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Journal of Cosmology and Astroparticle Physics
Many physical models contain nuisance parameters that quantify unknown properties of an experiment that are not of primary relevance. Typically, these cannot be measured except by fitting the models to the data from the experiment, requiring simultaneous
S. Paradiso +6 more
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Many physical models contain nuisance parameters that quantify unknown properties of an experiment that are not of primary relevance. Typically, these cannot be measured except by fitting the models to the data from the experiment, requiring simultaneous
S. Paradiso +6 more
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Elimination of Nuisance Parameters
1988The problem begins with an unknown state of nature represented by the parameter of interest θ . We have some information about θ to begin with — e.g., we know that θ is a member of some well-defined parameter space θ- but we are seeking more. Toward this end, a statistical experiment & is planned and performed and this generates the sample observation ...
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Dealing with nuisance parameters
2001Abstract Nuisance parameters create most of the complications in likelihood theory. They appear on the scene as a natural consequence of our effort to use ‘bigger and better ‘ models: while some parameters are of interest, others are only required to complete the model. The issue is important since nuisance parameters can have a dramatic
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Orthogonality of Estimating Functions and Nuisance Parameters
Biometrika, 1991SUMMARY Cox & Reid (1987) proposed the technique of orthogonalizing parameters, to deal with the general problem of nuisance parameters, within fully parametric models. They obtained a large-sample approximation to the conditional likelihood. Along the same lines Davison (1988) studied generalized linear models.
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1982
Consider a parametric family β = {Pθ,n:(θ,n) ∈ Θ × H} with Θ ⊂IRp and H arbitrary. We are interested in estimating the (structural) parameter θ The value of the nuisance parameter n changes from observation to observation, being a random variable, distributed according to some p-measure Γ on (H ,ℬ), i.e., the observation xν is a realization governed by
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Consider a parametric family β = {Pθ,n:(θ,n) ∈ Θ × H} with Θ ⊂IRp and H arbitrary. We are interested in estimating the (structural) parameter θ The value of the nuisance parameter n changes from observation to observation, being a random variable, distributed according to some p-measure Γ on (H ,ℬ), i.e., the observation xν is a realization governed by
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Estimating a Signal with Noisy Nuisance Parameters
Biometrika, 1989We describe two models in which n records of a signal in white noise are taken. In the first model the signal parameters of interest do not change between records, but the amplitude varies in a random way. In the second model, the location is the random nuisance parameter. We describe an efficient estimator for the first model.
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On ancillarityu and Fisher information in the presence of a nuisance parameter
Biometrika, 1984The author, ibid. 63, 277-284 (1976; Zbl 0339.62013), put forward two concepts of ancillarity in the presence of nuisance parameters. In this paper they are unified and extended with an extended concept of Fisher information.
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Is it useful to know a nuisance parameter?
Signal Processing, 1998zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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IEEE Transactions on Aerospace and Electronic Systems, 2017
A. Noroozi, M. Sebt
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A. Noroozi, M. Sebt
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