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ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R

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  • Duong, Tarn

Abstract

Kernel smoothing is one of the most widely used non-parametric data smoothing techniques. We introduce a new R package ks for multivariate kernel smoothing. Currently it contains functionality for kernel density estimation and kernel discriminant analysis. It is a comprehensive package for bandwidth matrix selection, implementing a wide range of data-driven diagonal and unconstrained bandwidth selectors.

Suggested Citation

  • 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).
    • Handle: RePEc:jss:jstsof:v:021:i07
    DOI: http://hdl.handle.net/10.18637/jss.v021.i07
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    Cited by:

    1. Natalia Khorunzhina & Jean-François Richard, 2019. "Finite Gaussian Mixture Approximations to Analytically Intractable Density Kernels," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 991-1017, March.
    2. Zhang, Yi & Cheng, Chuntian & Cai, Huaxiang & Jin, Xiaoyu & Jia, Zebin & Wu, Xinyu & Su, Huaying & Yang, Tiantian, 2022. "Long-term stochastic model predictive control and efficiency assessment for hydro-wind-solar renewable energy supply system," Applied Energy, Elsevier, vol. 316(C).
    3. Karim M Abadir & Michel Lubrano, 2023. "Explicit solutions for the asymptotically-optimal bandwidth in cross validation," AMSE Working Papers 2336, Aix-Marseille School of Economics, France.
    4. Kim, Wongon & Yoon, Heonjun & Lee, Guesuk & Kim, Taejin & Youn, Byeng D., 2020. "A new calibration metric that considers statistical correlation: Marginal Probability and Correlation Residuals," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    5. Lees, Kirsty J. & Guerin, Andrew J. & Masden, Elizabeth A., 2016. "Using kernel density estimation to explore habitat use by seabirds at a marine renewable wave energy test facility," Marine Policy, Elsevier, vol. 63(C), pages 35-44.
    6. R. N. Rattihalli & S. B. Patil, 2021. "Data Dependent Asymmetric Kernels for Estimating the Density Function," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 155-186, February.
    7. Zougab, Nabil & Adjabi, Smail & Kokonendji, Célestin C., 2014. "Bayesian estimation of adaptive bandwidth matrices in multivariate kernel density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 28-38.
    8. 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).
    9. Mirosław Kornatka & Anna Gawlak, 2021. "An Analysis of the Operation of Distribution Networks Using Kernel Density Estimators," Energies, MDPI, vol. 14(21), pages 1-12, October.
    10. Matias D. Cattaneo & Michael Jansson & Xinwei Ma, 2019. "lpdensity: Local Polynomial Density Estimation and Inference," Papers 1906.06529, arXiv.org, revised Feb 2021.
    11. Jiabo Yin & Shenglian Guo & Zhangjun Liu & Guang Yang & Yixuan Zhong & Dedi Liu, 2018. "Uncertainty Analysis of Bivariate Design Flood Estimation and its Impacts on Reservoir Routing," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1795-1809, March.
    12. Pulkkinen, Seppo, 2015. "Ridge-based method for finding curvilinear structures from noisy data," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 89-109.
    13. Azadbakhsh, Mahdis & Jankowski, Hanna & Gao, Xin, 2014. "Computing confidence intervals for log-concave densities," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 248-264.
    14. Ge, Suqin & Macieira, João, 2020. "Unobserved Worker Quality and Inter-Industry Wage Differentials," GLO Discussion Paper Series 491, Global Labor Organization (GLO).
    15. Delicado, Pedro & Vieu, Philippe, 2015. "Optimal level sets for bivariate density representation," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 1-18.
    16. Uddameri, Venkatesh & Ghaseminejad, Ali & Hernandez, E. Annette, 2020. "A tiered stochastic framework for assessing crop yield loss risks due to water scarcity under different uncertainty levels," Agricultural Water Management, Elsevier, vol. 238(C).
    17. Hernández-Lobato, José Miguel & Suárez, Alberto, 2011. "Semiparametric bivariate Archimedean copulas," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2038-2058, June.
    18. Oleksii Pokotylo & Karl Mosler, 2019. "Classification with the pot–pot plot," Statistical Papers, Springer, vol. 60(3), pages 903-931, June.
    19. Pablo Martínez-Camblor & Sonia Pérez-Fernández & Susana Díaz-Coto, 2021. "Optimal classification scores based on multivariate marker transformations," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(4), pages 581-599, December.
    20. Guillermo Basulto-Elias & Alicia L. Carriquiry & Kris Brabanter & Daniel J. Nordman, 2021. "Bivariate Kernel Deconvolution with Panel Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 122-151, May.
    21. Aurélien Vivancos & Gerry Closs & Cédric Tentelier, 2017. "Are 2D space-use analyses adapted to animals living in 3D environments? A case study on a fish shoal," Behavioral Ecology, International Society for Behavioral Ecology, vol. 28(2), pages 485-493.
    22. Bram Thijssen & Lodewyk F A Wessels, 2020. "Approximating multivariate posterior distribution functions from Monte Carlo samples for sequential Bayesian inference," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-25, March.
    23. Akpoti, Komlavi & Dossou-Yovo, Elliott R. & Zwart, Sander J. & Kiepe, Paul, 2021. "The potential for expansion of irrigated rice under alternate wetting and drying in Burkina Faso," Agricultural Water Management, Elsevier, vol. 247(C).
    24. R. C. Rodríguez-Caro & E. Graciá & S. P. Blomberg & H. Cayuela & M. Grace & C. P. Carmona & H. A. Pérez-Mendoza & A. Giménez & R. Salguero-Gómez, 2023. "Anthropogenic impacts on threatened species erode functional diversity in chelonians and crocodilians," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    25. Schoch, Tobias & Staub, Kaspar & Pfister, Christian, 2012. "Social inequality and the biological standard of living: An anthropometric analysis of Swiss conscription data, 1875–1950," Economics & Human Biology, Elsevier, vol. 10(2), pages 154-173.

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