IDEAS home Printed from https://ideas.repec.org/a/vrs/stintr/v22y2021i1p1-28n10.html
   My bibliography  Save this article

An application of a complex measure to model–based imputation in business statistics

Author

Listed:
  • Młodak Andrzej

    (Statistical Office in Poznań, Centre for Small Area Estimation, address: Statistical Office in Poznań, Branch in Kalisz, ul. Piwonicka 7–9, 62–800 Kalisz, Poland . and Calisia University, – Kalisz, Poland)

Abstract

When faced with missing data in a statistical survey or administrative sources, imputation is frequently used in order to fill the gaps and reduce the major part of bias that can affect aggregated estimates as a consequence of these gaps. This paper presents research on the efficiency of model–based imputation in business statistics, where the explanatory variable is a complex measure constructed by taxonomic methods. The proposed approach involves selecting explanatory variables that fit best in terms of variation and correlation from a set of possible explanatory variables for imputed information, and then replacing them with a single complex measure (meta–feature) exploiting their whole informational potential. This meta–feature is constructed as a function of a median distance of given objects from the benchmark of development. A simulation study and empirical study were used to verify the efficiency of the proposed approach. The paper also presents five types of similar techniques: ratio imputation, regression imputation, regression imputation with iteration, predictive mean matching and the propensity score method. The second study presented in the paper involved a simulation of missing data using IT business data from the California State University in Los Angeles, USA. The results show that models with a strong dependence on functional form assumptions can be improved by using a complex measure to summarize the predictor variables rather than the variables themselves (raw or normalized).

Suggested Citation

  • Młodak Andrzej, 2021. "An application of a complex measure to model–based imputation in business statistics," Statistics in Transition New Series, Polish Statistical Association, vol. 22(1), pages 1-28, March.
    • Handle: RePEc:vrs:stintr:v:22:y:2021:i:1:p:1-28:n:10
    DOI: 10.21307/stattrans-2021-001
    as

    Download full text from publisher

    File URL: https://doi.org/10.21307/stattrans-2021-001
    Download Restriction: no
    File URL: https://libkey.io/10.21307/stattrans-2021-001?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
    ---><---

    References listed on IDEAS

    as
    1. Horton N. J. & Lipsitz S. R., 2001. "Multiple Imputation in Practice: Comparison of Software Packages for Regression Models With Missing Variables," The American Statistician, American Statistical Association, vol. 55, pages 244-254, August.
    2. Jae Kwang Kim & J. Michael Brick & Wayne A. Fuller & Graham Kalton, 2006. "On the bias of the multiple‐imputation variance estimator in survey sampling," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 509-521, June.
    3. Rebecca R. Andridge & Roderick J. A. Little, 2010. "A Review of Hot Deck Imputation for Survey Non‐response," International Statistical Review, International Statistical Institute, vol. 78(1), pages 40-64, April.
    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. Andrzej Młodak, 2021. "An application of a complex measure to model–based imputation in business statistics," Statistics in Transition New Series, Polish Statistical Association, vol. 22(1), pages 1-28, March.
    2. Sullivan, Danielle & Andridge, Rebecca, 2015. "A hot deck imputation procedure for multiply imputing nonignorable missing data: The proxy pattern-mixture hot deck," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 173-185.
    3. Xiaojun Mao & Zhonglei Wang & Shu Yang, 2023. "Matrix completion under complex survey sampling," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(3), pages 463-492, June.
    4. Shu Yang & Jae Kwang Kim, 2020. "Asymptotic theory and inference of predictive mean matching imputation using a superpopulation model framework," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(3), pages 839-861, September.
    5. Raymundo M. Campos-Vázquez, 2013. "Efectos de los ingresos no reportados en el nivel y tendencia de la pobreza laboral en México," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(2), pages 23-54, November.
    6. Paul T. von Hippel, 2013. "Should a Normal Imputation Model be Modified to Impute Skewed Variables?," Sociological Methods & Research, , vol. 42(1), pages 105-138, February.
    7. Meyer, Bruce D. & Mittag, Nikolas, 2019. "Combining Administrative and Survey Data to Improve Income Measurement," IZA Discussion Papers 12266, Institute of Labor Economics (IZA).
    8. Nancy, Jane Y. & Khanna, Nehemiah H. & Arputharaj, Kannan, 2017. "Imputing missing values in unevenly spaced clinical time series data to build an effective temporal classification framework," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 63-79.
    9. Susanne Rässler & Regina T. Riphahn, 2006. "Survey Item Nonresponse and its Treatment," Springer Books, in: Olaf Hübler & Jachim Frohn (ed.), Modern Econometric Analysis, chapter 15, pages 215-230, Springer.
    10. McDonough, Ian K. & Millimet, Daniel L., 2017. "Missing data, imputation, and endogeneity," Journal of Econometrics, Elsevier, vol. 199(2), pages 141-155.
    11. Marcello D’Orazio, 2015. "Integration and imputation of survey data in R: the StatMatch package," Romanian Statistical Review, Romanian Statistical Review, vol. 63(2), pages 57-68, June.
    12. Zhong, Hua & Hu, Wuyang, 2015. "Farmers’ Willingness to Engage in Best Management Practices: an Application of Multiple Imputation," 2015 Annual Meeting, January 31-February 3, 2015, Atlanta, Georgia 196962, Southern Agricultural Economics Association.
    13. Yanqing Sun & Li Qi & Fei Heng & Peter B. Gilbert, 2020. "A hybrid approach for the stratified mark‐specific proportional hazards model with missing covariates and missing marks, with application to vaccine efficacy trials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 791-814, August.
    14. Thomas Masterson, 2012. "Simulations of Full-Time Employment and Household Work in the Levy Institute Measure of Time and Income Poverty (LIMTIP) for Argentina, Chile, and Mexico," Economics Working Paper Archive wp_727, Levy Economics Institute.
    15. Chiara Elena Dalla & Menon Martina & Perali Federico, 2019. "An Integrated Database to Measure Living Standards," Journal of Official Statistics, Sciendo, vol. 35(3), pages 531-576, September.
    16. Janet MacNeil Vroomen & Iris Eekhout & Marcel G. Dijkgraaf & Hein van Hout & Sophia E. de Rooij & Martijn W. Heymans & Judith E. Bosmans, 2016. "Multiple imputation strategies for zero-inflated cost data in economic evaluations: which method works best?," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 17(8), pages 939-950, November.
    17. 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.
    18. Daniel Araya & Guillermo Paraje, 2018. "The impact of prices on alcoholic beverage consumption in Chile," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-15, October.
    19. Aderiana Mutheu Mbandi & Jan R. Böhnke & Dietrich Schwela & Harry Vallack & Mike R. Ashmore & Lisa Emberson, 2019. "Estimating On-Road Vehicle Fuel Economy in Africa: A Case Study Based on an Urban Transport Survey in Nairobi, Kenya," Energies, MDPI, vol. 12(6), pages 1-28, March.
    20. Shaun R. Seaman & Ian R. White & Andrew J. Copas & Leah Li, 2012. "Combining Multiple Imputation and Inverse-Probability Weighting," Biometrics, The International Biometric Society, vol. 68(1), pages 129-137, March.

    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:vrs:stintr:v:22:y:2021:i:1:p:1-28:n:10. 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.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.