IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v80y2010i7-8p581-586.html
   My bibliography  Save this article

Strong Gaussian approximations of product-limit and quantile processes for truncated data under strong mixing

Author

Listed:
  • Ghalibaf, M. Bolbolian
  • Fakoor, V.
  • Azarnoosh, H.A.

Abstract

In this paper, we consider the product-limit quantile estimator of an unknown quantile function under a truncated dependent model. This is a parallel problem to the estimation of the unknown distribution function by the product-limit estimator under the same model. Simultaneous strong Gaussian approximations of the product-limit process and normed product-limit quantile process are constructed with rate O((logn)-[lambda]) for some [lambda]>0. The strong Gaussian approximation of the product-limit process is then applied to derive the law of the iterated logarithm for the product-limit process.

Suggested Citation

  • Ghalibaf, M. Bolbolian & Fakoor, V. & Azarnoosh, H.A., 2010. "Strong Gaussian approximations of product-limit and quantile processes for truncated data under strong mixing," Statistics & Probability Letters, Elsevier, vol. 80(7-8), pages 581-586, April.
    • Handle: RePEc:eee:stapro:v:80:y:2010:i:7-8:p:581-586
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-7152(09)00471-4
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sen, Pranab Kumar, 1972. "On the Bahadur representation of sample quantiles for sequences of [phi]-mixing random variables," Journal of Multivariate Analysis, Elsevier, vol. 2(1), pages 77-95, March.
    2. Gürler, Ülkü & Stute, Winfried & Wang, Jane-Ling, 1993. "Weak and strong quantile representations for randomly truncated data with applications," Statistics & Probability Letters, Elsevier, vol. 17(2), pages 139-148, May.
    3. Gastwirth, Joseph L, 1971. "A General Definition of the Lorenz Curve," Econometrica, Econometric Society, vol. 39(6), pages 1037-1039, November.
    4. Masry, Elias & Tjøstheim, Dag, 1997. "Additive Nonlinear ARX Time Series and Projection Estimates," Econometric Theory, Cambridge University Press, vol. 13(2), pages 214-252, April.
    5. Yu, Hao, 1996. "A note on strong approximation for quantile processes of strong mixing sequences," Statistics & Probability Letters, Elsevier, vol. 30(1), pages 1-7, September.
    6. Sun, Liuquan & Zhou, Xian, 2001. "Survival function and density estimation for truncated dependent data," Statistics & Probability Letters, Elsevier, vol. 52(1), pages 47-57, March.
    7. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    8. Fotopoulos, S. B. & Ahn, S. K., 1994. "Strong Approximation of the Quantile Processes and Its Applications under Strong Mixing Properties," Journal of Multivariate Analysis, Elsevier, vol. 51(1), pages 17-45, October.
    9. Masry, Elias & Tjøstheim, Dag, 1995. "Nonparametric Estimation and Identification of Nonlinear ARCH Time Series Strong Convergence and Asymptotic Normality: Strong Convergence and Asymptotic Normality," Econometric Theory, Cambridge University Press, vol. 11(2), pages 258-289, February.
    10. Szeman Tse, 2005. "Quantile process for left truncated and right censored data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 57(1), pages 61-69, March.
    11. Yoshihara, Ken-ichi, 1995. "The Bahadur representation of sample quantiles for sequences of strongly mixing random variables," Statistics & Probability Letters, Elsevier, vol. 24(4), pages 299-304, September.
    12. Zhou, Yong & Yip, Paul S. F., 1999. "A Strong Representation of the Product-Limit Estimator for Left Truncated and Right Censored Data," Journal of Multivariate Analysis, Elsevier, vol. 69(2), pages 261-280, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ajami, M. & Fakoor, V. & Jomhoori, S., 2011. "The Bahadur representation for kernel-type estimator of the quantile function under strong mixing and censored data," Statistics & Probability Letters, Elsevier, vol. 81(8), pages 1306-1310, August.
    2. Linton, Oliver & Mammen, Enno, 2003. "Estimating semiparametric ARCH (8) models by kernel smoothing methods," LSE Research Online Documents on Economics 2187, London School of Economics and Political Science, LSE Library.
    3. Kim, Woocheol & Linton, Oliver, 2003. "A local instrumental variable estimation method for generalized additive volatility models," LSE Research Online Documents on Economics 2028, London School of Economics and Political Science, LSE Library.
    4. O. Linton & E. Mammen, 2005. "Estimating Semiparametric ARCH(∞) Models by Kernel Smoothing Methods," Econometrica, Econometric Society, vol. 73(3), pages 771-836, May.
    5. Ho, Hwai-Chung, 2015. "Sample quantile analysis for long-memory stochastic volatility models," Journal of Econometrics, Elsevier, vol. 189(2), pages 360-370.
    6. Cai, Zongwu & Fan, Jianqing, 2000. "Average Regression Surface for Dependent Data," Journal of Multivariate Analysis, Elsevier, vol. 75(1), pages 112-142, October.
    7. Fakoor, Vahid & Jomhoori, Sarah & Azarnoosh, Hasanali, 2009. "Asymptotic expansion for ISE of kernel density estimators under censored dependent model," Statistics & Probability Letters, Elsevier, vol. 79(17), pages 1809-1817, September.
    8. Cai, Zongwu, 2003. "Nonparametric estimation equations for time series data," Statistics & Probability Letters, Elsevier, vol. 62(4), pages 379-390, May.
    9. Fakoor, V., 2010. "Strong uniform consistency of kernel density estimators under a censored dependent model," Statistics & Probability Letters, Elsevier, vol. 80(5-6), pages 318-323, March.
    10. Cai, Zongwu & Xu, Xiaoping, 2009. "Nonparametric Quantile Estimations for Dynamic Smooth Coefficient Models," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 371-383.
    11. Hu, Shuowen & Poskitt, D.S. & Zhang, Xibin, 2021. "Bayesian estimation for a semiparametric nonlinear volatility model," Economic Modelling, Elsevier, vol. 98(C), pages 361-370.
    12. Meitz, Mika & Saikkonen, Pentti, 2008. "Ergodicity, Mixing, And Existence Of Moments Of A Class Of Markov Models With Applications To Garch And Acd Models," Econometric Theory, Cambridge University Press, vol. 24(5), pages 1291-1320, October.
    13. da Silva, Murilo & Sriram, T.N. & Ke, Yuan, 2023. "Dimension reduction in time series under the presence of conditional heteroscedasticity," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    14. Mohamed Chikhi & Claude Diebolt, 2010. "Nonparametric analysis of financial time series by the Kernel methodology," Quality & Quantity: International Journal of Methodology, Springer, vol. 44(5), pages 865-880, August.
    15. Dong, Chaohua & Gao, Jiti & Tong, Howell, 2006. "Semiparametric penalty function method in partially linear model selection," MPRA Paper 11975, University Library of Munich, Germany, revised Aug 2006.
    16. Jürgen Franke & Peter Mwita & Weining Wang, 2015. "Nonparametric estimates for conditional quantiles of time series," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(1), pages 107-130, January.
    17. Chen, Gongmeng & Choi, Yoon K. & Zhou, Yong, 2005. "Nonparametric estimation of structural change points in volatility models for time series," Journal of Econometrics, Elsevier, vol. 126(1), pages 79-114, May.
    18. Xialu Liu & Zongwu Cai & Rong Chen, 2015. "Functional coefficient seasonal time series models with an application of Hawaii tourism data," Computational Statistics, Springer, vol. 30(3), pages 719-744, September.
    19. repec:hum:wpaper:sfb649dp2014-012 is not listed on IDEAS
    20. PERRON, Benoît, 1999. "Jumps in the Volatility of Financial Markets," Cahiers de recherche 9912, Universite de Montreal, Departement de sciences economiques.
    21. Lu, Zudi & Jiang, Zhenyu, 2001. "L1 geometric ergodicity of a multivariate nonlinear AR model with an ARCH term," Statistics & Probability Letters, Elsevier, vol. 51(2), pages 121-130, January.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:stapro:v:80:y:2010:i:7-8:p:581-586. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.