Results 151 to 160 of about 5,704 (194)

Integrating preliminary test and Stein-type techniques to improve estimation in the time-dependent Cox model. [PDF]

PLoS One
Ramezani R, Rabiei MR, Arashi M.
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Semiparametric Mode Regression

, 2020
Oelker, M. R., Sobotka, F., Klein, N., Kneib, Thomas   +3 more
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Deep learning for the change-point Cox model with current status data. [PDF]

Lifetime Data Anal
Huang Q, Feng A, Wu Q, Tong X.
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Information-Entropic Deep Learning with Gaussian Process Regularisation for Uncertainty-Aware Quantitative Trading. [PDF]

Entropy (Basel)
Lin F, Sun H.
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Efficiency Bounds for Semiparametric Regression

Econometrica, 1992
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Semiparametric Regression

2003
Semiparametric regression is concerned with the flexible incorporation of non-linear functional relationships in regression analyses. Any application area that benefits from regression analysis can also benefit from semiparametric regression. Assuming only a basic familiarity with ordinary parametric regression, this user-friendly book explains the ...
David Ruppert, M. P. Wand, R. J. Carroll
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SEMIPARAMETRIC TIME SERIES REGRESSION

Journal of Time Series Analysis, 1994
Abstract.Let (Xi,Yi),i= 0, pL 1,… denote a bivariate stationary time series withXibeing Rd‐valued andYibeing real‐valued. We consider the regression modelYi=θ(Xi) +Zi, where θ(·) is an unknown function and Ziis an autoregressive process. Given a realization of lengthn, we examine the problem of estimating the nonparametric function θ(·) and the ...
Truong, Young K., Stone, Charles J.
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Semiparametric regression model selections

Journal of Statistical Planning and Inference, 1999
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shi, Peide, Tsai, Chih-Ling
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Semiparametric Regression Functionals

Journal of the American Statistical Association, 1995
Abstract A regression method is developed for a general class of functionals. A semiparametric linear model is adopted, and the regression parameters are estimated by maximizing a profiled nonparametric or empirical likelihood based on a local estimate of the conditional distribution function.
Michael Leblanc, John Crowley
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Root-N-Consistent Semiparametric Regression

Econometrica, 1988
Summary: One type of semiparametric regression on an \({\mathcal R}\) \(p\times {\mathcal R}\) q-valued random variable (X,Z) is \(\beta 'X+\theta (Z)\), where \(\beta\) and \(\theta\) (Z) are an unknown slope coefficient vector and function, and X is neither wholly dependent on Z nor necessarily independent of it.
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