The Experts below are selected from a list of 87909 Experts worldwide ranked by ideXlab platform
Ana Pérez-gonzález - One of the best experts on this subject based on the ideXlab platform.
-
Goodness-of-fit tests for quantile regression with missing Responses
Statistical Papers, 2019Co-Authors: Ana Pérez-gonzález, Wenceslao González-manteiga, Tomás R. Cotos-yáñez, Rosa M. Crujeiras-casaisAbstract:Goodness-of-fit tests for quantile regression models, in the presence of missing observations in the Response Variable, are introduced and analysed in this paper. The different proposals are based on the construction of empirical processes considering three different approaches which involve the use of the gradient vector of the quantile function, a linear projection of the covariates (suitable for high-dimensional settings) and a projection of the estimating equations. Besides, two types of estimators for the null parametric model to be tested are considered. The performance of the different test statistics is analysed in an extensive simulation study. An application to real data is also included.
-
Plug-in marginal estimation under a general regression model with missing Responses and covariates
TEST, 2019Co-Authors: Ana M. Bianco, Graciela Boente, Wenceslao González-manteiga, Ana Pérez-gonzálezAbstract:In this paper, we consider a general regression model where missing data occur in the Response and in the covariates. Our aim is to estimate the marginal distribution function and a marginal functional, such as the mean, the median or any $$\alpha $$ α -quantile of the Response Variable. A missing at random condition is assumed in order to prevent from bias in the estimation of the marginal measures under a non-ignorable missing mechanism. We give two different approaches for the estimation of the Responses distribution function and of a given marginal functional, involving inverse probability weighting and the convolution of the distribution function of the observed residuals and that of the observed estimated regression function. Through a Monte Carlo study and two real data sets, we illustrate the behaviour of our proposals.
Solve Sæbø - One of the best experts on this subject based on the ideXlab platform.
-
Linear regression with bivariate Response Variable containing missing data. Strategies to increase prediction precision
Communications in Statistics - Simulation and Computation, 2019Co-Authors: Lars Erik Gangsei, Trygve Almøy, Solve SæbøAbstract:AbstractIn this study we focus on prediction precision for linear regression with a bivariate Response Variable, where the Response Variable of primary interest contains missing data in the trainin...
-
Theoretical evaluation of prediction error in linear regression with a bivariate Response Variable containing missing data
Communications in Statistics-theory and Methods, 2016Co-Authors: Lars Erik Gangsei, Trygve Almøy, Solve SæbøAbstract:ABSTRACTMethods for linear regression with multivariate Response Variables are well described in statistical literature. In this study we conduct a theoretical evaluation of the expected squared prediction error in bivariate linear regression where one of the Response Variables contains missing data. We make the assumption of known covariance structure for the error terms. On this basis, we evaluate three well-known estimators: standard ordinary least squares, generalized least squares, and a James–Stein inspired estimator. Theoretical risk functions are worked out for all three estimators to evaluate under which circumstances it is advantageous to take the error covariance structure into account.
Shintson Wu - One of the best experts on this subject based on the ideXlab platform.
-
submillisecond Response Variable optical attenuator based on sheared polymer network liquid crystal
Optics Express, 2004Co-Authors: Yunghsun Wu, Yanqing Lu, Janet R Wu, Shintson WuAbstract:We demonstrate a Variable optical attenuator (VOA) at λ=1.55 µm using a sheared polymer network liquid crystal (SPNLC). The SPNLC exhibits a fast Response time and weak wavelength dependency. Comparing with other polymer-stabilized liquid crystals, the SPNLC has lower driving voltage and negligible light scattering loss when the wavelength exceeds 700 nm. A reflection type VOA with ~0.24 ms Response time and -32 dB dynamic range is demonstrated at room temperature and 35 Vrms voltage.
Thomas Kneib - One of the best experts on this subject based on the ideXlab platform.
-
Noncrossing structured additive multiple-output Bayesian quantile regression models
Statistics and Computing, 2020Co-Authors: Bruno Santos, Thomas KneibAbstract:Quantile regression models are a powerful tool for studying different points of the conditional distribution of univariate Response Variables. Their multivariate counterpart extension though is not straightforward, starting with the definition of multivariate quantiles. We propose here a flexible Bayesian quantile regression model when the Response Variable is multivariate, where we are able to define a structured additive framework for all predictor Variables. We build on previous ideas considering a directional approach to define the quantiles of a Response Variable with multiple outputs, and we define noncrossing quantiles in every directional quantile model. We define a Markov chain Monte Carlo (MCMC) procedure for model estimation, where the noncrossing property is obtained considering a Gaussian process design to model the correlation between several quantile regression models. We illustrate the results of these models using two datasets: one on dimensions of inequality in the population, such as income and health; the second on scores of students in the Brazilian High School National Exam, considering three dimensions for the Response Variable.
Y. Vander Heyden - One of the best experts on this subject based on the ideXlab platform.
-
Raman spectroscopy as a process analytical technology (PAT) tool for the in-line monitoring and understanding of a powder blending process
Journal of Pharmaceutical and Biomedical Analysis, 2008Co-Authors: T. R.m. De Beer, C. Bodson, Y. Vander Heyden, Bieke Dejaegher, Pauline Vercruysse, Anneleen Burggraeve, Laurent Delattre, A. Lemos, Beata Walczak, Jean Paul RemonAbstract:The aim of this study is to propose a strategy to implement a PAT system in the blending step of pharmaceutical production processes. It was examined whether Raman spectroscopy can be used as PAT tool for the in-line and real-time endpoint monitoring and understanding of a powder blending process. A screening design was used to identify and understand the significant effects of two process Variables (blending speed and loading of the blender) and of a formulation Variable (concentration of active pharmaceutical ingredient (API): diltiazem hydrochloride) upon the required blending time (Response Variable). Interactions between the Variables were investigated as well. A Soft Independent Modelling of Class Analogy (SIMCA) model was developed to determine the homogeneity of the blends in-line and real-time using Raman spectroscopy in combination with a fiber optical immersion probe. One blending experiment was monitored using Raman and NIR spectroscopy simultaneously. This was done to verify whether two independent monitoring tools can confirm each other's endpoint conclusions. The analysis of the experimental design results showed that the measured endpoints were excessively rounded due to the large measurement intervals relative to the first blending times. This resulted in effects and critical effects which cannot be interpreted properly. To be able to study the effects properly, the ratio between the blending times and the measurement intervals should be sufficiently high. In this study, it anyway was demonstrated that Raman spectroscopy is a suitable PAT tool for the endpoint control of a powder blending process. Raman spectroscopy not only allowed in-line and real-time monitoring of the blend homogeneity, but also helped to understand the process better in combination with experimental design. Furthermore, the correctness of the Raman endpoint conclusions was demonstrated for one process by using a second independent endpoint monitoring tool (NIR spectroscopy). Hence, the use of two independent techniques for the control of one Response Variable not only means a mutual confirmation of both methods, but also provides a higher certainty in the determined endpoint. © 2008 Elsevier B.V. All rights reserved.
-
The use of CART and multivariate regression trees for supervised and unsupervised feature selection
Chemometrics and Intelligent Laboratory Systems, 2005Co-Authors: Frederik Questier, R. Put, D. Coomans, Beata Walczak, Y. Vander HeydenAbstract:Feature selection is a valuable technique in data analysis for information-preserving data reduction. This paper describes Classification and Regression Trees (CART) and Multivariate Regression Trees (MRT)-based approaches for both supervised and unsupervised feature selection. The well-known CART method allows to perform supervised feature selection by modeling one Response Variable (y) by some explanatory Variables (x). The recently proposed CART extension, MRT can handle more than one Response Variable (y). This allows to perform a supervised feature selection in the presence of more than one Response Variable. For unsupervised feature selection, where no Response Variables are available, we propose Auto-Associative Multivariate Regression Trees (AAMRT) where the original Variables (x) are not only used as explanatory Variables (x), but also as Response Variables (y=x). Since (AA)MRT is grouping the objects into groups with similar Response values by using explanatory Variables, this means that the Variables are found which are most responsible for the cluster structure in the data. We will demonstrate how these approaches can improve (the detection of) the cluster structure in data and how they can be used for knowledge discovery. © 2004 Elsevier B.V. All rights reserved.
