Results 301 to 310 of about 91,138 (340)
Some of the next articles are maybe not open access.
On information and ancillarity in the presence of a nuisance parameter
Biometrika, 1983This paper discusses ancillarity, in the presence of a nuisance parameter. For exponential distribution families, some equivalent properties regarding ancillarity are found and discussed.
openaire +1 more source
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 ...
openaire +1 more source
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
openaire +1 more source
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.
openaire +1 more source
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
openaire +1 more source
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
openaire +1 more source
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.
openaire +1 more source
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.
openaire +2 more sources
Is it useful to know a nuisance parameter?
Signal Processing, 1998zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +1 more source