<div dir="ltr"><div>Caros,<div><br></div><div><div>Na sexta-feira, <b>29 de novembro às 14:00hs</b>, teremos <b>dois</b> seminários do Programa Interinstitucional de Pós-Graduação em Estatística (PIPGEs) da UFSCar-USP.</div><div><div><b><br></b></div><div><b>Local: </b>5-001 do ICMC-USP (São Carlos) <br></div><div><br></div></div><div>Seguem os detalhes.</div><div><br></div><div>-------------<br></div><div><br></div><div><b>Nome</b>: Luis Pericchi, Department of Mathematics and Biostatistics, University of Puerto Rico.</div><div><br></div><div><b>Título</b>: Improved Empirical Scientific Inferences through Bayesian Statistical Methodology: Some Substantive Examples.</div><div><br></div><div><b>Abstract</b>: Empirical and Bayesian reasoning complement
each other. Rather than opposing schools of statistics here we
illustrate how Bayesian Statistics can improve Empirical
Frequentist-Scientific Measures. Illustrative Examples:<div class="gmail_default">• Why does the ”impossible” happen all the time? Accurate Bayesian Prediction of Catastrophes and Extreme Values.<br>• Turning Type-I errors-α into False Discovery Rates, through Bayesian Expected Trees.<br>• Why ”most statistical findings turn out to be false”? Replacement of p-values by Bayes Factors.<br>•
Is the Extra-Sensory Perception Real? ”Do you want to reject a
Hypothesis? Take a Large Enough Sample, and Do Not Use BayesFactors”.<br>•
If you have a data set with outliers, like in the famous Darwin’s data.
Can you make predictions weighting a Normal and an Outlier prone
Distribution, by their posterior model probabilities?<br>• Applications to Health Studies by Bayesian Model Averaging and Inclusion Probabilities.<br>•
Some of the most important questions of the world are Demographic and
Modern Demography is Probabilistic and Bayesian with Hierarchical Models
across countries (United Nations Models).</div></div><div><div><br></div><div>-------------</div><div><br></div><div><div><b>Nome</b>: Clarice Demétrio, Departamento de Ciências Exatas da ESALQ/USP.</div><div><br></div><div><b>Título</b>: Overdispersion models for clustered toxicological data in a bioassay of entomopathogenic fungus.</div><div><br></div><div><b>Abstract</b>: We consider discrete mortality data for groups of individuals observed
over time. The fitting cumulative mortality curves as a function of time
involves the longitudinal modeling of the multinomial response.
Typically such data exhibit overdispersion, that is greater variation
than predicted by the multinomial distribution. To model the
extra-multinomial variation (overdispersion) we consider a
Dirichlet-multinomial model, a random intercept model, and a random
intercept and slope model. We construct asymptotic and robust covariance
matrix estimators for the regression parameter standard errors.
Applying this model to a specific insect bioassay of the fungus <i>Beauveria bassiana</i>,
we note some simple relationships in the results and explore why these
are simply a consequence of the data structure. Fitted models are used
to make inferences on the effectiveness of different fungus isolates.
The results are compared with a simple empirical analysis to provide
recommendations for the field use of this fungus as a biological
control.</div></div><span style="color:rgb(47,71,112);font-kerning:none"></span><div><br></div><div>Todos são bem vindos!</div><div><br></div><div><br></div><div><img src="cid:ii_m3r7bvb20" alt="seminarios.png" width="516" height="516"><br><br></div><div><br></div></div></div></div><div><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div> Rafael<br><br>--<br></div>Rafael Izbicki<br>Assistant Professor | Vice Director of Graduate Studies<br>Department of Statistics<br>Federal University of São Carlos (UFSCar)<br><a href="https://rafaelizbicki.com/" target="_blank">https://rafaelizbicki.com/</a><br></div><div dir="ltr"><a href="http://www.small.ufscar.br" target="_blank">www.small.ufscar.br</a><br></div><div dir="ltr"><br><br></div></div></div></div></div></div></div></div></div></div></div></div></div></div>