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Variance Function Estimation [PDF]
Journal of the American Statistical Association, 1987 We develops a general theory for variance function estimation in regression. Most methods in common use are included in our development. The general qualitative conclusions are these. First, most variance function estimation procedures can be looked upon as regressions with responses' being transformations of absolute residuals from a preliminary fit ...
M. Davidian, R.J. Carroll
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Local Polynomial Variance Function Estimation [PDF]
Technometrics, 1998 The conditional variance function in a heteroscedastic, nonparametric regression model is estimated by linear smoothing of squared residuals. Attention is focused on local polynomial smoothers. Both the mean and variance functions are assumed to be smooth, but neither is assumed to be in a parametric family. The biasing effect of preliminary estimation
David Ruppert +3 more
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Warped functional analysis of variance [PDF]
Biometrics, 2014 SummaryThis article presents an Analysis of Variance model for functional data that explicitly incorporates phase variability through a time‐warping component, allowing for a unified approach to estimation and inference in presence of amplitude and time variability.
Patrick A. Carter, Daniel Gervini
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Matchings and the variance of Lipschitz functions [PDF]
ESAIM: Probability and Statistics, 2009 We are interested in the rate function of the moderate deviation principle for the two-sample matching problem. This is related to the determination of 1-Lipschitz functions with maximal variance. We give an exact solution for random variables which have normal law, or are uniformly distributed on the Euclidean ball.
Barthe, Franck, O'Connell, Neil
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Kriging with Nonparametric Variance Function Estimation [PDF]
Biometrics, 1999 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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The Annals of Statistics, 1989 Exponential dispersion models play an important role in the context of generalized linear models, where error distributions, other than the normal, are considered. Any statistical model expressible in terms of a variance-mean relation $(V, \Omega)$ leads to an exponential dispersion model provided that $(V, \Omega)$ is a variance function of a natural ...
Bar-Lev, Shaul K., Bshouty, Daoud
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Variance Function in Cluster Sampling [PDF]
International Journal of Computational and Theoretical Statistics, 2015 et al. Shukla A.K
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JTAM (Jurnal Teori dan Aplikasi Matematika), 2022 In this paper, an asymptotic distribution of the estimator for the variance function of a compound periodic Poisson process with power function trend is discussed.
Muhammad Wiranadi Utama +2 more
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The Iraqi Journal of Agricultural science, 2023 This research aimed to measure and analyze the impact of exchange rate shocks on some variables of the Iraqi economy during (1990-2022), because of the different effects of these shocks on the macroeconomic variables represented in money supply and ...
R. T. Al-Wasity, Hala A. Al-Attabi
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Variance Functions with Meromorphic Means
The Annals of Probability, 1991 A natural exponential family $\mathscr{F}$ is characterized by the pair $(V,\Omega)$, called the variance function (VF), where $\Omega$ is the mean domain and $V$ is the variance of $\mathscr{F}$ expressed in terms of the mean. Any VF can be used to construct an exponential dispersion model, thus providing a potential generalized linear model.
Bar-Lev, Shaul K. +2 more
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