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Title: Estimation and Inference for Generalized Geo-additive Models

Abstract: Advancements in geographical information systems have enabled scientists to
collect data of unprecedented size over space. Nowadays, these spatial data commonly arise in
diverse elds as biology, engineering, health sciences, environment and information technology.
In many of these studies, data are collected on a count or binary response with spatial
covariate information. In this talk, we will introduce a new class of generalized geoadditive
models (GGAMs) for spatial data distributed over complex domains. Through a link function,
the proposed GGAM assumes that the mean of the discrete response variable depends on
additive univariate functions of explanatory variables and a bivariate function to adjust for
the spatial eect. We propose a two-stage approach for estimating and making inferences
of the components in the GGAM. In the rst stage, the univariate components and the
geographical component in the model are approximated via univariate polynomial splines
and bivariate penalized splines over triangulation, respectively. In the second stage, local
polynomial smoothing is applied to the cleaned univariate data to average out the variation
of the rst-stage estimators. We investigate the consistency of the proposed estimators and
the asymptotic normality of the univariate components. We also establish the simultaneous
condence band for each of the univariate components. The performance of the proposed
method is used to analyze the crash counts data in the Tampa-St. Petersburg urbanized area
in Florida.

Contact

GuanNan Wang

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