nuisance parameter

nuisance parameter

[′nü·səns pə‚ram·əd·ər]
(statistics)
A parameter to be estimated by a statistic which arises in the distribution of the statistic under some hypothesis to be tested about the parameter.
References in periodicals archive ?
Note that because the precise CI (13) depends on the nuisance parameter [mu], this paper shows how to address the nuisance parameter [mu] based on a generalized pivotal quantity, and an exact CI for [sigma] is proposed based on the generalized pivotal quantity.
In contrast, under the cause specific hazard rate framework [17] introduced a semiparametric Bayesian method assuming that each cumulative baseline cause-specific hazard rate function has a gamma prior distribution, and a marginal likelihood function based on data and the prior parameter values was proposed for the estimation of regression parameters by considering cumulative baseline cause-specific hazard rate functions as a nuisance parameter.
This type of model arises when knowledge of all measurement variables is subject to significant error, and the model assumes a true value for each variable, which is to be estimated at least as a nuisance parameter.
Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative.
Generally, we would like to make inferences on parameters of interest without reference to specific nuisance parameter values.
This nuisance parameter represents some trait that might influence a systematist's choice of taxa and is expected to be similar in close relatives.
These latter parameters are usually referred to as nuisance parameters.
To simplify the computation, we use the concentrated CRB theory in [29], which can discard the nuisance parameters and only considers the interested ones, to calculate the CRB on the direction parameters.
We are interested primarily in estimating the association between A(t) and survival, [beta], while treating as nuisance parameters the underlying hazard functions,
For our purposes, the distributions of the intervention parameters [Omega] are of greatest interest, while the other parameters ([Theta], [Phi], and the variance of [Epsilon]) are nuisance parameters that complicate interpretation.
A regression of x on z recovers the nuisance parameters [x.
Conditional maximum likelihood methods have the disadvantage that certain restrictive assumptions must be made about the nature of the nuisance parameters or about the effect of other variables in the model.