RICCOMAGNO , Eva

A basic application of algebraic statistics to design and analysis of experiments considers a design as a zero-dimensional variety and identifies it with the ideal of the variety. Then, a subset of a standard basis of the design ideal is used as support for identifiable regression models. Estimation of the model parameter is performed by...

In 1982 and 1983 two articles [M. Fliess and F. Lamnabhi-Lagarrigue, J. Math. Phys. 23 (1982), no. 4, 495--502; F. Lamnabhi-Lagarrigue and M. Lamnabhi, in Computer algebra 55--67, Lecture Notes in Comput. Sci., 162, Springer, Berlin, 1983] were published in which the previous study is used to analyze the solution of stochastic differential...

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PhD thesis Department of Statistics, The University of Warwick (UK)

In algebraic statistics, computational techniques from algebraic geometry become tools to address statistical problems. This, in turn, may prompt research in algebraic geometry. The basic ideas at the core of algebraic statistics will be presented. In particular, we shall consider application to contingency tables and to design of experiments.

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Computer algebra and, in particular, Gröbner bases are powerful tools in experimental design [G. Pistone and H. P. Wynn, Biometrika 83 (1996), no. 3, 653--666 MR1423881 ]. This paper applies this algebraic methodology to the identifiability of Fourier models. The choice of the class of trigonometric models forces one to deal with complex...

Methods from Commutative Algebra and Numerical Analysis are combined to address a problem common to many disciplines: the estimation of the expected value of a polynomial of a random vector using a linear combination of a finite number of its values. In this work we remark on the error estimation in cubature formulæ for polynomial functions and...

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In this work we remark on the error estimation in cubature formulae. Methods from Commutative Algebra and Orthogonal Polynomial Theory are combined to address a problem common to many disciplines: the estimation of the expected value of a polynomial of a random vector using a linear combination of a finite number of its values. We study in...

Markov combinations for structural meta-analysis problems provide a way of constructing a statistical model that takes into account two or more marginal distributions by imposing condi- tional independence constraints between the variables that are not jointly observed. This paper considers Gaussian distributions and discusses how the...

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Department of Statistics, The University of Warwick, Warwick Preprint n. 394

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We present an algorithm for the reduction of dimensionality useful in statistical classification problems where observations from two multivariate normal distributions are discriminated. It is based on Principal Components Analysis and consists of a simultaneous diagonalization of two covariance matrices. The criterion for reduction of...

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Department of Statistics, The University of Warwick, Warwick Preprint n. 411