IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v137y2018icp336-342.html
   My bibliography  Save this article

Chunked-and-averaged estimators for vector parameters

Author

Listed:
  • Nguyen, Hien D.
  • McLachlan, Geoffrey J.

Abstract

A divide-and-conquer method for parameter estimation is the chunked-and-averaged (CA) estimator. CA estimators have been studied for univariate parameters under independent and identically distributed (IID) sampling. We study the CA estimators of vector parameters and under non-IID sampling.

Suggested Citation

  • Nguyen, Hien D. & McLachlan, Geoffrey J., 2018. "Chunked-and-averaged estimators for vector parameters," Statistics & Probability Letters, Elsevier, vol. 137(C), pages 336-342.
    • Handle: RePEc:eee:stapro:v:137:y:2018:i:c:p:336-342
    DOI: 10.1016/j.spl.2018.02.051
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715218300968
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2018.02.051?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Runze Li & Dennis K.J. Lin & Bing Li, 2013. "Statistical inference in massive data sets," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 29(5), pages 399-409, September.
    2. Matloff, Norman, 2016. "Software Alchemy: Turning Complex Statistical Computations into Embarrassingly-Parallel Ones," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 71(i04).
    3. Jenish, Nazgul & Prucha, Ingmar R., 2009. "Central limit theorems and uniform laws of large numbers for arrays of random fields," Journal of Econometrics, Elsevier, vol. 150(1), pages 86-98, May.
    4. Bradley, Richard C., 1989. "A caution on mixing conditions for random fields," Statistics & Probability Letters, Elsevier, vol. 8(5), pages 489-491, October.
    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. Richard C. Bradley, 2001. "A Stationary Rho-Mixing Markov Chain Which Is Not “Interlaced” Rho-Mixing," Journal of Theoretical Probability, Springer, vol. 14(3), pages 717-727, July.
    2. El Machkouri, Mohamed & Volný, Dalibor & Wu, Wei Biao, 2013. "A central limit theorem for stationary random fields," Stochastic Processes and their Applications, Elsevier, vol. 123(1), pages 1-14.
    3. Luis Alvarez & Bruno Ferman, 2020. "Inference in Difference-in-Differences with Few Treated Units and Spatial Correlation," Papers 2006.16997, arXiv.org, revised Apr 2023.
    4. Elsinger, Helmut, 2013. "Comment on: A non-parametric spatial independence test using symbolic entropy," Regional Science and Urban Economics, Elsevier, vol. 43(5), pages 838-840.
    5. Shi, Wei & Lee, Lung-fei, 2018. "A spatial panel data model with time varying endogenous weights matrices and common factors," Regional Science and Urban Economics, Elsevier, vol. 72(C), pages 6-34.
    6. Hidalgo, Javier & Seo, Myung Hwan, 2015. "Specification Tests For Lattice Processes," Econometric Theory, Cambridge University Press, vol. 31(2), pages 294-336, April.
    7. Pakel, Cavit, 2019. "Bias reduction in nonlinear and dynamic panels in the presence of cross-section dependence," Journal of Econometrics, Elsevier, vol. 213(2), pages 459-492.
    8. Qu, Xi & Lee, Lung-fei & Yang, Chao, 2021. "Estimation of a SAR model with endogenous spatial weights constructed by bilateral variables," Journal of Econometrics, Elsevier, vol. 221(1), pages 180-197.
    9. Daniel Nordman, 2008. "An empirical likelihood method for spatial regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 68(3), pages 351-363, November.
    10. Chen, Song Xi & Guo, Bin & Qiu, Yumou, 2023. "Testing and signal identification for two-sample high-dimensional covariances via multi-level thresholding," Journal of Econometrics, Elsevier, vol. 235(2), pages 1337-1354.
    11. Cuicui Lu & Weining Wang & Jeffrey M. Wooldridge, 2018. "Using generalized estimating equations to estimate nonlinear models with spatial data," Papers 1810.05855, arXiv.org.
    12. Lei, J., 2013. "Smoothed Spatial Maximum Score Estimation of Spatial Autoregressive Binary Choice Panel Models," Other publications TiSEM d63bf400-7ff2-4a1c-8067-1, Tilburg University, School of Economics and Management.
    13. Mullally, Conner, 2011. "Development in the Midst of Drought: Evaluating an Agricultural Extension and Credit Program in Nicaragua," 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania 108498, Agricultural and Applied Economics Association.
    14. Larry G. Epstein & Hiroaki Kaido & Kyoungwon Seo, 2016. "Robust Confidence Regions for Incomplete Models," Econometrica, Econometric Society, vol. 84, pages 1799-1838, September.
    15. Gupta, Abhimanyu, 2018. "Autoregressive spatial spectral estimates," Journal of Econometrics, Elsevier, vol. 203(1), pages 80-95.
    16. Xiu Xu & Weining Wang & Yongcheol Shin & Chaowen Zheng, 2021. "Dynamic Network Quantile Regression Model," Papers 2111.07633, arXiv.org.
    17. Michael P. Leung, 2021. "Rate-Optimal Cluster-Randomized Designs for Spatial Interference," Papers 2111.04219, arXiv.org, revised Sep 2022.
    18. Jungbin Hwang, 2017. "Simple and Trustworthy Cluster-Robust GMM Inference," Working papers 2017-19, University of Connecticut, Department of Economics, revised Aug 2020.
    19. Jieun Lee, 2022. "Evidence and Strategy on Economic Distance in Spatially Augmented Solow-Swan Growth Model," Papers 2209.05562, arXiv.org.
    20. Klein, Nadja & Herwartz, Helmut & Kneib, Thomas, 2020. "Modelling regional patterns of inefficiency: A Bayesian approach to geoadditive panel stochastic frontier analysis with an application to cereal production in England and Wales," Journal of Econometrics, Elsevier, vol. 214(2), pages 513-539.

    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:137:y:2018:i:c:p:336-342. 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.