IDEAS home Printed from https://ideas.repec.org/a/spr/sankhb/v78y2016i1d10.1007_s13571-015-0111-5.html
   My bibliography  Save this article

Sampling from Correlated Populations: Optimal Strategies and Comparison Study

Author

Listed:
  • Ioulia Papageorgiou

    (Athens University of Economics and Business)

Abstract

The problem of sampling from a population with correlated units is considered. The presence of correlation affects all stages of a survey, from the choice of the sampling scheme to the statistical inference. Ignoring or failing to identify the existing correlation can lead to incorrect inference, such as invalid standard errors, since standard sampling techniques are no longer appropriate. In this direction, several sampling methodologies have been proposed in the literature, aiming to accommodate the correlation in both the sampling and the estimation procedure. The problem can be quite difficult when the type of correlation is completely general and existing methods rely on either restricted assumptions about the population structure or limitations to practical implementation. We provide a review of currently available methodologies, drawing attention to the properties of the derived estimates, the assumptions made, the robustness of the methods under different types of correlation and the practical limitations. A question of how these methodologies compare arises because they differ on the optimality criterion they assume towards the solution. Some methodologies are even not theoretically justified, but they are commonly used as known superior in situations of correlated measurements. The comparison study is conducted on a basis of the relative efficiencies among the competing methodologies by using simulated and real data sets.

Suggested Citation

  • Ioulia Papageorgiou, 2016. "Sampling from Correlated Populations: Optimal Strategies and Comparison Study," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 78(1), pages 119-151, May.
    • Handle: RePEc:spr:sankhb:v:78:y:2016:i:1:d:10.1007_s13571-015-0111-5
    DOI: 10.1007/s13571-015-0111-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13571-015-0111-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13571-015-0111-5?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. Alwan, Layth C & Roberts, Harry V, 1988. "Time-Series Modeling for Statistical Process Control," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(1), pages 87-95, January.
    2. Cristiano Varin, 2008. "On composite marginal likelihoods," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 92(1), pages 1-28, February.
    3. C. A. Glasbey, 2001. "Non‐linear autoregressive time series with multivariate Gaussian mixtures as marginal distributions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(2), pages 143-154.
    4. Nils Lid Hjort & Cristiano Varin, 2008. "ML, PL, QL in Markov Chain Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(1), pages 64-82, March.
    5. D. R. Cox, 2004. "A note on pseudolikelihood constructed from marginal densities," Biometrika, Biometrika Trust, vol. 91(3), pages 729-737, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Daniel A. Griffith & Richard E. Plant, 2022. "Statistical Analysis in the Presence of Spatial Autocorrelation: Selected Sampling Strategy Effects," Stats, MDPI, vol. 5(4), pages 1-20, December.

    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. Bhat, Chandra R. & Sener, Ipek N. & Eluru, Naveen, 2010. "A flexible spatially dependent discrete choice model: Formulation and application to teenagers' weekday recreational activity participation," Transportation Research Part B: Methodological, Elsevier, vol. 44(8-9), pages 903-921, September.
    2. Bhat, Chandra R., 2011. "The maximum approximate composite marginal likelihood (MACML) estimation of multinomial probit-based unordered response choice models," Transportation Research Part B: Methodological, Elsevier, vol. 45(7), pages 923-939, August.
    3. Cristiano Varin, 2008. "On composite marginal likelihoods," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 92(1), pages 1-28, February.
    4. Paleti, Rajesh & Bhat, Chandra R., 2013. "The composite marginal likelihood (CML) estimation of panel ordered-response models," Journal of choice modelling, Elsevier, vol. 7(C), pages 24-43.
    5. Emil Aas Stoltenberg & Nils Lid Hjort, 2021. "Models and inference for on–off data via clipped Ornstein–Uhlenbeck processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(3), pages 908-929, September.
    6. Elsa Vazquez & Jeffrey R. Wilson, 2021. "Partitioned method of valid moment marginal model with Bayes interval estimates for correlated binary data with time-dependent covariates," Computational Statistics, Springer, vol. 36(4), pages 2701-2718, December.
    7. Paik, Jane & Ying, Zhiliang, 2012. "A composite likelihood approach for spatially correlated survival data," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 209-216, January.
    8. Vassilis Vasdekis & Silvia Cagnone & Irini Moustaki, 2012. "A Composite Likelihood Inference in Latent Variable Models for Ordinal Longitudinal Responses," Psychometrika, Springer;The Psychometric Society, vol. 77(3), pages 425-441, July.
    9. Lee Fawcett & David Walshaw, 2014. "Estimating the probability of simultaneous rainfall extremes within a region: a spatial approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(5), pages 959-976, May.
    10. Nobel, Anne & Lizin, Sebastien & Malina, Robert, 2023. "What drives the designation of protected areas? Accounting for spatial dependence using a composite marginal likelihood approach," Ecological Economics, Elsevier, vol. 205(C).
    11. Stanislav Anatolyev & Renat Khabibullin & Artem Prokhorov, 2012. "Reconstructing high dimensional dynamic distributions from distributions of lower dimension," Working Papers 12003, Concordia University, Department of Economics.
    12. A. Philip Dawid & Monica Musio & Laura Ventura, 2016. "Minimum Scoring Rule Inference," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(1), pages 123-138, March.
    13. Gourieroux, C. & Monfort, A., 2018. "Composite indirect inference with application to corporate risks," Econometrics and Statistics, Elsevier, vol. 7(C), pages 30-45.
    14. Papageorgiou, Ioulia & Moustaki, Irini, 2019. "Sampling of pairs in pairwise likelihood estimation for latent variable models with categorical observed variables," LSE Research Online Documents on Economics 87592, London School of Economics and Political Science, LSE Library.
    15. Ana-Maria Staicu, 2017. "Interview with Nancy Reid," International Statistical Review, International Statistical Institute, vol. 85(3), pages 381-403, December.
    16. Fangya Mao & Richard J. Cook, 2023. "Spatial dependence modeling of latent susceptibility and time to joint damage in psoriatic arthritis," Biometrics, The International Biometric Society, vol. 79(3), pages 2605-2618, September.
    17. Yang Wu & Malay Ghosh, 2017. "Asymptotic Expansion of the Posterior Based on Pairwise Likelihood," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 79(1), pages 39-75, February.
    18. K. Florios & I. Moustaki & D. Rizopoulos & V. Vasdekis, 2015. "A modified weighted pairwise likelihood estimator for a class of random effects models," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 217-228, August.
    19. Joe, Harry & Lee, Youngjo, 2009. "On weighting of bivariate margins in pairwise likelihood," Journal of Multivariate Analysis, Elsevier, vol. 100(4), pages 670-685, April.
    20. Anders Løland & Ragnar Bang Huseby & Nils Lid Hjort & Arnoldo Frigessi, 2013. "Statistical Corrections of Invalid Correlation Matrices," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(4), pages 807-824, December.

    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:spr:sankhb:v:78:y:2016:i:1:d:10.1007_s13571-015-0111-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.