Calculation of simplicial depth estimators for polynomial regression with applications

Wellmann, R., Katina, S. and Müller, C.H. (2007) Calculation of simplicial depth estimators for polynomial regression with applications. Computational Statistics and Data Analysis, 51(10), pp. 5025-5040. (doi: 10.1016/j.csda.2006.10.015)

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A fast algorithm for calculating the simplicial depth of a single parameter vector of a polynomial regression model is derived. Additionally, an algorithm for calculating the parameter vectors with maximum simplicial depth within an affine subspace of the parameter space or a polyhedron is presented. Since the maximum simplicial depth estimator is not unique, l1 and l2 methods are used to make the estimator unique. This estimator is compared with other estimators in examples of linear and quadratic regression. Furthermore, it is shown how the maximum simplicial depth can be used to derive distribution-free asymptotic α-level tests for testing hypotheses in polynomial regression models. The tests are applied on a problem of shape analysis where it is tested how the relative head length of the fish species Lepomis gibbosus depends on the size of these fishes. It is also tested whether the dependency can be described by the same polynomial regression function within different populations.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Katina, Dr Stanislav
Authors: Wellmann, R., Katina, S., and Müller, C.H.
Subjects:Q Science > QA Mathematics
College/School:College of Social Sciences > Adam Smith Research Foundation
Research Group:Statistical Modelling
Journal Name:Computational Statistics and Data Analysis
Published Online:09 November 2006

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