Search results for "quantile regression coefficients modeling"

showing 4 items of 4 documents

Penalized regression and clustering in high-dimensional data

The main goal of this Thesis is to describe numerous statistical techniques that deal with high-dimensional genomic data. The Thesis begins with a review of the literature on penalized regression models, with particular attention to least absolute shrinkage and selection operator (LASSO) or L1-penalty methods. L1 logistic/multinomial regression models are used for variable selection and discriminant analysis with a binary/categorical response variable. The Thesis discusses and compares several methods that are commonly utilized in genetics, and introduces new strategies to select markers according to their informative content and to discriminate clusters by offering reduced panels for popul…

High-dimensional dataQuantile regression coefficients modelingTuning parameter selectionGenomic dataLasso regressionLasso regression; High-dimensional data; Genomic data; Tuning parameter selection; Quantile regression coefficients modeling; Curves clustering;Settore SECS-S/01 - StatisticaCurves clustering
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A new approach for clustering of effects in quantile regression

2017

In this paper we aim at nding similarities among the coefficients from a multivariate regression. Using a quantile regression coefficients modeling, the effect of each covariate, given a response (also multivariate) is a curve in the multidimensional space of the percentiles. Collecting all the curves, describing the effects of each covariate on each response variable, we could be able to assess if only one or more covariates have same effects on different responses.

curves clustering; quantile regression coefficients modeling; multivariate analysis; functional datacurves clusteringmultivariate analysiSettore SECS-S/01 - Statisticaquantile regression coefficients modelingfunctional data
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Quantile Regression Coefficients Modeling: a Penalized Approach

2018

Modeling quantile regression coefficients functions permits describing the coefficients of a quantile regression model as parametric functions of the order of the quantile. This approach has numerous advantages over standard quantile regression, in which different quantiles are estimated one at the time: it facilitates estimation and inference, improves the interpretation of the results, and is statistically efficient. On the other hand, it poses new challenges in terms of model selection. We describe a penalized approach that can be used to identify a parsimonious model that can fit the data well. We describe the method, and analyze the dataset that motivated the present paper. The propose…

Lasso penalty Penalized integrated loss minimization Penalized quantile regression coefficients modeling Inspiratory capacity
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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…

Statistics and ProbabilityStatistics::TheoryMultivariate statistics05 social sciencesUnivariateFunctional data analysis01 natural sciencesQuantile regressionQuantile regression coefficients modeling Multivariate analysis Functional data analysis Curves clustering Variable selection010104 statistics & probabilityComputational Mathematics0502 economics and businessParametric modelCovariateStatistics::MethodologyApplied mathematics0101 mathematicsStatistics Probability and UncertaintyCluster analysisSettore SECS-S/01 - Statistica050205 econometrics MathematicsQuantile
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