Search results for "Quantile regression"

Individuazione dei trend di pioggia per la Sicilia utilizzando un approccio multiscala basato sulla regressione a quantili

2021

Uno degli argomenti più attuali e controversi è se, negli ultimi decenni, le piogge intense siano diventate più frequenti come possibile effetto dei cambiamenti climatici. La centralità del tema è giustificata dalle gravi conseguenze che l’intensificarsi di questi fenomeni, in frequenza e magnitudo, potrebbe indurre a livello sociale ed economico. Basti pensare alle “flash flood”, spesso causate da eventi di precipitazione con una forte componente convettiva, che in ambito urbano mettono in crisi i sistemi di drenaggio arrecando rilevanti danni e, purtroppo, anche perdite di vite umane. La Sicilia è stata spesso monitorata con l’obiettivo di individuare delle mutazioni significative del cic…

Do personal characteristics affect the Rasch measures of perceived physical risk? A quantile regression approach

2012

This paper focuses on the measurement of the perception of healthy risk according to personal characteristics. The Physical Risk Assessment Inventory was adopted as measurement tool and it was administered to 551 students enrolled in the first and in the fifth classes of some high schools of Palermo. The analysis of the determinants of the perceived risk is based on its quantitative measures. Therefore the analysis has been developed into two tracks. First track is devoted to obtain a quantitative measure of the perceived risk: an Extended Logistic Rasch Model was used considering separately males and females. Results highlight the different perception of risk between males and females, alt…

P-spline quantile regression: a new algorithm for smoothing parameter selection

A penalized approach to covariate selection through quantile regression coefficient models

2019

The coefficients of a quantile regression model are one-to-one functions of the order of the quantile. In standard quantile regression (QR), different quantiles are estimated one at a time. Another possibility is to model the coefficient functions parametrically, an approach that is referred to as quantile regression coefficients modeling (QRCM). Compared with standard QR, the QRCM approach facilitates estimation, inference and interpretation of the results, and generates more efficient estimators. We designed a penalized method that can address the selection of covariates in this particular modelling framework. Unlike standard penalized quantile regression estimators, in which model selec…

Parametric estimation of non-crossing quantile functions

2021

Quantile regression (QR) has gained popularity during the last decades, and is now considered a standard method by applied statisticians and practitioners in various fields. In this work, we applied QR to investigate climate change by analysing historical temperatures in the Arctic Circle. This approach proved very flexible and allowed to investigate the tails of the distribution, that correspond to extreme events. The presence of quantile crossing, however, prevented using the fitted model for prediction and extrapolation. In search of a possible solution, we first considered a different version of QR, in which the QR coefficients were described by parametric functions. This alleviated th…

Migration and students' performance: detecting geographical differences following a curves clustering approach

2020

Students’ migration mobility is the new form of migration: students migrate to improve their skills and become more valued for the job market. The data regard the migration of Italian Bachelors who enrolled at Master Degree level, moving typically from poor to rich areas. This paper investigates the migration and other possible determinants on the Master Degree students’ performance. The Clustering of Effects approach for Quantile Regression Coefficients Modelling has been used to cluster the effects of some variables on the students’ performance for three Italian macro-areas. Results show evidence of similarity between Southern and Centre students, with respect to the Northern ones.

Multiple smoothing parameters selection in additive regression quantiles

2021

We propose an iterative algorithm to select the smoothing parameters in additive quantile regression, wherein the functional forms of the covariate effects are unspecified and expressed via B-spline bases with difference penalties on the spline coefficients. The proposed algorithm relies on viewing the penalized coefficients as random effects from the symmetric Laplace distribution, and it turns out to be very efficient and particularly attractive with multiple smooth terms. Through simulations we compare our proposal with some alternative approaches, including the traditional ones based on minimization of the Schwarz Information Criterion. A real-data analysis is presented to illustrate t…

Quantile regression via iterative least squares computations

2012

We present an estimating framework for quantile regression where the usual L 1-norm objective function is replaced by its smooth parametric approximation. An exact path-following algorithm is derived, leading to the well-known ‘basic’ solutions interpolating exactly a number of observations equal to the number of parameters being estimated. We discuss briefly possible practical implications of the proposed approach, such as early stopping for large data sets, confidence intervals, and additional topics for future research.

Estimating growth charts via nonparametric quantile regression: a practical framework with application in ecology.

2013

We discuss a practical and effective framework to estimate reference growth charts via regression quantiles. Inequality constraints are used to ensure both monotonicity and non-crossing of the estimated quantile curves and penalized splines are employed to model the nonlinear growth patterns with respect to age. A companion R package is presented and relevant code discussed to favour spreading and application of the proposed methods.

Clusters of effects curves in quantile regression models

2018

In this paper, we propose a new method for finding similarity of effects based on quantile regression models. Clustering of effects curves (CEC) techniques are applied to quantile regression coefficients, which are one-to-one functions of the order of the quantile. We adopt the quantile regression coefficients modeling (QRCM) framework to describe the functional form of the coefficient functions by means of parametric models. The proposed method can be utilized to cluster the effect of covariates with a univariate response variable, or to cluster a multivariate outcome. We report simulation results, comparing our approach with the existing techniques. The idea of combining CEC with QRCM per…