Results 211 to 220 of about 6,656,167 (244)

Checking a Semiparametric Additive Risk Model

Lifetime Data Analysis, 2005
McKeague and Sasieni [A partly parametric additive risk model. Biometrika 81 (1994) 501] propose a restriction of Aalen's additive risk model by the additional hypothesis that some of the covariates have time-independent influence on the intensity of the observed counting process.
Gandy, Axel, Jensen, Uwe
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Approximate Standard Errors in Semiparametric Models

Biometrics, 1999
Summary.SUMMARY. We consider semiparametric models with p regressor terms and q smooth terms. We obtain an explicit expression for the estimate of the regression coefficients given by the back‐fitting algorithm. The calculation of the standard errors of these estimates based on this expression is a considerable computational exercise.
Durban, Maria, Hackett, Christine A., Currie, Iain D.   +2 more
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Semiparametric Models for Cumulative Incidence Functions

Biometrics, 2004
Summary.  In analyses of time‐to‐failure data with competing risks, cumulative incidence functions may be used to estimate the time‐dependent cumulative probability of failure due to specific causes. These functions are commonly estimated using nonparametric methods, but in cases where events due to the cause of primary interest are infrequent relative
Bryant, John, Dignam, James J.
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Semiparametric models: an evaluation

Statistica Neerlandica, 1995
We review ten years of work on semiparametric theory in statistics on the occasion of the recently published book by Bickel, Klaassen, Ritov and Wellner.
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Dynamic and semiparametric models

1997
This paper surveys dynamic or state space models and their relationship to non- and semiparametric models that are based on the roughness penalty approach. We focus on recent advances in dynamic modelling of non-Gaussian, in particular discrete-valued, time series and longitudinal data, make the close correspondence to semiparametric smoothing methods ...
Fahrmeir, Ludwig, Knorr-Held, Leonhard
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A special semiparametric model

1990
In this section we study models of the type described in Section 4 under the following additional assumption: There exists a function S: X × Θ → (Y, B) such that, for every ϑ ∈ Θ, the function S(⋅, ϑ) is sufficient for the family {Pϑ,τ: τ ∈ T}.
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Nonparametric and Semiparametric Models

Technometrics, 2005
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Semiparametric Smooth Coefficient Models

Journal of Business and Economic Statistics, 2002
Qi Li, Tsu-Tan Fu, Cliff J Huang
exaly  

Semiparametric Latent Variable Models With Bayesian P-Splines

Journal of Computational and Graphical Statistics, 2010
Xinyuan Song, Zhaohua Lu
exaly  

Semiparametric estimation of partially linear panel data models

Journal of Econometrics, 1996
Thanasēs Stengos
exaly