Selection De Variables Et Regression Sur Les Quantiles
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Sélection de variables et régression sur les quantiles
Ce travail est une contribution à la sélection de modèles statistiques et plus précisément à la sélection de variables dans le cadre de régression linéaire sur les quantiles pénalisée lorsque la dimension est grande. On se focalise sur deux points lors de la procédure de sélection : la stabilité de sélection et la prise en compte de variables présentant un effet de groupe. Dans une première contribution, on propose une transition des moindres carrés pénalisés vers la régression sur les quantiles (QR). Une approche de type bootstrap fondée sur la fréquence de sélection de chaque variable est proposée pour la construction de modèles linéaires (LM). Dans la majorité des cas, l'approche QR fournit plus de coefficients significatifs. Une deuxième contribution consiste à adapter certains algorithmes de la famille « Random » LASSO (Least Absolute Solution and Shrinkage Operator) au cadre de la QR et à proposer des méthodes de stabilité de sélection. Des exemples provenant de la sécurité alimentaire illustrent les résultats obtenus. Dans le cadre de la QR pénalisée en grande dimension, on établit la propriété d'effet groupement sous des conditions plus faibles ainsi que les propriétés oracles. Deux exemples de données réelles et simulées illustrent les chemins de régularisation des algorithmes proposés. La dernière contribution traite la sélection de variables pour les modèles linéaires généralisés (GLM) via la vraisemblance nonconcave pénalisée. On propose un algorithme pour maximiser la vraisemblance pénalisée pour une large classe de fonctions de pénalité non convexes. La propriété de convergence de l'algorithme ainsi que la propriété oracle de l'estimateur obtenu après une itération ont été établies. Des simulations ainsi qu'une application sur données réelles sont également présentées.
Handbook of Quantile Regression
Quantile regression constitutes an ensemble of statistical techniques intended to estimate and draw inferences about conditional quantile functions. Median regression, as introduced in the 18th century by Boscovich and Laplace, is a special case. In contrast to conventional mean regression that minimizes sums of squared residuals, median regression minimizes sums of absolute residuals; quantile regression simply replaces symmetric absolute loss by asymmetric linear loss. Since its introduction in the 1970's by Koenker and Bassett, quantile regression has been gradually extended to a wide variety of data analytic settings including time series, survival analysis, and longitudinal data. By focusing attention on local slices of the conditional distribution of response variables it is capable of providing a more complete, more nuanced view of heterogeneous covariate effects. Applications of quantile regression can now be found throughout the sciences, including astrophysics, chemistry, ecology, economics, finance, genomics, medicine, and meteorology. Software for quantile regression is now widely available in all the major statistical computing environments. The objective of this volume is to provide a comprehensive review of recent developments of quantile regression methodology illustrating its applicability in a wide range of scientific settings. The intended audience of the volume is researchers and graduate students across a diverse set of disciplines.
Quantile Regression
Author: I. Gusti Ngurah Agung
language: en
Publisher: John Wiley & Sons
Release Date: 2021-06-21
QUANTILE REGRESSION A thorough presentation of Quantile Regression designed to help readers obtain richer information from data analyses The conditional least-square or mean-regression (MR) analysis is the quantitative research method used to model and analyze the relationships between a dependent variable and one or more independent variables, where each equation estimation of a regression can give only a single regression function or fitted values variable. As an advanced mean regression analysis, each estimation equation of the mean-regression can be used directly to estimate the conditional quantile regression (QR), which can quickly present the statistical results of a set nine QR(τ)s for τ(tau)s from 0.1 up to 0.9 to predict detail distribution of the response or criterion variable. QR is an important analytical tool in many disciplines such as statistics, econometrics, ecology, healthcare, and engineering. Quantile Regression: Applications on Experimental and Cross Section Data Using EViews provides examples of statistical results of various QR analyses based on experimental and cross section data of a variety of regression models. The author covers the applications of one-way, two-way, and n-way ANOVA quantile regressions, QRs with multi numerical predictors, heterogeneous QRs, and latent variables QRs, amongst others. Throughout the text, readers learn how to develop the best possible quantile regressions and how to conduct more advanced analysis using methods such as the quantile process, the Wald test, the redundant variables test, residual analysis, the stability test, and the omitted variables test. This rigorous volume: Describes how QR can provide a more detailed picture of the relationships between independent variables and the quantiles of the criterion variable, by using the least-square regression Presents the applications of the test for any quantile of any numerical response or criterion variable Explores relationship of QR with heterogeneity: how an independent variable affects a dependent variable Offers expert guidance on forecasting and how to draw the best conclusions from the results obtained Provides a step-by-step estimation method and guide to enable readers to conduct QR analysis using their own data sets Includes a detailed comparison of conditional QR and conditional mean regression Quantile Regression: Applications on Experimental and Cross Section Data Using EViews is a highly useful resource for students and lecturers in statistics, data analysis, econometrics, engineering, ecology, and healthcare, particularly those specializing in regression and quantitative data analysis.