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<DIV><FONT size=3 face=Calibri><SPAN class=apple-style-span><B
style="mso-bidi-font-weight: normal"><U><SPAN
style="FONT-FAMILY: 'Arial','sans-serif'; FONT-SIZE: 18pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: pt-br; mso-fareast-language: pt-br; mso-bidi-language: ar-sa">Seminário
Conjunto UFSCar/ICMC – 19/06/2014 - 14h00</SPAN></U></B></SPAN></FONT></DIV>
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<P style="mso-line-height-alt: 13.5pt"><FONT size=3><SPAN
class=apple-style-span><B style="mso-bidi-font-weight: normal"><SPAN
style="FONT-FAMILY: 'Arial','sans-serif'; FONT-SIZE: 18pt">LOCAL:
</SPAN></B></SPAN><B style="mso-bidi-font-weight: normal"><SPAN
style="FONT-FAMILY: 'Arial','sans-serif'; FONT-SIZE: 18pt">Sala de seminários –
DEs-UFSCar </SPAN></B><SPAN
style="FONT-FAMILY: 'Tahoma','sans-serif'; FONT-SIZE: 18pt"><o:p></o:p></SPAN></FONT></P>
<P
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class=MsoNormal><FONT size=3><SPAN class=apple-style-span><B
style="mso-bidi-font-weight: normal"><SPAN
style="FONT-FAMILY: 'Arial','sans-serif'; FONT-SIZE: 18pt">TÍTULO</SPAN></B></SPAN><B
style="mso-bidi-font-weight: normal"><SPAN
style="FONT-FAMILY: 'Arial','sans-serif'; FONT-SIZE: 18pt">: </SPAN></B><B
style="mso-bidi-font-weight: normal"><SPAN
style="FONT-FAMILY: 'Arial','sans-serif'; COLOR: black; FONT-SIZE: 17pt; mso-ansi-language: en-us"
lang=EN-US>Regression Modeling based on Scale Mixtures of Skew-Normal
Distributions</SPAN></B><SPAN
style="FONT-FAMILY: myriadroman; COLOR: #001f46; FONT-SIZE: 18pt; mso-bidi-font-family: tahoma; mso-font-kerning: 18.0pt"><o:p></o:p></SPAN></FONT></P>
<P style="MARGIN: 0cm 0cm 0pt" class=MsoNormal><FONT size=3><SPAN
class=apple-style-span><B style="mso-bidi-font-weight: normal"><SPAN
style="FONT-FAMILY: 'Arial','sans-serif'; FONT-SIZE: 18pt">PALESTRANTE</SPAN></B></SPAN><B
style="mso-bidi-font-weight: normal"><SPAN
style="FONT-FAMILY: 'Arial','sans-serif'; FONT-SIZE: 18pt">: </SPAN></B><B
style="mso-bidi-font-weight: normal"><SPAN
style="FONT-FAMILY: 'Arial','sans-serif'; COLOR: black; FONT-SIZE: 18pt; mso-ansi-language: en-us"
lang=EN-US>Celso Cabral (UFAM)</SPAN></B><SPAN
style="FONT-SIZE: 18pt"><o:p></o:p></SPAN></FONT></P>
<P style="TEXT-ALIGN: justify; MARGIN: 0cm 0cm 0pt" class=MsoNormal><SPAN
class=apple-style-span><B style="mso-bidi-font-weight: normal"><SPAN
style="FONT-FAMILY: 'Arial','sans-serif'; FONT-SIZE: 18pt"><o:p><FONT
size=3> </FONT></o:p></SPAN></B></SPAN></P><FONT size=3><SPAN
class=apple-style-span><B style="mso-bidi-font-weight: normal"><SPAN
style="FONT-FAMILY: 'Arial','sans-serif'; FONT-SIZE: 18pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: pt-br; mso-fareast-language: pt-br; mso-bidi-language: ar-sa">RESUMO</SPAN></B></SPAN><B
style="mso-bidi-font-weight: normal"><SPAN
style="TEXT-TRANSFORM: uppercase; FONT-FAMILY: 'Arial','sans-serif'; FONT-SIZE: 18pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: pt-br; mso-fareast-language: pt-br; mso-bidi-language: ar-sa">:
</SPAN></B></FONT><SPAN
style="FONT-FAMILY: 'Times New Roman','serif'; FONT-SIZE: 18pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: pt-br; mso-fareast-language: pt-br; mso-bidi-language: ar-sa"><BR><BR></SPAN><B
style="mso-bidi-font-weight: normal"><SPAN
style="FONT-FAMILY: 'Arial','sans-serif'; FONT-SIZE: 14pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: pt-br; mso-fareast-language: pt-br; mso-bidi-language: ar-sa"><FONT
size=3>The traditional estimation of mixture regression models is based on the
assumption of normality (symmetry) of component errors and thus is sensitive to
outliers, heavy-tailed errors and/or asymmetric errors. We present a proposal to
deal with these issues simultaneously in the context of the mixture regression
by <BR>extending the classical normal model by assuming that the random errors
follow a scale mixtures of skew-normal distributions. This approach allows us to
model data with great flexibility, accommodating skewness and heavy tails. The
main virtue of considering the mixtures regression models under the class of
<BR>scale mixtures of skew-normal distributions is that they have a nice
hierarchical representation which allows an easy implementation of inference. We
develop a simple EM-type algorithm to perform maximum likelihood inference of
the parameters in the proposed model. In order to examine the robust aspect
<BR>of this flexible model against outlying observations, some simulation
studies have also been presented. Finally, a real data set has been analyzed,
illustrating the usefulness of the proposed methodology. <BR><BR>(Joint work
with Camila B. Zeller - Federal University of Juiz de Fora and Víctor H. Lachos
- Campinas State University)</FONT></SPAN></B></DIV>
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