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<DIV><FONT face=Calibri><SPAN class=apple-style-span><B
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Conjunto UFSCar/ICMC-USP – 21/11/2014 - 14h00</SPAN></U></B></SPAN></FONT></DIV>
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style="FONT-FAMILY: 'Arial','sans-serif'">LOCAL: Sala 4-005 –
ICMC-USP</SPAN></B></SPAN><B style="mso-bidi-font-weight: normal"><SPAN
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<H1 style="MARGIN: auto 0cm"><SPAN class=apple-style-span><SPAN
style="FONT-FAMILY: 'Arial','sans-serif'; FONT-SIZE: 12pt">TÍTULO</SPAN></SPAN><SPAN
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face="Times New Roman">: </FONT></SPAN></SPAN><SPAN class=apple-style-span><SPAN
style="FONT-FAMILY: 'Arial','sans-serif'; FONT-SIZE: 12pt; mso-bidi-font-weight: normal; mso-font-kerning: 0pt">STANOVA:
A smoothed-ANOVA-based model for spatio-temporal disease
mapping</SPAN></SPAN><SPAN style="FONT-SIZE: 12pt"><o:p></o:p></SPAN></H1>
<P style="LINE-HEIGHT: 13.5pt"><SPAN class=apple-style-span><SPAN
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<P style="LINE-HEIGHT: 13.5pt"><FONT size=3><SPAN class=apple-style-span><SPAN
style="FONT-FAMILY: 'Arial','sans-serif'">PALESTRANTE</SPAN><FONT
face="Times New Roman">: </FONT></SPAN><SPAN class=apple-style-span><SPAN
style="FONT-FAMILY: 'Arial','sans-serif'">Francisco Torres-Aviles (Universidad
de Santiago de Chile)</SPAN><o:p></o:p></SPAN></FONT></P>
<P style="TEXT-ALIGN: justify; MARGIN: 0cm 0cm 0pt"><SPAN
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<P style="TEXT-ALIGN: justify; MARGIN: 0cm 0cm 0pt"><FONT size=3><SPAN><SPAN
style="FONT-FAMILY: 'Arial','sans-serif'">RESUMO</SPAN>: </SPAN><BR><SPAN
class=apple-style-span><SPAN
style="FONT-FAMILY: 'Arial','sans-serif'">Spatio-temporal disease mapping can be
viewed as a multivariate disease mapping problem with a given order of the
geographic patterns to be studied. As a consequence, some of the techniques in
multivariate literature could also be used to build spatio-temporal models. In
this paper we propose using the smoothed ANOVA multivariate model for
spatio-temporal problems. Under our approach the time trend for each geographic
unit is modeled parametrically, projecting it on a preset orthogonal basis of
functions (the contrasts in the smoothed ANOVA nomenclature), while the
coefficients of these projections are considered to be spatially dependent
random effects. Despite the parametric temporal nature of our proposal, we show
with both simulated and real datasets that it may be as flexible as other
spatio-temporal smoothing models proposed in the literature and may model
spatio-temporal data with several sources of variability. (*) Joint work with
Miguel A. Martínez-Beneito (FISABIO and CIBERESP,
Spain)<o:p></o:p></SPAN></SPAN></FONT></P></SPAN></B></SPAN></FONT></DIV>
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