196,532 research outputs found
Smoothing Score Algorithm for Generalized Additive Models
No full textIn the framework of Generalized Additive Models (GAM) an automatic
data-driven procedure is introduced for assigning an appropriate smoother to each
covariate and for defining an ordering entrance for the covariates in the model.
The resulting Smoothing Score algorithm aims to improve model indentifiability. It
uses the bagging procedure in order to select the smoothers to be assigned to each
covariate and a new scoring measure able to rank the candidate smoothers with
respect to their bagged predictive accuracy. The adequacy of this scoring measure
is evaluated on artificial data. A comparison between the smoothing score algorithm
and the standard GAM is made using real data concerning a classification task
Psychological factors as determinants of chronic conditions: Clinical and psychodynamic advances
No full textOpinion articl
A NOTE ON MODEL SELECTION IN STIMA
No full textSimultaneous Threshold Interaction Modeling Algorithm (STIMA) has
been recently introduced in the framework of statistical modeling as a tool enabling
to automatically select interactions in a Generalized Linear Model (GLM) through
the estimation of a suitable defined tree structure called ”trunk”. STIMA integrates
GLM with a classification tree algorithm or a regression tree one, depending on the
nature of the response variable (nominal or numeric). Accordingly, it can be based
on the Classification Trunk Approach (CTA) or on the Regression Trunk Approach
(RTA). In both cases, interaction terms are expressed as ”threshold interactions” instead
of traditional cross-products. Compared with standard tree-based algorithms,
STIMA is based on a different splitting criterion as well as on the possibility to
”force” the first split of the trunk by manually selecting the first splitting predictor.
This paper focuses on model selection in STIMA and it introduces an alternative
model selection procedure based on a measure which evaluates the trade-off
between goodness of fit and accuracy. Its performance is compared with the one
deriving from the current implementation of STIMA by analyzing two real datasets
Stairway to heaven: An emotional journey in Divina Commedia with threshold-based Naïve Bayes classifier
No full textComputational literary uses data science and computer science techniques to study literature. In this framework, we investigate how an expert system can acquire knowledge from the specific content of a narrative text without any pre-existing information about it. We utilize the Threshold-based Naïve Bayes (Tb-NB) classifier to analyze the content of Dante Alighieri’s Divina Commedia poem. Tb-NB is a probabilistic data-driven model that predicts the polarity of a binary response based on the probability of an event occurring given certain features, and assigns a log-likelihood score to each word in a text. Our first task is understanding if and how the links between lexical forms and meanings characterize the three parts of the poem (Inferno, Purgatorio and Paradiso) in order to predict if a Canto belongs to Inferno or Paradiso based on its specific content, and to determine if a Canto of Purgatorio is more similar to those of Inferno or to those of Paradiso. We show Tb-NB outperform other similar approaches and achieves the same performance of Random Forest (F1-score = 0.985) but providing much more information to interpret the specific content and the lexical forms used by Dante Alighieri in its poem. The Tb-NB’s scores are the base of knowledge for the implementation of an expert system, like a search engine, that can help users to identify the most informative verses of a Canto or by better comprehend or discover the content of the poem from a word related to a particular feeling or emotion
“Acceptance-rejection in Alzheimer’s Dementia: A Study of Caregivers”, I° International Congress on Interpersonal Acceptance and Rejection
No full text
Attività del gruppo vaccini della SItI e calendario vaccinale per la vita
No full textAs a consequence of the revised title 5th of the Italian Constitution, the regional devolution on health organization
has led to a significant five-year drift in the vaccination offer following the absence of a National
Immunization Plan. Thus, the Società Italiana di Igiene Medicina Preventiva e Sanità Pubblica (SItI) identified
the need to provide disoriented public health operators a scientific support for a larger immunization offer
based on the equity principles. Consequently, the SItI Vaccine Working Group issued numerous evidence-based
recommendations on the immunization policies and practices (for instance, the Childhood Immunization
Schedule, the Adulthood Immunization Schedule, and so on), based on the interactive discussions with other
national scientific societies and all the stakeholders in the field. Eventually, it is worthwhile to mention the
last outcome of this fruitful cooperation: the Immunization Schedule for Life, issued in March 2012. It emphasizes
the early start of adequate vaccination practices in the childhood, followed by the integration of
other relevant immunization practices in the adolescence and adulthood until the elderly. This holistic and
integrated approach could allow to reach the quality objectives, underlined in the new 2012-2014 National
Prevention Plan, and to satisfy the equity principle of a homogeneous vaccination offer countrywide
Cryptocurrency ecosystems and social media environments: An empirical analysis through Hawkes’ models and natural language processing
No full textCopyright © 2021 The Author(s). We analyse, using a mixture of statistical models and natural language process techniques, what happened in social media from June 2019 onwards to understand the relationships between Cryptocurrencies’ prices and social media, focusing on the rise of the Bitcoin and Ethereum prices. In particular, we identify and model the relationship between the cryptocurrencies market price changes, and sentiment and topic discussion occurrences on social media, using Hawkes’ Model. We find that some topics occurrences and rise of sentiment in social media precedes certain types of price movements. Specifically, discussions concerning governments, trading, and Ethereum cryptocurrency as an exchange currency appear to negatively affect Bitcoin and Ethereum prices. Those concerning investments, appear to explain price rises, whilst discussions related to new decentralized realities and technological applications explain price falls. Finally, we validate our model using a real case study: the already famous case of ”Wallstreetbet and GameStop”1 that took place in January 2021.Funding: No funding was received for this work
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