Search results for "Mixture models"

showing 5 items of 15 documents

Ranking Scientific Journals Via Latent Class Models for Polytomous Item Response Data

2015

Summary We propose a model-based strategy for ranking scientific journals starting from a set of observed bibliometric indicators that represent imperfect measures of the unobserved ‘value’ of a journal. After discretizing the available indicators, we estimate an extended latent class model for polytomous item response data and use the estimated model to cluster journals. We illustrate our approach by using the data from the Italian research evaluation exercise that was carried out for the period 2004–2010, focusing on the set of journals that are considered relevant for the subarea statistics and financial mathematics. Using four bibliometric indicators (IF, IF5, AIS and the h-index), some…

Statistics and ProbabilityEconomics and EconometricEconomics and EconometricsClass (set theory)Research evaluationClusteringSet (abstract data type)Valutazione della Qualità delle RicercaCovariateStatisticsEconometricsFinite mixture modelsCluster analysisFinite mixture modelMathematicsGraded response modelMathematical financeItem response theory modelsItem response theory modelProbability and statisticsLatent class modelRankingStatistics Probability and UncertaintySettore SECS-S/01 - StatisticaValutazione della Qualità delle Ricerca; Clustering; Finite mixture models; Graded response model; Item response theory models; Research evaluation;Social Sciences (miscellaneous)Journal of the Royal Statistical Society Series A: Statistics in Society
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GAMLSS for high-variability data: an application to liver fibrosis case

2020

In this paper, we propose management of the problem caused by overdispersed data by applying the generalized additive model for location, scale and shape framework (GAMLSS) as introduced by Rigby and Stasinopoulos (2005). The idea of using a GAMLSS approach for handling our problem comes from the idea of Aitkin (1996) consisting in the use of an EM maximum likelihood estimation algorithm (Dempster, Laird, and Rubin, 1977) to deal with overdispersed generalized linear models (GLM). As in the GLM case, the algorithm is initially derived as a form of Gaussian quadrature assuming a normal mixing distribution. The GAMLSS specification allows the extension of the Aitkin algorithm to probability d…

Statistics and Probabilitymixture models worm plot residual analysis liver diseasesScale (ratio)Generalized additive modelliver diseases mixture models residual analysis worm plotStatistical modelProbability and statisticsGeneral MedicineVariance (accounting)ResidualMixture model01 natural sciences030218 nuclear medicine & medical imaging010104 statistics & probability03 medical and health sciences0302 clinical medicineOverdispersionEconometrics0101 mathematicsStatistics Probability and UncertaintyThe International Journal of Biostatistics
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INEQUALITIES AND TOURISM CONSUMPTION BEHAVIOUR: A MIXTURE MODEL ANALYSIS

2015

The criticism of income as a measure of well-being and trends in living standards is well known and recently scholars have been involved in defining measures to better assess material well-being and differences in living standards. Recent evidence shows that individuals improve their well-being significantly if they are able to spend on higher-order goods and services like tourism and leisure activities. In the light of that, our study proposes to explore differences in living standards in Italy by analysing the distribution of tourism expenditure. For this aim, Mixtures of Regression Models were used in order to investigate whether there is an unobserved heterogeneity in tourism consumptio…

Tourism expenditure distribution Mixture models Consumption inequality Living standards.Settore SECS-S/03 - Statistica EconomicaSettore SECS-S/01 - Statistica
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The use of diagnostic tools in GAMLSS for liver fibrosis detection

mixture modelGAMLSSliver fibrosiGAMLSS; liver fibrosis; ARFI; mixture models; ROC curveSettore SECS-S/01 - StatisticaARFIROC curve
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Clickstream Data Analysis: A Clustering Approach Based on Mixture Hidden Markov Models

2023

Nowadays, the availability of devices such as laptops and cell phones enables one to browse the web at any time and place. As a consequence, a company needs to have a website so as to maintain or increase customer loyalty and reach potential new customers. Besides, acting as a virtual point-of-sale, the company portal allows it to obtain insights on potential customers through clickstream data, web generated data that track users accesses and activities in websites. However, these data are not easy to handle as they are complex, unstructured and limited by lack of clear information about user intentions and goals. Clickstream data analysis is a suitable tool for managing the complexity of t…

model selectionhidden Markov modelsSettore SECS-S/03 - Statistica Economicaentropy based scoremixture modelsbrowsing profilesclickstream data
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