Strategies for local smoothing in high dimensions: using density thresholds and adapted GCV
(2010)
Conference Proceeding
Taylor, J., & Einbeck, J. (2010). Strategies for local smoothing in high dimensions: using density thresholds and adapted GCV. In A. Bowman (Ed.),
Outputs (4)
Constructing Economic Summary Indexes via Principal Curves (2010)
Conference Proceeding
Zayed, M., & Einbeck, J. (2010). Constructing Economic Summary Indexes via Principal Curves. In Y. Lechevallier, & G. Saporta (Eds.),Index number construction is an important and traditional subject in both the statistical and the economical sciences. A novel technique based on localized principal components to compose a single summary index from a collection of indexes is propose... Read More about Constructing Economic Summary Indexes via Principal Curves.
Localized regression on principal manifolds (2010)
Conference Proceeding
Einbeck, J., & Evers, L. (2010). Localized regression on principal manifolds. In A. Bowman (Ed.),We consider nonparametric dimension reduction techniques for multivariate regression problems in which the variables constituting the predictor space are strongly nonlinearly related. Specifically, the predictor space is approximated via ``local'' pr... Read More about Localized regression on principal manifolds.
Data compression and regression based on local principal curves (2010)
Conference Proceeding
Einbeck, J., Evers, L., & Hinchliff, K. (2010). Data compression and regression based on local principal curves. In A. Fink, B. Lausen, W. Seidel, & A. Ultsch (Eds.), Advances in data analysis, data handling and business intelligence (701-712). https://doi.org/10.1007/978-3-642-01044-6_64Frequently the predictor space of a multivariate regression problem of the type y = m(x_1, …, x_p ) + ε is intrinsically one-dimensional, or at least of far lower dimension than p. Usual modeling attempts such as the additive model y = m_1(x_1) + … +... Read More about Data compression and regression based on local principal curves.