This article illustrates the use of generalised lambda distributions in conjunction with MLE, PWMs matching and SR method to find an approximate probability density function and confidence interval for the survival curve.
The goodness of fit is evaluated by considering whether the sum of squared errors divided by the estimated variance of the mean is within a 95% confidence interval of the probability density function defined by a χ2 distribution with D degrees of freedom[18].
Given these upper and lower limits, and considering the stringent confidence interval defined for kernel density function, we identified 13 significant clusters of markers (in 8 LGs), corresponding to recombination cold spots.
In this work, a hybrid uncertain numerical method known as the change-of-variable interval stochastic perturbation method (CVISPM) is proposed to predict the interval of the response probability density function and the response confidence interval of a HUSAS.
It is shown that the early arrival time of particles at a control location, reflected in the lower limit of the 95% confidence interval of the mass flux cumulative density function stochastic process, associated with the upscaled model is strikingly similar to the one associated with the real solution for all heterogeneities.
Calibrated dates and corresponding confidence intervals represent the 50th, 2.5th and 97.5th percentiles of the cumulative probability density function of calibrated ages, respectively [33].
The continuous normal lognormal bivariate density function was therefore selected as an adequate model to ascertain rational boundary values ("confidence interval") for MSW shear strength parameters.
The qualitative probability density function for the complete system is presented in Fig. 8, in which the confidence ranges are included.
Based on the response probability density function, the interval perturbation approach is used to predict the interval of the response probability density function and the response confidence interval.
A Gaussian process model allows the specification of confidence bands for the estimated fiber orientation density and the rigorous selection of the hyperparameters, here the high-frequency content in the density function and the noise variance of the MR observations.
Power spectral density function.
