1,720,987 research outputs found
Spanning trees and identifiability of a single-factor model
No full textThe aim of this paper is to propose conditions for exploring the class
of identifiable Gaussian models with one latent variable. In particular, we focus
attention on the topological structure of the complementary graph of the residuals.
These conditions are mainly based on the presence of odd cycles and bridge edges
in the complementary graph. We propose to use the spanning tree representation
of the graph and the associated matrix of fundamental cycles. In this way it is
possible to obtain an algorithm able to establish in advance whether modifying
the graph corresponding to an identifiable model, the resulting graph still denotes
identifiability
Monitoring and improving Greek banking services using Bayesian Networks: An analysis of mystery shopping data
No full textMystery shopping is a well known marketing technique used by companies and marketing
analysts to measure quality of service, and gather information about products and services. In
this article, we analyse data from mystery shopping surveys via Bayesian networks in order to
examine and evaluate the quality of service offered by the loan departments of Greek banks.
We use mystery shopping visits to collect information about loan products and services and,
by this way, evaluate the customer satisfaction and plan improvement strategies that will assist
banks to reach their internal standards. Bayesian Networks not only provide a pictorial representation
of the dependence structure between the characteristics of interest but also allow to
evaluate, interpret and understand the effects of possible improvement strategies
Zero Inflated Bivariate Poisson Regression Models for a Sport (in)activity Data Analysis
No full textModelling scale effects in rating data: a Bayesian approach
No full textWe present a Bayesian approach for the analysis of rating data when a scaling component is taken into account, thus incorporating a specific form of heteroskedasticity. Model-based probability effect measures for comparing distributions of several groups, adjusted for explanatory variables affecting both location and scale components, are proposed. Markov Chain Monte Carlo techniques are implemented to obtain parameter estimates of the fitted model and the associated effect measures. An analysis on students’ evaluation of a university curriculum counselling service is carried out to assess the performance of the method and demonstrate its valuable support for the decision-making process
Mystery shopping at Greek banks: a Bayesian network analysis
No full textThe banking industry is highly competitive, and customer satisfaction
plays an essential rule. Hence, methods to evaluate customer satisfaction are important
for bank managers. In this work, two instruments for customer satisfaction
analysis are combined: Mystery shopping methodology and Bayesian Networks.
Mystery shoppers are used to survey and monitor the quality of customer service
and to identify areas requiring enhancement. After each visit they complete a report
prepared in advance on their service experience. Bayesian Networks are then used to
provide a pictorial representation of the dependence structure between the variables
of interest, and are used to study the effect of different improvement strategies. We
present a real data analysis concerning customer satisfaction in Greek banks
Bayesian Analysis of Value-at-Risk with Product Partition Models
No full textWe consider Bayesian estimation of Value-at-Risk (VaR) using parametric Product Partition Models (PPM). VaR is a standard tool to measure and control the market risk of an asset or a
portfolio, and it is also required for regulatory purposes. We use PPM to provide robustly Bayesian
estimators of VaR, remaining in a Normal setting, even in presence of outlying points. We consider two
different scenarios: a product partition structure on the vector of means and a product partition structure
on the vector of variances. In both frameworks we obtain a closed-form expression for VaR. The results
are illustrated with an application to a set of shares from the Italian stock market. The methodology and
the obtained results are described in details in Bormetti et al. (2009)
Bayesian Value-at-Risk with product partition models
No full textIn this paper we propose a novel Bayesian methodology for Value-at-Risk computation based on parametric Product Partition Models. Value-at-Risk is a standard tool for measuring and controlling the market risk of an asset or portfolio, and is also required for regulatory purposes. Its popularity is partly due to the fact that it is an easily understood measure of risk. The use of Product Partition Models allows us to remain in a Normal setting even in the presence of outlying points, and to obtain a closed-form expression for Value-at-Risk computation. We present and compare two different scenarios: a product partition structure on the vector of means and a product partition structure on the vector of variances. We apply our methodology to an Italian stock market data set from Mib30. The numerical results clearly show that Product Partition Models can be successfully exploited in order to quantify market risk exposure. The obtained Value-at-Risk estimates are in full agreement with Maximum Likelihood approaches, but our methodology provides richer information about the clustering structure of the data and the presence of outlying points
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