We consider the profile score function in models with smooth and parametric components. If local respectively weighted likelihood estimation is used for fitting the smooth component, the resulting profile likelihood estimate for the parametric component is asymptotically efficient as shown in Severini&Wong (Annals of Statist., 1992). However, as in solely parametric models the profile score function is not unbiased. We propose a small sample bias adjustment which results by extending the correction suggested in McCullagh&Tibshirani (JRSS B, 1990) to the framework of semiparametric models
We discuss higher-order adjustments for a quasi-profile likelihood for a scalar parameter of interes...
We calculate the bias of the profile score for the autoregressive parameters p and covariate slopes ...
An asymptotically efficient likelihood-based semiparametric estimator is derived for the censored re...
We consider the profile score function in models with smooth and parametric components. If local res...
AbstractWe consider the profile score function in models with smooth and parametric components. If l...
We propose a likelihood function endowed with a penalisation that reduces the bias of the maximum li...
In this paper, we propose a general class of penalized profiled semiparametric estimating functions ...
Maximum-likelihood estimates of nonlinear panel data models with fixed effects are generally not con...
We provide easy to verify sufficient conditions for the consistency and asymptotic normality of a cl...
Estimation of the derivative of the log density, or score, function is central to much of recent wor...
Recently, Hjort and Claeskens (2003) developed an asymptotic theory for model selection, model avera...
We propose a scheme of iterative adjustments to the profile score to deal with incidental-parameter ...
Profile likelihood is a popular method of estimation in the presence of a nuisance parameter. It is ...
We consider consistent estimation of partially linear panel data models with fixed effects. We propo...
Varying-coefficient partially linear models are frequently used in statistical modeling. Yet, their...
We discuss higher-order adjustments for a quasi-profile likelihood for a scalar parameter of interes...
We calculate the bias of the profile score for the autoregressive parameters p and covariate slopes ...
An asymptotically efficient likelihood-based semiparametric estimator is derived for the censored re...
We consider the profile score function in models with smooth and parametric components. If local res...
AbstractWe consider the profile score function in models with smooth and parametric components. If l...
We propose a likelihood function endowed with a penalisation that reduces the bias of the maximum li...
In this paper, we propose a general class of penalized profiled semiparametric estimating functions ...
Maximum-likelihood estimates of nonlinear panel data models with fixed effects are generally not con...
We provide easy to verify sufficient conditions for the consistency and asymptotic normality of a cl...
Estimation of the derivative of the log density, or score, function is central to much of recent wor...
Recently, Hjort and Claeskens (2003) developed an asymptotic theory for model selection, model avera...
We propose a scheme of iterative adjustments to the profile score to deal with incidental-parameter ...
Profile likelihood is a popular method of estimation in the presence of a nuisance parameter. It is ...
We consider consistent estimation of partially linear panel data models with fixed effects. We propo...
Varying-coefficient partially linear models are frequently used in statistical modeling. Yet, their...
We discuss higher-order adjustments for a quasi-profile likelihood for a scalar parameter of interes...
We calculate the bias of the profile score for the autoregressive parameters p and covariate slopes ...
An asymptotically efficient likelihood-based semiparametric estimator is derived for the censored re...