STAD means "city" in Dutch - tribute to the founders' background acquainted at the Leiden University in the Netherlands which countryside was flat long before the globalization. STAD is an agglomeration of persons in the flat world where a new "citizen" can aggregate from almost anywhere. This city contains a lot of "buildings" tocreate and work out ideas, provide statistical science, transfer technology, analyze data with a meta-disciplinary approach, "crossing the bridge" towards other scientific communities. This city contains also "green areas" to enjoy spare time activities together, such as sport, eating and drinking, sharing innovation and enlightenment culture, dreaming the future! Founders STAD at University of Naples Federico II are Roberta Siciliano (mentore), Valerio Tutore, Massimo Aria, Antonio D'Ambrosio, whose first letters of their surnames form, amazingly, another STAD!

Research

- B-Spline and P-Spline, differential equations and optimal smoothing
- Sequential Web Pattern Analysis
- Density Regression Trees
- Penalized smoothed probability distance in clustering of time series
- Consensus Ranking Methods
- Rank Trees for Preference Data
- Accurate Incremental Imputation of Missing Data and Data Fusion within the Statistical Learning Paradigm
- Concurvity in nonlinear and nonparametric regression models
- Ternary Classification Trees for Symbolic Data
- Web sentiment analysis
- Customer Satisfaction Analysis
- Association Rules and Market Basket Analysis
- Statistical Monitoring of Tourism
- Posterior Prediction Model of Optimal Trees
- Three-way Trees for Classification and Regression
- Generalized Additive Multi and Multi-Mixture Models
- Ensemble FAST Trees
- Neural Budget Networks of Sensorial Data
- Unconditional Latent Budget Analysis
- Multivariate Classification and Regression Trees
- Two-stage Discriminant Trees
- Multi-Class Budget Trees
- Ternary Factorial Trees
- Logistic Classification Trees and Linear Regression Trees
- Statistical Testing Pruning in Classification Trees
- TWO-CLASS Trees for Regression
- FAST algorithms for accelerating CART splitting procedure
- Two-stage splitting criteria based on predictability measures for classification and regression trees
- Factorial Discriminant Analysis and Probabilistic Models
- Simultaneous Latent Budget Analysis
- Reduced-Rank Models for Dependence Analysis of Categorical Data
- Asymptotic distribution of eigenvalues and statistical tests in Nonsymmetric Correspondence Analysis,
- Maximum likelihood estimation of Nonsymmetric Correspondence Analysis
- Non symmetrical logarithmic analysis for contingency tables