Results 51 to 60 of about 2,902,778 (234)
Joint semiparametric kernel network regression
Statistics in Medicine, 2023 Variable selection and graphical modeling play essential roles in highly correlated and high‐dimensional (HCHD) data analysis. Variable selection methods have been developed under both parametric and nonparametric model settings. However, variable selection for nonadditive, nonparametric regression with high‐dimensional variables is challenging due to ...
Byung‐Jun Kim, Inyoung Kim
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Semiparametric Bayesian inference in multiple equation models [PDF]
, 2005 This paper outlines an approach to Bayesian semiparametric regression in multiple equation models which can be used to carry out inference in seemingly unrelated regressions or simultaneous equations models with nonparametric components.
Koop, Gary +2 more
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Stroke Research and Treatment, 2022 Introduction. Every year worldwide, between five to six million deaths are associated with stroke; on average, one stroke-related death occurs every four minutes.
Dereje Gebeyehu Ababu +1 more
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MODELLING OF POVERTY PERCENTAGE IN EAST JAVA PROVINCE WITH SEMIPARAMETRIC REGRESSION APPROACH
Barekeng, 2023 Poverty is an economic problem faced by all countries in the world, including Indonesia. Poverty is seen as the inability of a person from an economic standpoint to meet basic food and non-food needs as measured from the expenditure side.
Idrus Syahzaqi +2 more
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Semiparametric analysis of clustered interval‐censored survival data using soft Bayesian additive regression trees (SBART) [PDF]
Biometrics, 2020 Popular parametric and semiparametric hazards regression models for clustered survival data are inappropriate and inadequate when the unknown effects of different covariates and clustering are complex.
Piyali Basak +3 more
semanticscholar +1 more source
Semiparametric regression during 2003–2007
Electronic Journal of Statistics, 2009 Semiparametric regression is a fusion between parametric regression and nonparametric regression that integrates low-rank penalized splines, mixed model and hierarchical Bayesian methodology - thus allowing more streamlined handling of longitudinal and spatial correlation. We review progress in the field over the five-year period between 2003 and 2007.
Ruppert, D., Wand, M. P., Carroll, R. J.
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Semiparametric Single-Index Predictive Regression [PDF]
SSRN Electronic Journal, 2018 This paper studies a semiparametric single-index predictive regression model with multiple nonstationary predictors that exhibit co-movement behaviour. Orthogonal series expansion is employed to approximate the unknown link function in the model and the estimator is derived from an optimization under constraint.
Zhou, W., Gao, J., Harris, D, Kew, H.
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Semiparametric regression for discrete time-to-event data [PDF]
, 2017 : Time-to-event models are a popular tool to analyse data where the outcome variable is the time to the occurrence of a specific event of interest. Here, we focus on the analysis of time-to-event outcomes that are either intrinsically discrete or grouped
M. Berger, M. Schmid
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On Semiparametric Mode Regression Estimation [PDF]
Communications in Statistics - Theory and Methods, 2010 It has been found that, for a variety of probability distributions, there is a surprising linear relation between mode, mean, and median. In this article, the relation between mode, mean, and median regression functions is assumed to follow a simple parametric model.
Gannoun, Ali, Saracco, Jerôme, Yu, Y.
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Rank‐based estimation of propensity score weights via subclassification
Canadian Journal of Statistics, EarlyView.Abstract Propensity score (PS) weighting estimators are widely used for causal effect estimation and enjoy desirable theoretical properties, such as consistency and potential efficiency under correct model specification. However, their performance can degrade in practice due to sensitivity to PS model misspecification.
Linbo Wang +3 more
wiley +1 more source