Results 151 to 160 of about 5,885 (195)

Causal K-Means Clustering. [PDF]

J R Stat Soc Series B Stat Methodol
Kim K, Kim J, Kennedy EH.
europepmc   +1 more source

Doubly robust and efficient calibration of prediction sets for right-censored time-to-event outcomes. [PDF]

Biometrika
Farina R, Tchetgen Tchetgen E, Kuchibhotla AK.   +2 more
europepmc   +1 more source

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity. [PDF]

Stat Med
Liu J, Xu K, Ferguson JF, Kang K, Wang Y, Qiu Y, Shao L, Tu S, Nguyen TT, Lin T, Zhang X.   +10 more
europepmc   +1 more source

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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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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 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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New Ridge Regression Estimator in Semiparametric Regression Models

Communications in Statistics - Simulation and Computation, 2015
In the context of ridge regression, the estimation of shrinkage parameter plays an important role in analyzing data. Many efforts have been put to develop the computation of risk function in different full-parametric ridge regression approaches using eigenvalues and then bringing an efficient estimator of shrinkage parameter based on them.
Mahdi Roozbeh, Mohammad Arashi
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Influence diagnostics in semiparametric regression models

Statistics & Probability Letters, 2002
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Kim, Choongrak, Park, Byeong U., Kim, Woochul   +2 more
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