NEAREST NEIGHBOR BIAS IN THE SUBSTITUTION OF MISSING VALUES

Các tác giả

  • Chris J. Cieszewski The University of Georgia, USA
  • Kim Iles Kim Iles & Associates, Nanaimo, B.C., Canada

Từ khóa:

Mapping, Nearest Neighbor Imputation, Nearest Neighbor Bias, Large-area Forest Inventories, Multi-source Data Fusion.

Tóm tắt

This is a short technical note illustrating the bias inherent in the general case of the Nearest Neighbor (NN) method used to substitute missing values. This presentation doesn't make any assumptions about the geometry of the sampled subjects. The general examples illustrate that the bias exists mainly at the limits of the data range and not necessarily within the center part of the range. However, the latter is also possible around any significant data gaps. The NN data domain stretches across an arbitrary subject characteristic rather than across the physical space. It is possible to reduce the discussed here biases by assuring that the domain range of the considered attribute is well-represented within its entire range, especially at its upper and lower limits and there are no major gaps in the training data.

Tiểu sử tác giả

  • {affiliation}
    Kim Iles is an expert in sampling and forest inventory and inventor of many new statistical estimators. He got his B.S. in Forest Management and M.Sc. in Forest Biometrics, from Oregon State University, and Ph.D. Forest Biometrics, from University of British Columbia in 1979. He was the Biometrician and Head of Growth and Yield Dept., MacMillan Bloedel Ltd., Nanaimo, B.C. for 12 years until 1991 and a consultant since then. He is the author of a textbook on Forest Inventory (now in second printing), and he was the principle inventory design consultant for the British Columbia Provincial Inventory design (this inventory covered an area of 100,000,000 hectares). Kim's specialties of Variable Plot Sampling and 3P sampling are the dominant inventory systems used in the North America. He has taught inventory techniques to several thousand professional cruisers on 3 continents, and is the major contributor to the Cruising and Inventory Newsletter published by John Bell Associates. He has been a member of several committees of national standing, and has developed and introduced a number of innovations in cruising systems in North America. /p

Tài liệu tham khảo

Czaplewski, R. 2010. Review of: Nearest Neighbor Bias -- A simple example. Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS), 2(1), Pages: 66-66 (1). Retrieved from https://mcfns.net/index.php/Journal/article/view/MCFNS.2-66

Haara, A., & Kangas, A. 2012. Comparing K Nearest Neighbours Methods and Linear Regression – Is There Reason To Select One Over the Other?. Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS), 4(1), Pages: 50-65 (16). Retrieved from https://mcfns.net/index.php/Journal/article/view/MCFNS.4%3A50

Iles, K. 2010. Nearest Neighbor Bias -- A simple example. Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS), 2(1), Pages: 18-19 (2). Retrieved from https://mcfns.net/index.php/Journal/article/view/MCFNS.2-18

Iles, K. 2009. “Total-Balancing” an inventory: A method for unbiased inventories using highly biased non-sample data at variable scales. MCFNS. 1(1):10-13. http://mcfns.com/index.php/Journal/article/view/MCFNS-1:10/18

Lowe, R., & Cieszewski, C. 2014. Multi-source K-nearest neighbor, Mean Balanced forest inventory of Georgia. Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS), 6(2), 65-79 (15). Retrieved from https://mcfns.net/index.php/Journal/article/view/6_65/184

Lượt tải xuống

Đã Xuất bản

2021-11-01

Số

Chuyên mục

Sampling and Natural Resource Inventories

Cách trích dẫn

NEAREST NEIGHBOR BIAS IN THE SUBSTITUTION OF MISSING VALUES. (2021). Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS), 13(2), 27-30(4). https://v3.mcfns.com/index.php/Journal/article/view/13.8

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