Functional variance estimation using penalized splines with principal component analysis

Kauermann G, Wegener M (2011)
Statistics and Computing 21(2): 159-171.

Journal Article | Published | English

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In many fields of empirical research one is faced with observations arising from a functional process. If so, classical multivariate methods are often not feasible or appropriate to explore the data at hand and functional data analysis is prevailing. In this paper we present a method for joint modeling of mean and variance in longitudinal data using penalized splines. Unlike previous approaches we model both components simultaneously via rich spline bases. Estimation as well as smoothing parameter selection is carried out using a mixed model framework. The resulting smooth covariance structures are then used to perform principal component analysis. We illustrate our approach by several simulations and an application to financial interest data.
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Kauermann G, Wegener M. Functional variance estimation using penalized splines with principal component analysis. Statistics and Computing. 2011;21(2):159-171.
Kauermann, G., & Wegener, M. (2011). Functional variance estimation using penalized splines with principal component analysis. Statistics and Computing, 21(2), 159-171.
Kauermann, G., and Wegener, M. (2011). Functional variance estimation using penalized splines with principal component analysis. Statistics and Computing 21, 159-171.
Kauermann, G., & Wegener, M., 2011. Functional variance estimation using penalized splines with principal component analysis. Statistics and Computing, 21(2), p 159-171.
G. Kauermann and M. Wegener, “Functional variance estimation using penalized splines with principal component analysis”, Statistics and Computing, vol. 21, 2011, pp. 159-171.
Kauermann, G., Wegener, M.: Functional variance estimation using penalized splines with principal component analysis. Statistics and Computing. 21, 159-171 (2011).
Kauermann, Göran, and Wegener, Michael. “Functional variance estimation using penalized splines with principal component analysis”. Statistics and Computing 21.2 (2011): 159-171.
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