXiv:1506.06966.

• Barbour, A. D., Gan, H. L., & Xia, A. (2015). Stein factors for negative binomial approximation in Wasserstein distance. Bernoulli, 21(2), 1002-1013.

• Chen, L. H., & Poly, G. (2015). Stein's method, Malliavin calculus, Dirichlet forms and the fourth moment theorem. In Festschrift Masatoshi Fukushima: In Honor of Masatoshi Fukushima's Sanju (pp. 107-130).

• Daly, F., & Gaunt, R. E. (2015). The Conway-Maxwell-Poisson distribution: distributional theory and approximation. arXiv preprint arXiv:1503.07012.

• Döbler, C. (2015). Stein's method of exchangeable pairs for the Beta distribution and generalizations. Electronic Journal of Probability, 20.

• Eichelsbacher, P., & Martschink, B. (2015). On rates of convergence in the Curie–Weiss–Potts model with an external field. In Annales de l'IHP Probabilités et statistiques (Vol. 51, No. 1, pp. 252-282).

• Eichelsbacher, P., & Thäle, C. (2015). Malliavin-Stein method for Variance-Gamma approximation on Wiener space. Electronic Journal of Probability, 20.

• Fulman, J., & Goldstein, L. (2015). Stein’s method and the rank distribution of random matrices over finite fields. The Annals of Probability, 43(3), 1274-1314.

• Gan, H. L., & Xia, A. (2015). Stein’s method for conditional compound Poisson approximation. Statistics & Probability Letters, 100, 19-26.

• Gaunt, R. E. (2015). Stein's method for functions of multivariate normal random variables. arXiv preprint arXiv:1507.08688.

• Gorham, J., & Mackey, L. (2015). Measuring sample quality with Stein's method. In Advances in Neural Information Processing Systems (pp. 226-234).

• Ledoux, M., Nourdin, I., & Peccati, G. (2015). Stein’s method, logarithmic Sobolev and transport inequalities. Geometric and Functional Analysis, 25(1), 256-306.

• Privault, N., & Torrisi, G. L. (2015). The Stein and Chen-Stein methods for functionals of non-symmetric Bernoulli processes. ALEA Lat. Am. J. Probab. Math. Stat, 12(1), 309-356.

• Fang, X. (2014). Discretized normal approximation by Stein’s method. Bernoulli, 20(3), 1404-1431.

• Fulman, J., & Goldstein, L. (2014). Stein's method, semicircle distribution, and reduced decompositions of the longest element in the symmetric group. arXiv preprint arXiv:1405.1088.

• Gaunt, R. (2014). Variance-Gamma approximation via Stein's method. Electronic Journal of Probability, 19.

• Janzamin, M., Sedghi, H., & Anandkumar, A. (2014). Score function features for discriminative learning: Matrix and tensor framework. arXiv preprint arXiv:1412.2863.

• Mackey, L., Jordan, M. I., Chen, R. Y., Farrell, B., & Tropp, J. A. (2014). Matrix concentration inequalities via the method of exchangeable pairs. The Annals of Probability, 42(3), 906-945.

• Koudou, A. E., & Ley, C. (2014). Characterizations of GIG laws: A survey. Probability surveys, 11, 161-176.

• Nourdin, I., Peccati, G., & Swan, Y. (2014). Entropy and the fourth moment phenomenon. Journal of Functional Analysis, 266(5), 3170-3207.

• Nourdin, I., Peccati, G., & Swan, Y. (2014, June). Integration by parts and representation of information functionals. In 2014 IEEE International Symposium on Information Theory (pp. 2217-2221). IEEE.

• Schuhmacher, D., & Stucki, K. (2014). Gibbs point process approximation: Total variation bounds using Stein’s method. The Annals of Probability, 42(5), 1911-1951.

• Chen, L. H., Fang, X., & Shao, Q. M. (2013). From Stein identities to moderate deviations. The Annals of Probability, 41(1), 262-293.

• Chen, L. H., & Röllin, A. (2013). Approximating dependent rare events. Bernoulli, 19(4), 1243-1267.

• Daly, F. (2013). Compound Poisson approximation with association or negative association via Stein's method. Electronic Communications in Probability, 18.

• Döbler, C. (2013). Stein's method for the half-normal distribution with applications to limit theorems related to the simple symmetric random walk. arXiv preprint arXiv:1303.4592.

• Formanov, S. K., & Formanova, T. A. (2013). The Stein-Tikhomirov Method and Berry-Esseen Inequality for Sampling Sums from a Finite Population of Independent Random Variables. In Prokhorov and Contemporary Probability Theory (pp. 261-273). Springer, Berlin, Heidelberg.

• Goldstein, L. (2013). A Berry–Esseen bound with applications to vertex degree counts in the Erdős–Rényi random graph. The Annals of Applied Probability, 23(2), 617-636.

• Goldstein, L., & Reinert, G. (2013). Stein's method for the Beta distribution and the Polya-Eggenberger urn. Journal of Applied Probability, 50(4), 1187-1205.

• Ley, C., & Swan, Y. (2013). Stein's density approach and information inequalities. Electronic Communications in Probability, 18.

• Peköz, E. A., Röllin, A., & Ross, N. (2013). Degree asymptotics with rates for preferential attachment random graphs. The Annals of Applied Probability, 23(3), 1188-1218.

• Peköz, E. A., Röllin, A., & Ross, N. (2013). Total variation error bounds for geometric approximation. Bernoulli, 19(2), 610-632.

• Ross, N. (2013). Power laws in preferential attachment graphs and Stein's method for the negative binomial distribution. Advances in Applied Probability, 45(3), 876-893.

• Coutin, L., & Decreusefond, L. (2012). Stein's method for Brownian approximations. arXiv preprint arXiv:1207.3517.

• Daly, F., Lefèvre, C., & Utev, S. (2012). Stein's method and stochastic orderings. Advances in Applied Probability, 44(2), 343-372.

• Döbler, C. (2012). A rate of convergence for the arcsine law by Stein's method. arXiv preprint arXiv:1207.2401.

• Fulman, J. (2012). Stein's method, heat kernel, and traces of powers of elements of compact Lie groups. Electronic Journal of Probability, 17.

• Fulman, J., & Ross, N. (2012). Exponential approximation and Stein's method of exchangeable pairs. ALEA Lat. Am. J. Probab. Math. Stat., 10(1):1-13.

• Kusuoka, S., & Tudor, C. A. (2012). Stein’s method for invariant measures of diffusions via Malliavin calculus. Stochastic Processes and their Applications, 122(4), 1627-1651.

• Sason, I. (2012, September). On the entropy of sums of bernoulli random variables via the chen-stein method. In 2012 IEEE Information Theory Workshop (pp. 542-546). IEEE.

• Pike, J., & Ren, H. (2012). Stein's method and the Laplace distribution. arXiv preprint arXiv:1210.5775.

• Chatterjee, S., Fulman, J., & Röllin, A. (2011). Exponential approximation by Stein’s method and spectral graph theory. ALEA Lat. Am. J. Probab. Math. Stat, 8, 197-223.

• Chatterjee, S., & Shao, Q. M. (2011). Nonnormal approximation by Stein’s method of exchangeable pairs with application to the Curie–Weiss model. The Annals of Applied Probability, 21(2), 464-483.

• Döbler, C., & Stolz, M. (2011). Stein's Method and the Multivariate CLT for Traces of Powers on the Compact Classical Groups. Electronic Journal of Probability, 16, 2375-2405.

• Ghosh, S., & Goldstein, L. (2011). Concentration of measures via size-biased couplings. Probability theory and related fields, 149(1-2), 271-278.

• Peköz, E. A., & Röllin, A. (2011). New rates for exponential approximation and the theorems of Rényi and Yaglom. The Annals of Probability, 39(2), 587-608.

• Chatterjee, S., & Dey, P. S. (2010). Applications of Stein’s method for concentration inequalities. The Annals of Probability, 38(6), 2443-2485.

• Daly, F. (2010). Stein's method for compound geometric approximation. Journal of applied probability, 47(1), 146-156.

• Goldstein, L. (2010). Bounds on the constant in the mean central limit theorem. The Annals of Probability, 38(4), 1672-1689.

• Nourdin, I., Peccati, G., & Réveillac, A. (2010). Multivariate normal approximation using Stein's method and Malliavin calculus. In Annales de l'IHP Probabilités et statistiques (Vol. 46, No. 1, pp. 45-58).

• Peccati, G., Solé, J. L., Taqqu, M. S., & Utzet, F. (2010). Stein’s method and normal approximation of Poisson functionals. The Annals of Probability, 38(2), 443-478.

• Pekoz, E., Rollin, A., & Ross, N. (2010). Total variation and local limit error bounds for geometric approximation. Bernoulli.

• Teerapabolarn, K. (2010). Non Uniform Bounds on Geometric Approximation Via Stein's Method and w-Functions. Communications in Statistics—Theory and Methods, 40(1), 145-158.

2000-2009

• Eichelsbacher, P., & Löwe, M. (2009). Stein's method for dependent random variables occuring in statistical mechanics (No. arXiv: 0908.1909). MFO.

• El Karoui, N., & Jiao, Y. (2009). Stein’s method and zero bias transformation for CDO tranche pricing. Finance and Stochastics, 13(2), 151-180.

• Gan, L., Ling, C., Do, T. T., & Tran, T. D. (2009). Analysis of the statistical restricted isometry property for deterministic sensing matrices using Stein’s method. preprint, 190.

• Meckes, E. (2009). On Stein’s method for multivariate normal approximation. In High dimensional probability V: the Luminy volume (pp. 153-178). Institute of Mathematical Statistics.

• Nourdin, I., & Peccati, G. (2009). Stein’s method on Wiener chaos. Probability Theory and Related Fields, 145(1-2), 75-118.

• Nourdin, I., & Peccati, G. (2009). Stein’s method and exact Berry–Esseen asymptotics for functionals of Gaussian fields. The Annals of Probability, 37(6), 2231-2261.

• Peköz, E. A., Röllin, A., Čekanavičius, V., & Shwartz, M. (2009). A three-parameter binomial approximation. Journal of applied probability, 46(4), 1073-1085.

• Reinert, G., & Röllin, A. (2009). Multivariate normal approximation with Stein’s method of exchangeable pairs under a general linearity condition. The Annals of Probability, 37(6), 2150-2173.

• Ross, N. (2009). Step size in stein's method of exchangeable pairs. Combinatorics, Probability and Computing, 18(6), 979-1017.

• Schuhmacher, D. (2009). Stein’s method and Poisson process approximation for a class of Wasserstein metrics. Bernoulli, 15(2), 550-568.

• Chatterjee, S. (2008). A new method of normal approximation. The Annals of Probability, 36(4), 1584-1610.

• Chatterjee, S., & Meckes, E. (2008). Multivariate normal approximation using exchangeable pairs. ALEA Lat. Am. J. Probab. Math. Stat. 4, 257–283 .

• Daly, F. (2008). Upper bounds for Stein-type operators. Electronic Journal of Probability, 13, 566-587.

• Eichelsbacher, P., & Reinert, G. (2008). Stein’s method for discrete Gibbs measures. The Annals of Applied Probability, 18(4), 1588-1618.

• Röllin, A. (2008). A note on the exchangeability condition in Stein’s method. Statistics & probability letters, 78(13), 1800-1806.

• Raič, M. (2007). CLT-related large deviation bounds based on Stein's method. Advances in Applied Probability, 39(3), 731-752.

• Röllin, A. (2007). Translated Poisson approximation using exchangeable pair couplings. The Annals of Applied Probability, 17(5/6), 1596-1614.

• Barbour, A. D., & Xia, A. (2006). On Stein's factors for Poisson approximation in Wasserstein distance. Bernoulli, 943-954.

• Chatterjee, S. (2006). Stein's method for concentration inequalities. arXiv preprint math/0604352.

• Goldstein, L., & Xia, A. (2006). Zero biasing and a discrete central limit theorem. The Annals of Probability, 34(5), 1782-1806.

• Barbour, A. D. (2005). Multivariate Poisson-binomial approximation using Stein’s. Stein's Method and Applications, 5, 131.

• Chatterjee, S., Diaconis, P., & Meckes, E. (2005). Exchangeable pairs and Poisson approximation. Probability Surveys, 2, 64-106.

• Fulman, J. (2005). Stein’s method and Plancherel measure of the symmetric group. Transactions of the American Mathematical Society, 357(2), 555-570.

• Fulman, J. (2005). Stein's method and descents after riffle shuffles. Electronic Journal of Probability, 10, 901-924.

• Goldstein, L. (2005). Berry-Esseen bounds for combinatorial central limit theorems and pattern occurrences, using zero and size biasing. Journal of applied probability, 42(3), 661-683.

• Goldstein, L., & Reinert, G. (2005). Distributional transformations, orthogonal polynomials, and Stein characterizations. Journal of Theoretical Probability, 18(1), 237-260.

• Goldstein, L., & Reinert, G. (2005). Zero biasing in one and higher dimensions, and applications. Stein’s method and applications, 5, 1-18.

• Götze, F., & Tikhomirov, A. (2005). Limit theorems for spectra of random matrices with martingale structure. Stein’s method and applications, 5, 181-193.

• Götze, F., & Tikhomirov, A. (2005). The rate of convergence for spectra of GUE and LUE matrix ensembles. Open Mathematics, 3(4), 666-704.

• Xia, A. (2005). Stein’s method and Poisson process approximation. An introduction to Stein’s method, 4, 115-181.

• Chen, L. H., & Shao, Q. M. (2004). Normal approximation under local dependence. The Annals of Probability, 32(3), 1985-2028.

• Chen, L. H., & Xia, A. (2004). Stein’s method, Palm theory and Poisson process approximation. The Annals of Probability, 32(3B), 2545-2569.

• Diaconis, P. (2004). Stein’s method for Markov chains: first examples. In Stein's Method (pp. 26-41). Institute of Mathematical Statistics.

• Fulman, J. (2004). Stein’s method and non-reversible Markov chains. In Stein's Method (pp. 66-74). Institute of Mathematical Statistics.

• Goldstein, L. (2004). Normal approximation for hierarchical structures. The Annals of Applied Probability, 14(4), 1950-1969.

• Holmes, S. (2004). Stein’s method for birth and death chains. In Stein's Method (pp. 42-65). Institute of Mathematical Statistics.

• Holmes, S., & Reinert, G. (2004). Stein’s method for the bootstrap. In Stein's Method (pp. 93-132). Institute of Mathematical Statistics.

• Stein, C., Diaconis, P., Holmes, S., & Reinert, G. (2004). Use of exchangeable pairs in the analysis of simulations. In Stein's Method (pp. 1-25). Institute of Mathematical Statistics.

• Raic, M. (2003). Normal approximation by Stein’s method. In Proceedings of the 7th Young Statisticians Meeting (pp. 71-97).

• Nicoleris, T., & Sagris, A. (2002). Random function prediction and Stein's identity. Statistics & probability letters, 59(3), 293-305.

• Arnold, B. C., Castillo, E., & Sarabia, J. M. (2001). A multivariate version of Stein's identity with applications to moment calculations and estimation of conditionally specified distributions.

• Barbour, A. D., Chryssaphinou, O., & Vaggelatou, E. (2001). Applications of compound Poisson approximation. Probability and Statistical Models with Applications, 41-62.

• Brown, T. C., & Xia, A. (2001). Stein's method and birth-death processes. Annals of probability, 1373-1403.

• Chen, L. H., & Shao, Q. M. (2001). A non-uniform Berry–Esseen bound via Stein's method. Probability theory and related fields, 120(2), 236-254.

• Diaconis, P., Graham, R., & Holmes, S. P. (2001). Statistical problems involving permutations with restricted positions. Lecture Notes-Monograph Series, 195-222.

• Schoutens, W. (2001). Orthogonal polynomials in Stein's method. Journal of mathematical analysis and applications, 253(2), 515-531.

• Chen, L. H. (2000). Non-uniform bounds in probability approximations using Stein’s method. Probability and statistical models with applications: a volume in honor of Theophilos Cacoullos, Eds: N. Balakrishnan, Ch. A. Charalambides & MV Koutras, 3-14.

• Papadatos, N., & Papathanasiou, V. (2000). Stein-Type Identity. Probability and Statistical Models with Applications, 87.

• Reinert, G. (2000). Stein's Method for Epidemic Processes. Complex Stochastic Systems, 235-275.

1990-1999

• Brown, T. C., & Phillips, M. J. (1999). Negative binomial approximation with Stein's method. Methodology and computing in applied probability, 1(4), 407-421.

• Erhardsson, T. (1999). Compound Poisson approximation for Markov chains using Stein’s method. The Annals of Probability, 27(1), 565-596.

• Reinert, G. (1998). Couplings for normal approximations with Stein’s method. DIMACS Ser. Discrete Math. Theoret. Comput. Sci, 41, 193-207.

• Goldstein, L., & Reinert, G. (1997). Stein's method and the zero bias transformation with application to simple random sampling. The Annals of Applied Probability, 7(4), 935-952.

• Penrose, M. D. (1997). The longest edge of the random minimal spanning tree. The annals of applied probability, 340-361.

• Rinott, Y., & Rotar, V. (1997). On coupling constructions and rates in the CLT for dependent summands with applications to the antivoter model and weighted U-statistics. The Annals of Applied Probability, 1080-1105.

• Xia, A. (1997). On using the first difference in the Stein-Chen method. The Annals of Applied Probability, 899-916.

• Xia, A. (1997). On the rate of Poisson process approximation to a Bernoulli process. Journal of Applied Probability, 34(4), 898-907.

• Dembo, A., & Rinott, Y. (1996). Some examples of normal approximations by Stein’s method. In Random discrete structures (pp. 25-44). Springer, New York, NY.

• Goldstein, L., & Rinott, Y. (1996). Multivariate normal approximations by Stein's method and size bias couplings. Journal of Applied Probability, 33(1), 1-17.

• Peköz, E. A. (1996). Stein's method for geometric approximation. Journal of applied probability, 33(3), 707-713.

• Soon, S. Y. (1996). Binomial approximation for dependent indicators. Statistica Sinica, 703-714.

• Reinert, G. (1995). A weak law of large numbers for empirical measures via Stein's method. The Annals of Probability, 334-354.

• Cacoullos, T., Papathanasiou, V., & Utev, S. A. (1994). Variational inequalities with examples and an application to the central limit theorem. The Annals of Probability, 22(3), 1607-1618.

• Roos, M. (1994). Stein's method for compound Poisson approximation: the local approach. The Annals of Applied Probability, 4(4), 1177-1187.

• Koutras, M. V., & Papastavridis, S. G. (1993). Application of the stein‐chen method for bounds and limit theorems in the reliability of coherent structures. Naval Research Logistics (NRL), 40(5), 617-631.

• Barbour, A. D., Chen, L. H., & Loh, W. L. (1992). Compound Poisson approximation for nonnegative random variables via Stein's method. The Annals of Probability, 1843-1866.

• Barbour, A. D., & Brown, T. C. (1992). The Stein-Chen method, point processes and compensators. The Annals of Probability, 20(3), 1504-1527.

• Loh, W. L. (1992). Stein's method and multinomial approximation. The Annals of Applied Probability, 536-554.

• Ehm, W. (1991). Binomial approximation to the Poisson binomial distribution. Statistics & Probability Letters, 11(1), 7-16.

• Gotze, F. (1991). On the rate of convergence in the multivariate CLT. The Annals of Probability, 724-739.

• Loh, W. L. (1991). Estimating covariance matrices. The Annals of Statistics, 283-296.

• Loh, W. L. (1991). Estimating covariance matrices II. Journal of multivariate analysis, 36(2), 163-174.

• Barbour, A. D. (1990). Stein's method for diffusion approximations. Probability theory and related fields, 84(3), 297-322.

1980-1989

• Arratia, R., Goldstein, L., & Gordon, L. (1989). Two moments suffice for Poisson approximations: the Chen-Stein method. The Annals of Probability, 17(1), 9-25.

• Barbour, A. D., Karoński, M., & Ruciński, A. (1989). A central limit theorem for decomposable random variables with applications to random graphs. Journal of Combinatorial Theory, Series B, 47(2), 125-145.

• Gillett, R. (1989). Confidence interval construction by Stein's method: a practical and economical approach to sample size determination. Journal of marketing research, 26(2), 237-240.

• Barbour, A. D. (1988). Stein's method and Poisson process convergence. Journal of Applied Probability, 25(A), 175-184.

• Deheuvels, P., & Pfeifer, D. (1988). On a relationship between Uspensky's theorem and Poisson approximations. Annals of the Institute of Statistical Mathematics, 40(4), 671-681.

• Barbour, A. D. (1987). Asymptotic expansions in the Poisson limit theorem. The Annals of Probability, 748-766.

• Barbour, A. D., & Hall, P. (1984, May). On the rate of Poisson convergence. In Mathematical Proceedings of the Cambridge Philosophical Society (Vol. 95, No. 3, pp. 473-480). Cambridge University Press.

• Bickel, P. J., & Robinson, J. (1982). Edgeworth expansions and smoothness. The Annals of Probability, 500-503.

• Bolthausen, E. (1982). On the central limit theorem for stationary mixing random fields. The Annals of Probability, 1047-1050.

• Tikhomirov, A. N. (1980). On the rate of convergence in the central limit theorem for weakly dependent random variables. Teoriya Veroyatnostei i ee Primeneniya, 25(4), 800-818.

1970-1979

• Ho, S. T., & Chen, L. H. (1978). An L_p bound for the remainder in a combinatorial central limit theorem. The Annals of Probability, 231-249.

• Hudson, H. M. (1978). A natural identity for exponential families with applications in multiparameter estimation. The Annals of Statistics, 6(3), 473-484.

• Diaconis, P. (1977). The distribution of leading digits and uniform distribution mod 1. The Annals of Probability, 5(1), 72-81.

• Chen, L. H. (1975). Poisson approximation for dependent trials. The Annals of Probability, 534-545.

• Stein, C. (1972). A bound for the error in the normal approximation to the distribution of a sum of dependent random variables. In Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability, Volume 2: Probability Theory. The Regents of the University of California.