A5046058157- Sensory Analysis and Statistical Methods
- Statistical Methods and Bayesian Inference
- Bayesian Methods and Mixture Models
- Advanced Statistical Methods and Models
- Economic and Environmental Valuation
- Complex Systems and Time Series Analysis
- Multi-Criteria Decision Making
- Time Series Analysis and Forecasting
- Bayesian Modeling and Causal Inference
- Customer Service Quality and Loyalty
- Financial Risk and Volatility Modeling
- Forecasting Techniques and Applications
- Consumer Market Behavior and Pricing
- Wine Industry and Tourism
- Survey Sampling and Estimation Techniques
- Pancreatic function and diabetes
- Cognitive and psychological constructs research
- Urban Planning and Valuation
- Hydrological Forecasting Using AI
- Meat and Animal Product Quality
- Evaluation and Performance Assessment
- Color perception and design
- Hydrology and Drought Analysis
- Psychological Well-being and Life Satisfaction
- Diabetes and associated disorders
University of Naples Federico II
2013-2025
Istituto Nazionale di Fisica Nucleare, Sezione di Napoli
1984-1990
Abstract. In a number of practical problems where clustering or choosing from set dynamic structures is needed, the introduction distance between data an early step in application multivariate statistical methods. this paper parametric approach proposed order to introduce well‐defined metric on class autoregressive integrated moving‐average (ARIMA) invertible models as Euclidean their expansions. Two case studies for economic time series and assessing consistency seasonal adjustment...
Social sustainability relies on the promotion of social processes and structures that ensure basic needs individuals communities are met while also encouraging constructive interactions among them. This paper offers an overview characteristics Italy’s peripheral rural areas presents findings from a targeted survey conducted across three southern Italian regions. Evaluations collected sample residents were analyzed using class CUB models, which suitable for preference opinion data. Subjective...
We present a new statistical approach to measure customer satisfaction aimed at understanding theoretical and empirical evidence about the causal relationships among motivations, personal characteristics expressed agreement. The is based on mixture model that able express stated evaluation via subjects' covariates. Specifically, it examines compares uncertainty of answer feeling towards items. After brief review current approaches methods for ordinal data, we provide discussion our proposal...
Summary Ordinal measurements as ratings, preference and evaluation data are very common in applied disciplines, their analysis requires a proper modelling approach for interpretation, classification prediction of response patterns. This work proposes comparative discussion between two statistical frameworks that serve these goals: the established class cumulative models mixtures discrete random variables, denoted CUB models, whose peculiar feature is specification an uncertainty component to...
We introduce cube models with covariates, a class of discrete mixture distributions able to take uncertainty and overdispersion ordinal data into account. The main result the paper concerns analytical derivation observed variance–covariance matrix this model, necessary step for asymptotic inference about estimated parameters model validation. emphasize some computational aspects procedure discuss usefulness approach on real case study.
D'Elia and Piccolo (2005) have recently proposed a mixture distribution, named CUB, for ordinal data. The use of such distribution modelling ratings is justified by the following consideration: judgment that subject expresses result two components, uncertainty selectiveness. possibility relating parameters CUB models to covariates makes formulation interesting practical applications In this case study, sample 224 fair-trade coffee consumers were interviewed at stores. With data-set, model...
The present paper deals with the robustness of estimators and tests for ordinal response models. In this context, gross-errors in variable, specific deviations due to some respondents’ behavior, outlying covariates can strongly affect reliability maximum likelihood that related test procedures. highlights choice link function inferential methods, presents a comparison among most frequently used links. Subsequently robust $M$-estimators are proposed as an alternative estimators. Their...
The Autoregressive metric was firstly introduced in 1983 as a tool for choosing representative element from large collection of time series and clustering temporal data. proposal has been extended to many contexts raised increasing interests both methods applications. main results concerning this metric, its asymptotic distribution some operational comparative issues are presented. A discussion about the merits distance criterion caveats usage conclude paper.