[Forum SIS] Prossimi seminari: M. Geraci e T. Schimid

[gottard]

- PROSSIMI SEMINARI -

14 Giugno  2018 ore 12.00

Marco Geraci (University of South Carolina)  terrà il seguente seminario : 

A brief history of linear quantile mixed models and recent developments in nonlinear and additive regression

What follows is a story that began about sixteen years ago in Viale Morgagni (with some of the events taking place in a cottage of the Montalve’s estate). In this talk, I will retrace the steps that led me to develop linear quantile mixed models (LQMMs). These models have found application in public health, preventive medicine, virology, genetics, anesthesiology, immunology, ophthalmology, orthodontics, cardiology, pharmacology, biochemistry, biology, marine biology, environmental, climate and marine sciences, psychology, criminology, gerontology, economics and finance, linguistic and lexicography. Supported by a grant from the National Institute of Child Health and Human Development, I recently extended LQMMs to nonlinear and additive regression. I will present models, estimation algorithms and software, along with a few applications.

Referente: Alessandra Mattei


18 Giugno  2018 ore 12.00

Timo Schimid (Freie Universität Berlin)  terrà il seguente seminario : 

Data-driven transformations in small area estimation: An application with the R-package emdi

Small area models typically depend on the validity of model assumptions. For example, a commonly used version of the Empirical Best Predictor relies on the Gaussian assumptions of the error terms of the linear mixed regression model, a feature rarely observed in applications with real data. The present paper proposes to tackle the potential lack of validity of the model assumptions by using data-driven scaled transformations as opposed to ad-hoc chosen transformations. Different types of transformations are explored, the estimation of the transformation parameters is studied in detail under the linear mixed regression model and transformations are used in small area prediction of linear and non-linear parameters. Mean squared error estimation that accounts for the uncertainty due to the estimation of the transformation parameters is explored using bootstrap approaches. The proposed methods are illustrated using real survey and census data for estimating income deprivation parameters for municipalities in Mexico with the R-package emdi. The package enables the estimation of regionally disaggregated indicators using small area estimation methods and includes tools for (a) customized parallel computing, (b) model diagnostic analyses, (c) creating high quality maps and (d) exporting the results to Excel and OpenDocument Spreadsheets are included. Simulation studies and the results from the application show that using carefully selected, data-driven transformations can improve small area estimation.

Referente: Alessandra Petrucci

I seminari saranno tenuti presso l’aula 32 del DiSIA, Viale Morgagni n. 59 - 50134 Firenze.

Tutti gli interessati sono cordialmente invitati a partecipare.

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Anna Gottard
Dipartimento di Statistica Informatica Applicazioni
Università di Firenze
V.le Morgagni 59, Firenze

gottard a disia.unifi.it
http://local.disia.unifi.it/gottard/
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