<div dir="ltr">Caros redistas,<div><br></div><div>É com prazer que vos convido para o seminário do Henrique Bolfarine (U Texas Austin) sobre Bayesian factor analysis. O seminário ocorre na próxima quinta-feira (25/11/2021) às 16h horário de Brasília.</div><div><br></div><div>Cordialmente,</div><div><br></div><div>Luiz<br><div class="gmail_quote"><div dir="ltr" class="gmail_attr"><br></div><br><br>





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<b><span style="font-size:13.0pt;line-height:115%;font-family:"Arial",sans-serif;color:#1f4e79">RECENT ADVANCES IN BAYESIAN FACTOR ANALYSIS</span></b><b><span style="font-size:13.0pt;line-height:115%;font-family:"Arial",sans-serif;color:#1f497d">
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<span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#404040">In this presentation, we propose a decision-theoretic approach that brings to light the relation between a sparse loadings matrix and the factor dimension. This relation
 is done through a summary from the information contained in the multivariate posterior. Under the presented approach, the prior is a component of the Bayesian specification while model selection is restricted to a loss function that reflects the trade-off
 between fit and sparsity. This trade-off is then displayed in a summary that exposes the models with the best fit. We compare this new procedure with other recent approaches through a simulation study and application to real data.<u></u><u></u></span></p>
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^{<span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#595959">Texto informado pelo autor</span>}^{<span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#3b3838">.
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<b><span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#1f497d">Quando e Onde:</span></b><u></u><u></u></p>
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<span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#404040">25 de Novembro de 2021, às 16h</span><span style="color:#404040"><u></u><u></u></span></p>
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<span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#404040">Via Zoom</span><span style="color:#404040"><u></u><u></u></span></p>
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<span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#404040">Registre-se com antecedência para este seminário:
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<span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#404040">Após o registro, você receberá um e-mail de confirmação contendo informações sobre conexão no seminário.<u></u><u></u></span></p>
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<b><span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#1f4e79">Palestrante:</span></b><b><span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif"><u></u><u></u></span></b></p>
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<b><span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#404040">Henrique Bolfarine
</span></b><span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#404040"> -
</span><span style="color:#404040"> </span><span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#404040">holds a PhD in Statistics from the University of <span class="m_-1552162607769335213gmail-im">São Paulo, a BA in Applied Mathematics, and a MA
 in Statistics from the University of São Paulo. </span>Currently he is a researcher at the Salem Center for Policy, at the University of Texas at Austin. His research interests include Bayesian variable selection, MCMC methods, mixture models, factor models,
 Bayesian learning, and applications in social sciences.</span><span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#1f497d"><u></u><u></u></span></p>
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</div><br clear="all"><div><br></div>-- <br><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div dir="ltr" style="color:rgb(80,0,80)"><div dir="ltr">Luiz Max Fagundes de Carvalho</div><div dir="ltr">Lecturer, School of Applied Mathematics (EMAp), Getúlio Vargas Foundation (FGV), Brazil.</div><div dir="ltr">Praia de Botafogo, 190, Sala 511, Rio de Janeiro, RJ 22250-900.</div><div dir="ltr">Brazil</div></div></div></div></div></div></div></div>