[ABE-L] Seminarios em High Dimensional Data

[Ronaldo Dias]

Prezados(as),

Convidamos a todos os interessados a participar. Obrigado, abs.
Ronaldo

Seminários em Análise de Dados em Alta Dimensão. <AD,AD>
Sala: 221 IMECC, as 13hs, 13/06/2018

A spline-based approach to spatially confounded linear regression of
geostatistical data
(Guilherme Ludwig, DE, IMECC-UNICAMP)

Abstract: For spatial linear regression, the traditional approach is to use
a parametric linear mixed-effects model such that spatial dependence is
captured as a spatial random effect; this effect is often assumed to be a
Gaussian process with mean zero and a parametric covariance function.
Spline surfaces can be used as an alternative approach to capture spatial
variability, giving rise to a semiparametric method that does not require
the specification of a parametric covariance structure. The spline
component in such a semiparametric method, however, impacts the estimation
of the regression coefficients. In this talk, we investigate such an impact
in spatial linear regression with spline-based spatial effects. Statistical
properties of the regression coefficient estimators are established under
the model assumptions of the traditional spatial linear regression. We also
develop a method to choose the tuning parameter for the smoothing splines
that is tailored toward drawing inference about the regression
coefficients. Further, we examine the empirical properties of the
regression coefficient estimators under spatial confounding. A data example
in precision agriculture research regarding soybean yield in relation to
field conditions is presented for illustration.




-- 
Ronaldo Dias
Professor
Dept. of Statistics-IMECC, UNICAMP
www.ime.unicamp.br/~dias
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