1,721,076 research outputs found
Bayesian binary quantile regression for the analysis of Bachelor-Master transition
No full textThe multi-cycle organization of the modern university systems stimulates
the interest in studying the progression to higher level degree courses during the
academic career. In particular, after the achievement of the first level qualification
(Bachelor degree), students have to decide whether to continue their university studies,
by enrolling in a second level (Master) programme, or to conclude their training
experience. In this work we propose a binary quantile regression approach to analyse
the Bachelor-Master transition adopting the Bayesian inferential perspective.
Quantile regression represents a well-established and useful device to gain a more
in-depth understanding of the relation between the outcome of interest and the explanatory
variables. By using the data augmentation strategy, quantile regression
modeling for continuous responses has been recently extended for the treatment of
binary response variables. We illustrate the utility of the Bayesian binary quantile
regression approach to characterize the non-continuation decision with an application
to administrative data of Bachelor graduates at the Faculty of Economics of
“’Sapienza” University of Rome
Bayesian binary quantile regression for the analysis of Bachelor-to-Master transition
Full text linkThe multi-cycle organization of modern university systems stimulates the interest in studying the progression to higher level degree courses during the academic career. In particular, after the achievement of the first level qualification (Bachelor degree), students have to decide whether to continue their university studies, by enrolling in a second level (Master) programme, or to conclude their training experience. In this work we propose a binary quantile regression (BQR) approach to analyse the Bachelor-to-Master transition phenomenon with the adoption of the Bayesian inferential perspective. In addition to the traditional predictors of academic outcomes, such as the personal characteristics and the field of study, different aspects of student's performance are considered. Moreover, the role of a new contextual variable, representing the type of university regulations experienced during the academic path, is investigated. The utility of the Bayesian BQR to characterize the non-continuation decision after the first cycle studies is illustrated with an application to administrative data of Bachelor graduates at the School of Economics of Sapienza University of Rome. The method favourably compares with more conventional model specifications concerning the conditional mean of the binary respons
Multiple seasonal cycles forecasting model: the Italian electricity demand
Full text linkForecasting energy load demand data based on high frequency time series
has become of primary importance for energy suppliers in nowadays competitive
electricity markets. In this work, we model the time series of Italian electricity consumption
from 2004 to 2014 using an exponential smoothing approach. Data are
observed hourly showing strong seasonal patterns at different frequencies as well as
some calendar effects.We combine a parsimonious model representation of the intraday
and intraweek cycles with an additional seasonal term that captures the monthly
variability of the series. Irregular days, such as public holidays, are modelled separately
by adding a specific exponential smoothing seasonal term. An additive ARMA
error term is then introduced to lower the volatility of the estimated trend component
and the residuals’ autocorrelation. The forecasting exercise demonstrates that
the proposed model performs remarkably well, in terms of lower root mean squared
error and mean absolute percentage error criteria, in both short term and medium term
forecasting horizons
Dynamic Quantile Regression Forest
No full textLe potenzialità degli algoritmi di machine learning per la valutazione dei
rischi di mercato sono ancora poco conosciute, in particolar modo per quel che
concerne il calcolo del Value-at-Risk (VaR). Lo scopo di questo lavoro, dunque, è
quello di introdurre la regression forest quantilica dinamica, un modello che unisce
le regression forest con il calcolo dinamico del VaR, ossia tenendo conto
dell’evoluzione del quantile nel tempo: in questo senso il modello è definito dinamico
in quanto permette di stimare la distribuzione condizionata della variabile tenendo
conto, fra le altre covariate, anche dell’evoluzione del quantile nel tempo.The potential of machine learning algorithms in the assessment of market
risks has not been completely investigated in the literature, such as in the forecasting
Value-at-Risk (VaR). In this paper we introduce the Dynamic Quantile Regression
Forest, a model combining Quantile Regression Forests with a dynamic VaR. The
model is dynamic as the quantile prediction of the previous random forest becomes
part of the training set used to train the next random forest. Thus, it is possible to
estimate the response variable conditional distribution by accounting for the evolution
of the quantile over time among other covariates
Mixture of conjugate prior distributions and large deviations for level crossing probabilities
No full textIn this paper we preset asymptotic estimates of level crossing probabilities from a Bayesian point of view, based on large deviation. For the Bayesian analysis we chose a finite mixture of conjugate prior distributions to model the uncertenty of the unknow parameters of two classes of stochastic process considered: the brownian motion and the compound Poisson process with upward jumps and negative drift. The estimate of level crossing probabilities are derived as a consequence of large deviation principles of posterior distributions
- …
