Add to ORKGThe aim of this contribution is to investigate the stability of approximate conditional procedures used to eliminate nuisance parameters in regression-scale models
Classical statistical inference relies mostly on parametric models and on optimal procedures which a...
Likelihood-based methods of statistical inference provide a useful general methodology that is appea...
Abstract: In this thesis, we give a general construction of a conditional model through embedding th...
The aim of this contribution is to investigate the stability of approximate conditional procedures u...
The aim of this contribution is to derive a robust approximate conditional procedure used to elimina...
Conditional inference is an intrinsic part of statistical theory, though not routinely of statistica...
We consider robust methods of likelihood and frequentist inference for the nonlinear parameter, say ...
The robustness properties of conditional normal-theory procedures of inference for a location parame...
This paper presents a set of REDUCE procedures that make a number of existing higher-order asymptoti...
This paper establishes sufficient conditions to bound the error in perturbed conditional mean estima...
Let y, ,..., Yn denote independent real-valued observations, each distributed accord-ing to a densit...
This article studies the local robustness of estimators and tests for the conditional location and s...
To quantify uncertainty around point estimates of conditional objects such as conditional means or v...
Conditional inference procedures are discussed for the shape parameter and for the current system re...
Deposited with permission of the author. © 1984 Dr. John Musisi Senyonyi-MubiruConditional inference...
Classical statistical inference relies mostly on parametric models and on optimal procedures which a...
Likelihood-based methods of statistical inference provide a useful general methodology that is appea...
Abstract: In this thesis, we give a general construction of a conditional model through embedding th...
The aim of this contribution is to investigate the stability of approximate conditional procedures u...
The aim of this contribution is to derive a robust approximate conditional procedure used to elimina...
Conditional inference is an intrinsic part of statistical theory, though not routinely of statistica...
We consider robust methods of likelihood and frequentist inference for the nonlinear parameter, say ...
The robustness properties of conditional normal-theory procedures of inference for a location parame...
This paper presents a set of REDUCE procedures that make a number of existing higher-order asymptoti...
This paper establishes sufficient conditions to bound the error in perturbed conditional mean estima...
Let y, ,..., Yn denote independent real-valued observations, each distributed accord-ing to a densit...
This article studies the local robustness of estimators and tests for the conditional location and s...
To quantify uncertainty around point estimates of conditional objects such as conditional means or v...
Conditional inference procedures are discussed for the shape parameter and for the current system re...
Deposited with permission of the author. © 1984 Dr. John Musisi Senyonyi-MubiruConditional inference...
Classical statistical inference relies mostly on parametric models and on optimal procedures which a...
Likelihood-based methods of statistical inference provide a useful general methodology that is appea...
Abstract: In this thesis, we give a general construction of a conditional model through embedding th...