Spina, Stefania
(2014)
Network data in the Partial Least Squares framework.
[Tesi di dottorato]
| Item Type: |
Tesi di dottorato
|
| Lingua: |
English |
| Title: |
Network data in the Partial Least Squares framework |
| Creators: |
| Creators | Email |
|---|
| Spina, Stefania | stefania.spina@unina.it |
|
| Date: |
30 March 2014 |
| Number of Pages: |
134 |
| Institution: |
Università degli Studi di Napoli Federico II |
| Department: |
Scienze Economiche e Statistiche |
| Scuola di dottorato: |
Scienze economiche e statistiche |
| Dottorato: |
Statistica |
| Ciclo di dottorato: |
26 |
| Coordinatore del Corso di dottorato: |
| nome | email |
|---|
| Lauro, Carlo Natale | clauro@unina.it |
|
| Tutor: |
| nome | email |
|---|
| Giordano, Giuseppe | UNSPECIFIED | | Palumbo, Francesco | UNSPECIFIED |
|
| Date: |
30 March 2014 |
| Number of Pages: |
134 |
| Uncontrolled Keywords: |
Network data, Network Effects Model, PLS-PM, Social Influence. |
| Settori scientifico-disciplinari del MIUR: |
Area 13 - Scienze economiche e statistiche > SECS-S/01 - Statistica
Area 13 - Scienze economiche e statistiche > SECS-S/05 - Statistica sociale |
[error in script]
[error in script]
| Date Deposited: |
10 Apr 2014 17:03 |
| Last Modified: |
15 Jul 2015 01:01 |
| URI: |
http://www.fedoa.unina.it/id/eprint/9827 |
Abstract
This thesis stems from the idea to draw a statistical soft-modeling framework
to network data.
Network data arise in very different and multidisciplinary fields in order to
study relational ties among units. The different fields highlighted in recent
years the necessity to collect relational and attribute data, as well as metadata
describing the actors in the network.
Since usual relational datasets are characterized by i) very different amount
of units (from very few units to huge networks), ii) biased sampling (for
instance, people with more social connections may have a higher chance
of selection) and iii) a kind of heterogeneous information attached to both
nodes and ties; these facets highlight the peculiarity for classical statistical
tools and models to be applied.
In the specific, we are interested in processes where social relations provide
a basis for the alteration of an attitude or behavior by one actor in response
to another one.
This social process of attitude change, that appears in a social network, is
known as social in
uence or contagion.
A mathematical formalization of the effects of social network on behaviors
is given by the Network Effects Model.
From an empirical point of view, these models are far from being directly
observable.
The possibility of measuring them as latent factors depending from multidimensional
constructs still remains.
All together, a component-based approach to network data through Partial
Least Squares-path model algorithms is proposed.
A simulation study is presented.
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