Review Articles

Rates of convergence of powered order statistics from general error distribution

Yuhan Zou ,

School of Mathematics and Statistics, Southwest University, Chongqing, People's Republic of China

Yingyin Lu ,

School of Science, Southwest Petroleum University, Chengdu, People's Republic of China

Zuoxiang Peng

School of Mathematics and Statistics, Southwest University, Chongqing, People's Republic of China

pzx@swu.edu.cn

Pages | Received 03 May. 2022, Accepted 04 Nov. 2022, Published online: 21 Nov. 2022,
  • Abstract
  • Full Article
  • References
  • Citations

Let {Xn,n≥1}{Xn,n≥1} be a sequence of independent random variables with common general error distribution GED(v)GED(v) with shape parameter v>0, and let Mn,rMn,r denote the rth largest order statistics of X1,X2,…,XnX1,X2,…,Xn. With different normalizing constants the distributional expansions and the uniform convergence rates of normalized powered order statistics |Mn,r|p|Mn,r|p are established. An alternative method is presented to estimate the probability of the rth extremes. Numerical analyses are provided to support the main results.

Your browser may not support PDF viewing. Please click to download the file.

  • Cao, W., & Zhang, Z. (2021). New extreme value theory for maxima of maxima. Statistical Theory and Related Fields5(3), 232–252. https://doi.org/10.1080/24754269.2020.1846115 
  • Hall, P. (1979). On the rate of convergence of normal extremes. Journal of Applied Probability16(2), 433–439. https://doi.org/10.2307/3212912 
  • Hall, P. (1980). Estimating probabilities for normal extremes. Advances in Applied Probability12(2), 491–500. https://doi.org/10.2307/1426608 
  • Hashorva, E., Peng, Z., & Weng, Z. (2016). Higher-order expansions of distributions of maxima in a Hüsler-Reiss model. Methodology and Computing in Applied Probability18(1), 181–196. https://doi.org/10.1007/s11009-014-9407-6 
  • Jia, P., & Li, T. (2014). Higher-order expansions for distributions of extremes from general error distribution. Journal of Inequalities and Applications2014, 213. https://doi.org/10.1186/1029-242X-2014-213 
  • Leadbetter, M. R., Lindgren, G., & Rootzén, H. (1983). Extremes and related properties of random sequences and processes. Springer Verlag. 
  • Li, T., & Peng, Z. (2018). Moment convergence of powered normal extremes. Communication in Statistics-Theory and Methods47(14), 3453–3463. https://doi.org/10.1080/03610926.2017.1359294 
  • Liao, X., Peng, L., Peng, Z., & Zheng, Y. (2016). Dynamic bivariate normal copula. Science China Mathematics59(5), 955–976. https://doi.org/10.1007/s11425-015-5114-1 
  • Liao, X., & Peng, Z. (2012). Convergence rates of limit distribution of maxima of lognormal samples. Journal of Mathematical Analysis and Applications395(2), 643–653. https://doi.org/10.1016/j.jmaa.2012.05.077 
  • Liao, X., & Peng, Z. (2014). Convergence rate of maxima of bivariate gaussian arrays to the Hüsler–Reiss distribution. Statistics and Its Interface7(3), 351–362. https://doi.org/10.4310/SII.2014.v7.n3.a5 
  • Liao, X., & Peng, Z. (2015). Asymptotics for the maxima and minima of Hüsler–Reiss bivariate gaussian arrays. Extremes18, 1–14. https://doi.org/10.1007/s10687-014-0196-7 
  • Liao, X., Peng, Z., & Nadarajah, S. (2014a). Tail behavior and limit distribution of maximum of logarithmic general error distribution. Communications in Statistics-Theory and Methods43(24), 5276–5289. https://doi.org/10.1080/03610926.2012.730168
  • Liao, X., Peng, Z., Nadarajah, S., & Wang, X. (2014b, January). Rates of convergence of extremes from skew-normal samples. Statistics and Probability Letters84, 40–47. https://doi.org/10.1016/j.spl.2013.09.027 
  • Lu, Y., & Peng, Z. (2017). Maxima and minima of independent and non-identically distributed bivariate Gaussian triangular arrays. Extremes20, 187–198. https://doi.org/10.1007/s10687-016-0263-3 
  • Nair, K. A. (1981). Asymptotic distribution and moments of normal extremes. Annals of Probability9(1), 150–153. https://doi.org/10.1214/aop/1176994515 
  • Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica59(2), 347–370. https://doi.org/10.2307/2938260 
  • Peng, Z., Nadarajah, S., & Lin, F. (2010). Convergence rate of extremes for the general error distribution. Journal of Applied Probability47(3), 668–679. https://doi.org/10.1239/jap/1285335402 
  • Peng, Z., Tong, B., & Nadarajah, S. (2009). Tail behavior of the general error distribution. Communications in Statistics-Thoery and Methods38(11), 1884–1892. https://doi.org/10.1080/03610920802478367 
  • Resnick, S. I. (1987). Extreme values, regular variation and point processes. Springer-Verlag. 
  • Zhang, Z. (2021). On studying extreme values and systematic risks with nonlinear time series models and tail dependence measures. Statistical Theory and Related Fields5(1), 1–25. https://doi.org/10.1080/24754269.2020.1856590
  • Zhou, W., & Ling, C. (2016, April). Higher-order expansions of powered extremes of normal samples. Statistics and Probability Letters111, 12–17. https://doi.org/10.1016/j.spl.2016.01.003 

To cite this article: Yuhan Zou, Yingyin Lu & Zuoxiang Peng (2023) Rates of convergence of powered order statistics from general error distribution, Statistical Theory and Related Fields, 7:1, 1-29, DOI: 10.1080/24754269.2022.2146955 To link to this article: https://doi.org/10.1080/24754269.2022.2146955