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The potential of kernel density estimation for modelling relations among dairy farm characteristics

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  • Senga Kiessé, Tristan
  • Corson, Michael S.
  • Eugène, Maguy

Abstract

Agricultural systems are generally characterised by many dependent variables that represent their management practices and performances. Parametric approaches are usually used to explore data collected from farms and relations among variables. However, these approaches are generally limited by strong assumptions about the shape of the model that relates variables to each other, which can induce bias in studies.

Suggested Citation

  • Senga Kiessé, Tristan & Corson, Michael S. & Eugène, Maguy, 2022. "The potential of kernel density estimation for modelling relations among dairy farm characteristics," Agricultural Systems, Elsevier, vol. 199(C).
    • Handle: RePEc:eee:agisys:v:199:y:2022:i:c:s0308521x22000427
    DOI: 10.1016/j.agsy.2022.103406
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    References listed on IDEAS

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    1. Tarn Duong & Martin L. Hazelton, 2005. "Cross‐validation Bandwidth Matrices for Multivariate Kernel Density Estimation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 32(3), pages 485-506, September.
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    3. Christopher A. Wolf & Daniel A. Sumner, 2001. "Are Farm Size Distributions Bimodal? Evidence from Kernel Density Estimates of Dairy Farm Size Distributions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(1), pages 77-88.
    4. Chen, Song Xi, 1999. "Beta kernel estimators for density functions," Computational Statistics & Data Analysis, Elsevier, vol. 31(2), pages 131-145, August.
    5. Duong, Tarn, 2007. "ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 21(i07).
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