Results 11 to 20 of about 4,577,308 (305)
VarReg: An R package for semi-parametric variance regression
SoftwareXVariance regression is used to model heteroscedasticity in terms of covariates. Algorithms already exist but they may face computational instability due to the required parameter constraints.
Kristy P Robledo, Ian C Marschner
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Relationship Between Sums Of Squares In Linear Regression And Semi-Parametric Regression
, 2008 {"references": ["Mayers, Raymond. H., Classical and Modern Regression with\nApplications, Duxbury Classical Series, United States, 1990.", "Montgomarey, C. Douglas., Peck, A. Elizabeth., Vining, G. Geoffrey.,\nIntroduction to Linear Regression Analysis, John Wiley&Sons,Inc.,\nToronto, 2001.", "Hardle, Wolfang., M\u251c\u255dller, Marlene., Sperlich,
Aydın, Dursun, Şenel, B.
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Flexible semi-parametric quantile regression models [PDF]
, 2020 This work involves interquantile identification and variable selection in two semi-parametric quantile regression models, an additive model and an additive coefficient model. In the first part, we investigate the commonality of non-parametric component functions among different quantile levels in additive regression models with fixed dimension.
Fan, Zengyan
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Semi-parametric Regression under Model Uncertainty: Economic Applications. [PDF]
Oxf Bull Econ Stat, 2019 AbstractEconomic theory does not always specify the functional relationship between dependent and explanatory variables, or even isolate a particular set of covariates. This means that model uncertainty is pervasive in empirical economics. In this paper, we indicate how Bayesian semi‐parametric regression methods in combination with stochastic search ...
Malsiner-Walli G, Hofmarcher P, Grün B.
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Fuzzy Semi-Parametric Regression Based on Z-numbers
Transactions on Fuzzy Sets and SystemsSemi-parametric regression models are highly valued for combining parametric structure with nonparametric flexibility. However, in real-world situations involving data with inherent uncertainty and variable reliability, classical fuzzy numbers are ...
Mahdi Ali Abdulhussein +2 more
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Semi-parametric bivariate polychotomous ordinal regression [PDF]
Statistics and Computing, 2015 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Francesco Donat, Giampiero Marra
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Semi-parametric Bayes regression with network-valued covariates
Machine Learning, 2022 There is an increasing recognition of the role of brain networks as neuroimaging biomarkers in mental health and psychiatric studies. Our focus is posttraumatic stress disorder (PTSD), where the brain network interacts with environmental exposures in complex ways to drive the disease progression.
Xin Ma 0022 +2 more
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Semi-parametric rank regression with missing responses
Journal of Multivariate Analysis, 2015 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Huybrechts F. Bindele, Asheber Abebe
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A semi-parametric regression model for analysis of middle censored lifetime data
Statistica, 2016 Middle censoring introduced by Jammalamadaka and Mangalam (2003), refers to data arising in situations where the exact lifetime becomes unobservable if it falls within a random censoring interval, otherwise it is observable.
Sreenivasa Rao Jammalamadaka +2 more
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icenReg: Regression Models for Interval Censored Data in R
Journal of Statistical Software, 2017 The non-parametric maximum likelihood estimator and semi-parametric regression models are fundamental estimators for interval censored data, along with standard fullyparametric regression models.
Clifford Anderson-Bergman
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