A framework for robust optimization under uncertainty based on the use of the generalized inverse distribution function (GIDF), also called quantile function, is here proposed. Compared to more classical approaches that rely on the usage of statistical moments as deterministic attributes that define ...
A framework for robust optimization under uncertainty based on the use of the generalized inverse distribution function (GIDF), also called quantile function, is here proposed. Compared to more classical approaches that rely on the usage of statistical moments as deterministic attributes that define the...
Using IGW distribution, a four-parameter distribution named as Generalized Inverse Generalized Weibull (GIGW) distribution is introduced and its properties are studied. The mixture of two GIGW distributions has been investigated. Empirical estimates of parameters have been found using maximum likelihood ...
Generalized inverse Weibull DistributionAcceptance sampling plans for a Generalized Inverse Weibull Distribution (GIWD) when the lifetime is truncated at a pre-determined time are suggested in this paper. The minimum sample sizes necessary to ensure the specified mean life time for a given consumer'...
Inverse Gaussian DistributionNormal Variance-Mean MixtureNumerical QuadratureIn this study, a numerical quadrature for the generalized inverse Gaussian distribution is derived from the Gauss--Hermite quadrature by exploiting its relationship with the normal distribution. Unlike Gaussian quadrature, the proposed...
The skew-generalized inverse weibull distribution (SGIW) has four parameters of lifetime distribution. It could have different hazard rates: increasing, decreasing and unimodal. In this paper, the method of Azzalini's (1985) is used to provide a shape of parameter to generalize inverse weibull,...
DistributionCanonical Link Function NameLink FunctionMean (Inverse) Function 'normal''identity'f(μ) =μμ=Xb 'binomial''logit'f(μ) = log(μ/(1 –μ))μ= exp(Xb) / (1 + exp(Xb)) 'poisson''log'f(μ) = log(μ)μ= exp(Xb) ...
DistributionCanonical Link Function NameLink FunctionMean (Inverse) Function 'normal' 'identity' f(μ) = μ μ = Xb 'binomial' 'logit' f(μ) = log(μ/(1 – μ)) μ = exp(Xb) / (1 + exp(Xb)) 'poisson' 'log' f(μ) = log(μ) μ = exp(Xb) 'gamma' -1 f(μ) = 1/μ...
The asymptotic confidence intervals can be obtained using the diagonal entries of the inverse of the observed Fisher information matrix. Alternatively, a bootstrap confidence intervals may be constructed in a routine manner. El-Din et al. [112] also considered the inferential issues under the ...
Link Function NameLink FunctionMean (Inverse) Function 'identity'(default for'normal'distribution)f(μ) =μμ=Xb 'log'(default for'poisson'distribution)f(μ) = log(μ)μ= exp(Xb) 'logit'(default for'binomial'distribution)f(μ) = log(μ/(1 –μ))μ= exp(Xb) / (1 + exp(Xb)) ...