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Outputs (2004)

Generative linear mixture modelling (2012)
Presentation / Conference Contribution
Lawson, A., & Einbeck, J. (2012). Generative linear mixture modelling. In A. Komarek, & S. Nagy (Eds.), 27th International Workshop on Statistical Modelling, 16-20 July 2012, Prague, Czech Republic ; proceedings (595-600)

For multivariate data with a low–dimensional latent structure, a novel approach to linear dimension reduction based on Gaussian mixture models is pro- posed. A generative model is assumed for the data, where the mixture centres (or ‘mass points’) are... Read More about Generative linear mixture modelling.

Multivariate regression smoothing through the 'fallling net' (2011)
Presentation / Conference Contribution
Taylor, J., & Einbeck, J. (2011). Multivariate regression smoothing through the 'fallling net'. In D. Conesa, A. Forte, A. Lopez-Quilez, & F. Munoz (Eds.), 26th International Workshop on Statistical Modelling, 5-11 July 2011, Valencia, Spain ; proceedings (597-602)

We consider multivariate regression smoothing through a conditional mean shift procedure. By computing local conditional means iteratively over a set or grid of target points, at each iteration a `net' is formed which gently drifts towards the data c... Read More about Multivariate regression smoothing through the 'fallling net'.

League tables for literacy survey data based on random effect models (2008)
Presentation / Conference Contribution
Sofroniou, N., Hoad, D., & Einbeck, J. (2008). League tables for literacy survey data based on random effect models. In P. Eilers (Ed.), 23rd International Workshop on Statistical Modelling, 7-11 July 2008, Utrecht ; proceedings (402-405)

Data from the International Adult Literacy Survey are used to illustrate how league tables can be obtained from summary data, consisting of percentages and their standard errors, using random effects models estimated by nonparametric maximum likeliho... Read More about League tables for literacy survey data based on random effect models.

Data compression and regression based on local principal curves (2010)
Presentation / Conference Contribution
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_64

Frequently 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.

Constructing Economic Summary Indexes via Principal Curves (2010)
Presentation / Conference Contribution
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)
Presentation / Conference Contribution
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.