Results 21 to 30 of about 4,577,308 (305)
Semi-parametric order-based generalized multivariate regression
Journal of Multivariate Analysis, 2017 In this paper, we consider a generalized multivariate regression problem where the responses are monotonic functions of linear transformations of predictors. We propose a semi-parametric algorithm based on the ordering of the responses which is invariant to the functional form of the transformation function. We prove that our algorithm, which maximizes
Milad Kharratzadeh, Mark Coates
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Semi-parametric transformation boundary regression models [PDF]
Annals of the Institute of Statistical Mathematics, 2019 In the context of nonparametric regression models with one-sided errors, we consider parametric transformations of the response variable in order to obtain independence between the errors and the covariates. We focus in this paper on stritcly increasing and continuous transformations.
Neumeyer, Natalie +2 more
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truncSP: An R Package for Estimation of Semi-Parametric Truncated Linear Regression Models
Journal of Statistical Software, 2014 Problems with truncated data occur in many areas, complicating estimation and inference. Regarding linear regression models, the ordinary least squares estimator is inconsistent and biased for these types of data and is therefore unsuitable for use ...
Maria Karlsson, Anita Lindmark
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Application of the ADMM Algorithm for a High-Dimensional Partially Linear Model
Mathematics, 2022 This paper focuses on a high-dimensional semi-parametric regression model in which a partially linear model is used for the parametric part and the B-spline basis function approach is used to estimate the unknown function for the non-parametric part ...
Aifen Feng +3 more
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Bayesian Geoadditive Seemingly Unrelated Regression [PDF]
, 2002 Parametric seemingly unrelated regression (SUR) models are a common tool for multivariate regression analysis when error variables are reasonably correlated, so that separate univariate analysis may result in inefficient estimates of covariate effects. A
Steiner, Winfried J. +3 more
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Non-parametric specification testing of non-nested econometric models [PDF]
, 1994 We consider the non-nested testing prqblem of non-parametric regressions. We show that, when the regression functions are unknown under both the null and the alternative hypotheses, an extension of the J-test procedure of Davidson and Mackinnon (1981 ...
Delgado, Miguel A. +2 more
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E-Jurnal Matematika, 2013 Dropout number is one of the important indicators to measure the human progress resources in education sector. This research uses the approaches of Semi-parametric Geographically Weighted Poisson Regression to get the best model and to determine the ...
GUSTI AYU RATIH ASTARI +2 more
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Multivariate Functional Kernel Machine Regression and Sparse Functional Feature Selection
Entropy, 2022 Motivated by mobile devices that record data at a high frequency, we propose a new methodological framework for analyzing a semi-parametric regression model that allow us to study a nonlinear relationship between a scalar response and multiple functional
Joseph Naiman, Peter Xuekun Song
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South African Journal of Industrial Engineering, 2012 Forecasting electricity consumption is one of the most important challenges in electricity system planning. This paper presents an improved semi-parametric regression model using the Student distribution function of residual to replace the nonparametric ...
Xiaojia Wang +2 more
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Semi-parametric ROC regression analysis with placement values [PDF]
Biostatistics, 2004 Advances in technology provide new diagnostic tests for early detection of disease. Frequently, these tests have continuous outcomes. One popular method to summarize the accuracy of such a test is the Receiver Operating Characteristic (ROC) curve. Methods for estimating ROC curves have long been available. To examine covariate effects, Pepe (1997, 2000)
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