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Innovative Neighbor Imputation Method for Norwegian Agriculture Survey

Learn about restricted neighbor imputation method applied to Norwegian Agriculture Survey for reliable statistics. Explore objective, method, empirical results, and future work.

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Innovative Neighbor Imputation Method for Norwegian Agriculture Survey

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  1. Statistical registers by restricted neighbor imputation – An application to the Norwegian Agriculture Survey Nina Hagesæther and Li-Chun Zhang Statistics Norway

  2. Outline • Objective • Method • Empirical results • Future work

  3. Objective • Statistical register: Variables Known Known Units Unknown Known

  4. Statistical register: Objective Register variables Good quality Known Known Units Unknown Poor quality Known Known 4

  5. Statistical register: Objective Register variables Target variables Units in sample Known Known Units outside sample Unknown Known 5

  6. Objective • Triple-goal criterion (Zhang and Nordbotten, 2008) • Efficient estimates • Correct covariance structure • Non-stochastic

  7. The RENI Method • REstricted Neighbor Imputation • Restrictions: Totals are already estimated • Donors = respondents • Receivers = population – respondents • Nearest neighbor (NN) = unit in same imputation class that satisfy

  8. Algorithm • Fine-tune phase (FT) • Donor among k nearest neighbors • Choose the donor that best satisfy the restrictions • An iterative process • Jump-start phase (JS) • NN imputation for a given proportion of totals • Speeds up the process • Proportion can be reduced or JS omitted

  9. Agriculture Survey 2006 • 50 000 units in the population, 10 000 in the sample • 84 target variables • Publish: class of farmlands in decares (6), farming activity (FA, 12), county • Important topics: leasing, investment, maintenance

  10. Empirical results – Number of neighbors FA- 2: 2660 receivers, 727 donors FA- 4: 9984 receivers, 3266 donors FA-10: 384 receivers, 243 donors FA-11: 586 receivers, 340 donors

  11. Empirical results (FA-4) – Restriction Donors: 3000, Receivers: 10000 Alt 1: Equal weight for all 84 restrictions when calculating delta (84) (84) (12) Alt 2: Chosen 12 restrictions 10 times higher weights Alt 3: 9 sets of sub-population restrictions in addition to alternative 2 (12x10) 11

  12. Empirical results (FA-10) – Restriction Donors: 240, Receivers: 380 Alt 1: Equal weight for all 84 restrictions when calculating delta (84) (12) Alt 2: Chosen 12 restrictions 10 times higher weights Alt 3: 9 sets of sub-population restrictions in addition to alternative 2 (12x10) 12

  13. Empirical results – CVRENI / Weighting

  14. Empirical results - Correlations Farmingactivity 10

  15. Future work • Restriction • How to choose restrictions • How to calculate delta • Adjust for partial non-response • Donor and receiver do not match on observed values of receiver • Partial missing in target variables • Unit missing of target variables as partial missing of combined auxiliary and target variables

  16. Thank you for your attention! Statistics Norway in Kongsvinger Statistics Norway in Oslo

  17. Empirical results – Computation time Farming activity 4 Farming activity 10

  18. Empirical results – Computation time

  19. Empirical results – Two-way classification Farmingactivity 10

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