Results 11 to 20 of about 1,198,252 (292)
Kriging with Nonparametric Variance Function Estimation [PDF]
Summary. A method for fitting regression models to data that exhibit spatial correlation and heteroskedas‐ticity is proposed. It is well known that ignoring a nonconstant variance does not bias least‐squares estimates of regression parameters; thus, data analysts are easily lead to the false belief that moderate heteroskedas‐ticity can generally be ...
Opsomer, Jean +4 more
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MEAN–VARIANCE PORTFOLIO MANAGEMENT WITH FUNCTIONAL OPTIMIZATION [PDF]
This paper introduces a new functional optimization approach to portfolio optimization problems by treating the unknown weight vector as a function of past values instead of treating them as fixed unknown coefficients in the majority of studies. We first show that the optimal solution, in general, is not a constant function.
KA WAI TSANG, ZHAOYI HE
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This research is a follow-up research of Utama (2022) on asymptotic distribution of an estimator for variance function of a compound periodic Poisson with the power function trend.
Ade Irawan +2 more
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Bar-Lev, Shaul K., Bshouty, Daoud
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New Closed Form Estimators for the Beta Distribution
In this paper, we detail closed form estimators for beta distribution that are simpler than those proposed by Tamae, Irie and Kubokawa. The proposed estimators are shown to have smaller asymptotic variances and smaller asymptotic covariances compared to ...
Victor Mooto Nawa, Saralees Nadarajah
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A link function approach to heterogeneous variance components
d'Arnoldi Caroline +2 more
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Variance function of boolean additive convolution [PDF]
We determine the formula for pseudo-variance function (or variance function V{\nu} in case of existence) under boolean additive convolution ...
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Optimal designs for variance function estimation using sample variances [PDF]
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Goos, P., Tack, L., Vandebroek, M.
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Discrimination between Some Over Dispersed Count Distributions
The Poisson inverse Gaussian and generalized Poisson distributions are widely used in modelling overdispersed count data which are commonly found in healthcare, insurance, engineering, econometric and ecology.
Yook-Ngor Phang +2 more
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Jackknife Empirical Likelihood Inference for the Variance Residual Life Function
In life testing situations, the residual life time of a component which has survived t units of time is Xt = X −t|X > t. In this paper, we give a central limit theorem result for the estimator of Var(Xt), the variance residual life(VRL) function.
Vali Zardasht
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