We consider asymptotic theory for the maximum likelihood estimator in the generalized linear model with an unknown breakpoint. A proof for the asymptotic normality is given. The methods are based on the work of Huber (1967). The main problem is the non--differentiability of the likelihood and the score function, which requires non--standard methods. An example from epidemiology is presented, where confidence intervals for the parameters are calculated with the asymptotic results
This Note generalizes two estimators of the quadratic regression with measurement errors by Fuller a...
This article discusses some properties of the first order regression method for imputation of missin...
We study the law of functionals whose prototype is integral(0)(+infinity) e(s)(B(V)) dW(s)((mu),) wh...
We consider asymptotic theory for the maximum likelihood estimator in the generalized linear model w...
Reliability measures in linear models are used in geodetic science and elsewhere to quantify the pot...
We study marked point processes (MPP's) with an arbitrary mark space. First we develop some statisti...
We consider two tests for testing the hypothesis that a density lies in a parametric class of densit...
The question of recovering a multiband signal from noisy observationsmotivates a model in which the ...
We are dealing with regression models for point processes having a multiplicative intensity process ...
The problem of estimation of the finite dimensional parameter in a partial linear model is considere...
Kernel smoothing in nonparametric autoregressive schemes offers a powerful tool in modelling time se...
This paper proposes a nonparametric test of the non-convexity of a smooth regression function based ...
We investigate the relative merits of a “moment-oriented” bootstrap method of Bunke (1997) in compar...
Kernel smoothing in nonparametric autoregressive schemes offers a powerful tool in modelling time se...
This paper describes a software tool for marginal regression methods. MAREG currently handles binary...
This Note generalizes two estimators of the quadratic regression with measurement errors by Fuller a...
This article discusses some properties of the first order regression method for imputation of missin...
We study the law of functionals whose prototype is integral(0)(+infinity) e(s)(B(V)) dW(s)((mu),) wh...
We consider asymptotic theory for the maximum likelihood estimator in the generalized linear model w...
Reliability measures in linear models are used in geodetic science and elsewhere to quantify the pot...
We study marked point processes (MPP's) with an arbitrary mark space. First we develop some statisti...
We consider two tests for testing the hypothesis that a density lies in a parametric class of densit...
The question of recovering a multiband signal from noisy observationsmotivates a model in which the ...
We are dealing with regression models for point processes having a multiplicative intensity process ...
The problem of estimation of the finite dimensional parameter in a partial linear model is considere...
Kernel smoothing in nonparametric autoregressive schemes offers a powerful tool in modelling time se...
This paper proposes a nonparametric test of the non-convexity of a smooth regression function based ...
We investigate the relative merits of a “moment-oriented” bootstrap method of Bunke (1997) in compar...
Kernel smoothing in nonparametric autoregressive schemes offers a powerful tool in modelling time se...
This paper describes a software tool for marginal regression methods. MAREG currently handles binary...
This Note generalizes two estimators of the quadratic regression with measurement errors by Fuller a...
This article discusses some properties of the first order regression method for imputation of missin...
We study the law of functionals whose prototype is integral(0)(+infinity) e(s)(B(V)) dW(s)((mu),) wh...