[Forum SIS] 4th Seminar "D2 Seminar Series" - Florence Center for Data Science

[datascience a unifi.it]

Dear all,

The Florence Center for Data Science is happy to present the fourth 
Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will 
be held online Friday 2nd of July 2021, from 2-3.30 pm.
The seminar will be held by Anna Gottard from the Department of 
Statistics, Computer Science, Applications “G. Parenti” and Costanza 
Conti from the Department of Industrial Engineering of the University of 
Florence.

Register in advance for this webinar:
https://us02web.zoom.us/webinar/register/WN_WkYeStnjRK6cincfDPXUZg

After registering, you will receive a confirmation email containing 
information about joining the webinar.

We hope to see you there! You are invited to invite also your students, 
PhDs and colleagues who may be interested in the Seminar (you find a 
Flyer with all the info attached).

Kind Regards,
Florence Center for Data Science

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Speaker: Anna Gottard - Department of Statistics, Computer Science, 
Applications "G. Parenti", University of Florence

Title: Circular data, conditional independence & graphical models

Abstract: Circular variables, arising in several contexts and fields, 
are characterized by periodicity. Models for studying the 
dependence/independence structure of circular variables are 
under-explored. We will discuss three multivariate circular 
distributions, the von Mises, the Wrapped Normal and the Inverse 
Stereographic distributions, focusing on their properties concerning 
conditional independence. For each of these distributions, we examine 
the main properties related to conditional independence and introduce 
suitable classes of graphical models. The usefulness of the proposal is 
shown by modelling the conditional independence among dihedral angles 
characterizing the three-dimensional structure of some proteins.


Speaker: Costanza Conti  - Department of Industrial Engineering, 
University of Florence

Title: Penalized hyperbolic-polynomial splines
(Joint work with:  Rosanna Campagna, Universit`a degli Studi della 
Campania “L. Vanvitelli”)

Abstract: The advent of P-splines, first introduced by Eilers and Marx 
in 2010 (see [4]), has led to important developments in data regression 
through splines. With the aim of generalizing polynomial P-splines, in 
[1] we have recently defined a model of penalized regression spline, 
called HP-spline, in which polynomial B-spline functions are replaced by 
Hyperbolic-Polynomial bell-shaped basis functions. HP-splines are 
defined as a solution to a minimum problem characterized by a discrete 
penalty term. They inherit from P-splines the advantages of the model, 
like the separation of the data from the spline nodes, so avoiding the 
problems of overfitting and the consequent oscillations at the edges. 
HP-splines are particularly interesting in different applications that 
require analysis and forecasting of data with exponential trends. 
Indeed, the starting idea is the definition of a polynomial-exponential 
smoothing spline model to be used in the framework of the Laplace 
transform inversion as done in [2,3]. We present some recent results on 
the existence, uniqueness, and reproduction properties of HP-splines, 
also with the aim of extending their usage to data analysis.

[1] C. Conti, R. Campagna, Penalized exponential-polynomial splines, 
Appl. Math. Letters, 118, (2021) 107--159

  [2] R. Campagna, C. Conti, S. Cuomo, Computational Error Bounds for 
Laplace Transform Inversion Based on Smoothing Splines, Appl. Math. 
Comput., 383, (2020) 125--376

[3] R. Campagna, C. Conti, S. Cuomo, Smoothing exponential-polynomial 
splines for multiexponential decay data, Dolomites Research note on 
Approximation (2019) 86--10

[4] P.H.C. Eilers and B.D.Marx, Splines, knots, and penalties, WIREs 
Comp. Stat., 2, (2010) 637-653.
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