Confidence intervals are constructed by fixing one parameter and inferring all others over a range of values. Each row corresponds to a different experimental design. Note that the minimally informative experimental designs (f1 and f2) have non-identifiable parameters, indicated by infinite or one-sided confidence bounds. In contrast, inclusion of LV data (f3 and f4) remedy the issue of non-identifiable parameters in the set. Large deviations in the profile likelihood correspond to local minima and parameter sets that are incompatible for the system of DAE’s.</p
Profiles of the likelihood can be used for the construction of confidence intervals for param-eters,...
AbstractThe behaviors of various confidence/credible interval constructions are explored, particular...
The variability and accuracy of two recently published semiparametric model-robust regression techni...
Normal-based confidence intervals for a parameter of interest are inaccurate when the sampling distr...
<p>The solid lines show the profile likelihood versus parameter value for model M0, given the estima...
After reviewing pertinent literature on the estimation of sampling variances and confidence interval...
In many ecological research studies, abundance data, which usually contain a large number of zeros, ...
Profile likelihood is an interesting theory to visualize and compute confidence interval for estimat...
International audienceWhen a measurement of a physical quantity is reported, the total uncertainty i...
Three methods for computing confidence intervals (CIs) of hidden Markov model parameters are compare...
<p>Model parameters or initial conditions of variables are given on the x-axis of each subfigure. Th...
In this paper we propose methods to construct confidence intervals for the bias of the two-stage lea...
This thesis deals with a statistical method called pro le likelihood. We use it in estimating the un...
In a capture–recapture analysis, uncertainty in the parameter estimates is usually ex-pressed by pre...
Practical identifiability of Systems Biology models has received a lot of attention in recent scient...
Profiles of the likelihood can be used for the construction of confidence intervals for param-eters,...
AbstractThe behaviors of various confidence/credible interval constructions are explored, particular...
The variability and accuracy of two recently published semiparametric model-robust regression techni...
Normal-based confidence intervals for a parameter of interest are inaccurate when the sampling distr...
<p>The solid lines show the profile likelihood versus parameter value for model M0, given the estima...
After reviewing pertinent literature on the estimation of sampling variances and confidence interval...
In many ecological research studies, abundance data, which usually contain a large number of zeros, ...
Profile likelihood is an interesting theory to visualize and compute confidence interval for estimat...
International audienceWhen a measurement of a physical quantity is reported, the total uncertainty i...
Three methods for computing confidence intervals (CIs) of hidden Markov model parameters are compare...
<p>Model parameters or initial conditions of variables are given on the x-axis of each subfigure. Th...
In this paper we propose methods to construct confidence intervals for the bias of the two-stage lea...
This thesis deals with a statistical method called pro le likelihood. We use it in estimating the un...
In a capture–recapture analysis, uncertainty in the parameter estimates is usually ex-pressed by pre...
Practical identifiability of Systems Biology models has received a lot of attention in recent scient...
Profiles of the likelihood can be used for the construction of confidence intervals for param-eters,...
AbstractThe behaviors of various confidence/credible interval constructions are explored, particular...
The variability and accuracy of two recently published semiparametric model-robust regression techni...