Results 171 to 180 of about 6,937 (202)

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
openaire   +3 more sources

Semiparametric ARCH Models

Journal of Business & Economic Statistics, 1991
Abstract This article introduces a semiparametric autoregressive conditional hetero scedasticity (ARCH) model that has conditional first and second moments given by autoregressive moving average and ARCH parametric formulations but a conditional density that is assumed only to be sufficiently smooth to be approximated by a ...
Robert F Engle, Gloria Gonzalez-Rivera
openaire   +1 more source

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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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
openaire   +1 more source

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
openaire   +1 more source

An em algorithm for a semiparametric finite mixture model

Journal of Statistical Computation and Simulation, 2002
Biao Zhang
exaly  

Semiparametric Smooth Coefficient Models

Journal of Business and Economic Statistics, 2002
Qi Li, , Tsu‐Tan Fu
exaly  

Semiparametric Estimation in the Rasch Model and Related Exponential Response Models, Including a Simple Latent Class Model for Item Analysis

Journal of the American Statistical Association, 1991
Bruce Lindsay, John Grego, Clifford C Clogg   +2 more
exaly  

Maximum Likelihood Estimation for Semiparametric Density Ratio Model

International Journal of Biostatistics, 2012
Guoqing Diao, Jing Ning, Jing Qin
exaly