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Evaluation of matching noise for imputation techniques based on nonparametric local linear regression estimators

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  • Conti, Pier Luigi
  • Marella, Daniela
  • Scanu, Mauro

Abstract

A new matching procedure based on imputing missing data by means of a local linear estimator of the underlying population regression function (that is assumed not necessarily linear) is introduced. Such a procedure is compared to other traditional approaches, more precisely hot deck methods as well as methods based on kNN estimators. The relationship between the variables of interest is assumed not necessarily linear. Performance is measured by the matching noise given by the discrepancy between the distribution generating genuine data and the distribution generating imputed values.

Suggested Citation

  • Conti, Pier Luigi & Marella, Daniela & Scanu, Mauro, 2008. "Evaluation of matching noise for imputation techniques based on nonparametric local linear regression estimators," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 354-365, December.
    • Handle: RePEc:eee:csdana:v:53:y:2008:i:2:p:354-365
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    References listed on IDEAS

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    1. Aluja-Banet, Tomas & Daunis-i-Estadella, Josep & Pellicer, David, 2007. "GRAFT, a complete system for data fusion," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 635-649, October.
    2. Chinhui Juhn & Sandra E. Black, 2000. "The Rise of Female Professionals: Are Women Responding to Skill Demand?," American Economic Review, American Economic Association, vol. 90(2), pages 450-455, May.
    3. Marella, Daniela & Scanu, Mauro & Luigi Conti, Pier, 2008. "On the matching noise of some nonparametric imputation procedures," Statistics & Probability Letters, Elsevier, vol. 78(12), pages 1593-1600, September.
    4. Loader, Catherine, 2004. "Smoothing: Local Regression Techniques," Papers 2004,12, Humboldt University of Berlin, Center for Applied Statistics and Economics (CASE).
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    Cited by:

    1. Antonio D’Ambrosio & Massimo Aria & Roberta Siciliano, 2012. "Accurate Tree-based Missing Data Imputation and Data Fusion within the Statistical Learning Paradigm," Journal of Classification, Springer;The Classification Society, vol. 29(2), pages 227-258, July.
    2. Riccardo D’Alberto & Matteo Zavalloni & Meri Raggi & Davide Viaggi, 2018. "AES Impact Evaluation With Integrated Farm Data: Combining Statistical Matching and Propensity Score Matching," Sustainability, MDPI, vol. 10(11), pages 1-24, November.
    3. Claramunt González, Juan & van Delden, Arnout & de Waal, Ton, 2023. "Assessment of the effect of constraints in a new multivariate mixed method for statistical matching," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).
    4. Ahfock, Daniel & Pyne, Saumyadipta & McLachlan, Geoffrey J., 2022. "Statistical file-matching of non-Gaussian data: A game theoretic approach," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    5. D'Alberto, R. & Raggi, M., 2018. "Statistical Matching in agricultural economics: how to integrate different farm data sources," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277101, International Association of Agricultural Economists.
    6. Endres Eva & Fink Paul & Augustin Thomas, 2019. "Imprecise Imputation: A Nonparametric Micro Approach Reflecting the Natural Uncertainty of Statistical Matching with Categorical Data," Journal of Official Statistics, Sciendo, vol. 35(3), pages 599-624, September.
    7. Zhang Li-Chun, 2015. "On Proxy Variables and Categorical Data Fusion," Journal of Official Statistics, Sciendo, vol. 31(4), pages 783-807, December.
    8. Zahra Rezaei Ghahroodi, 2023. "Statistical matching of sample survey data: application to integrate Iranian time use and labour force surveys," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 1023-1051, September.
    9. Nicklas Pettersson, 2013. "Bias reduction of finite population imputation by kernel methods," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 14(1), pages 139-160, March.

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