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Claudio Conversano
Researcher at University of Cagliari
Publications - 74
Citations - 404
Claudio Conversano is an academic researcher from University of Cagliari. The author has contributed to research in topics: Computer science & Statistical model. The author has an hindex of 11, co-authored 65 publications receiving 317 citations. Previous affiliations of Claudio Conversano include University of Cassino & University of Naples Federico II.
Papers
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Journal ArticleDOI
Combining an Additive and Tree-Based Regression Model Simultaneously: STIMA
TL;DR: A new algorithm is proposed—Simultaneous Threshold Interaction Modeling Algorithm (STIMA)—to estimate a regression trunk model that is more general and more efficient than the initial one (RTA) and is implemented in the R-package stima.
Journal ArticleDOI
Incremental Tree-Based Missing Data Imputation with Lexicographic Ordering
TL;DR: An incremental procedure based on the iterative use of tree-based method is proposed and a suitable Incremental Imputation Algorithm is introduced to define a lexicographic ordering of cases and variables so that conditional mean imputation via binary trees can be performed incrementally.
Journal ArticleDOI
An Integrated Approach to Select Key Quality Indicators in Transit Services
TL;DR: An integrated approach is proposed, which identifies a long list of key quality indicators (KQI), defines their properties, involves experts to elicit judgments for each KQI, evaluates the long list, and points out the most promising set.
Journal ArticleDOI
On the Use of Markov Models in Pharmacoeconomics: Pros and Cons and Implications for Policy Makers.
A. Carta,Claudio Conversano +1 more
TL;DR: The main methodological features and the goals of pharmacoeconomic models that are classified in three major categories: regression models, decision trees, and Markov models are presented and decision makers are advised to interpret the results with extreme caution.
Book ChapterDOI
Decision Tree Induction
TL;DR: Decision Tree Induction is a tool to induce a classification or regression model from (usually large) datasets characterized by n objects (records), each one containing a set x of numerical or nominal attributes, and a special feature y designed as its outcome.