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Asymptotic theorems for urn models with nonhomogeneous generating matrices

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  • Bai, Z. D.
  • Hu, Feifang

Abstract

The generalized Friedman's urn (GFU) model has been extensively applied to biostatistics. However, in the literature, all the asymptotic results concerning the GFU are established under the assumption of a homogeneous generating matrix, whereas, in practical applications, the generating matrices are often nonhomogeneous. On the other hand, even for the homogeneous case, the generating matrix is assumed in the literature to have a diagonal Jordan form and satisfies [lambda]>2 Re([lambda]1), where [lambda] and [lambda]1 are the largest eigenvalue and the eigenvalue of the second largest real part of the generating matrix (see Smythe, 1996, Stochastic Process. Appl. 65, 115-137). In this paper, we study the asymptotic properties of the GFU model associated with nonhomogeneous generating matrices. The results are applicable to a variety of settings, such as the adaptive allocation rules with time trends in clinical trials and those with covariates. These results also apply to the case of a homogeneous generating matrix with a general Jordan form as well as the case where [lambda] = 2 Re([lambda]1).

Suggested Citation

  • Bai, Z. D. & Hu, Feifang, 1999. "Asymptotic theorems for urn models with nonhomogeneous generating matrices," Stochastic Processes and their Applications, Elsevier, vol. 80(1), pages 87-101, March.
    • Handle: RePEc:eee:spapps:v:80:y:1999:i:1:p:87-101
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    References listed on IDEAS

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    1. Smythe, R. T., 1996. "Central limit theorems for urn models," Stochastic Processes and their Applications, Elsevier, vol. 65(1), pages 115-137, December.
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    Cited by:

    1. Davidson, Allison & D. Ward, Mark, 2018. "The characterization of tenable Pólya urns," Statistics & Probability Letters, Elsevier, vol. 135(C), pages 38-43.
    2. Wioletta M. Ruszel & Debleena Thacker, 2024. "Positive Reinforced Generalized Time-Dependent Pólya Urns via Stochastic Approximation," Journal of Theoretical Probability, Springer, vol. 37(4), pages 2859-2885, November.
    3. Aletti, Giacomo & Ghiglietti, Andrea, 2017. "Interacting generalized Friedman’s urn systems," Stochastic Processes and their Applications, Elsevier, vol. 127(8), pages 2650-2678.
    4. Yanqing Yi & Yuan Yuan, 2013. "An optimal allocation for response-adaptive designs," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(9), pages 1996-2008, September.
    5. Soumaya Idriss, 2022. "Nonlinear Unbalanced Urn Models via Stochastic Approximation," Methodology and Computing in Applied Probability, Springer, vol. 24(1), pages 413-430, March.
    6. Janson, Svante, 2004. "Functional limit theorems for multitype branching processes and generalized Pólya urns," Stochastic Processes and their Applications, Elsevier, vol. 110(2), pages 177-245, April.
    7. Yuan, Ao & Chai, Gen Xiang, 2008. "Optimal adaptive generalized Polya urn design for multi-arm clinical trials," Journal of Multivariate Analysis, Elsevier, vol. 99(1), pages 1-24, January.
    8. Bai, Z. D. & Hu, Feifang & Shen, Liang, 2002. "An Adaptive Design for Multi-Arm Clinical Trials," Journal of Multivariate Analysis, Elsevier, vol. 81(1), pages 1-18, April.
    9. Li-Xin, Zhang, 2006. "Asymptotic results on a class of adaptive multi-treatment designs," Journal of Multivariate Analysis, Elsevier, vol. 97(3), pages 586-605, March.

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