Results 11 to 20 of about 1,213,973 (118)
Heteroscedastic nested error regression models with variance functions [PDF]
Statistica Sinica, 2018 The nested error regression model is a useful tool for analyzing clustered (grouped) data, and is especially used in small area estimation. The classical nested error regression model assumes normality of random effects and error terms, and homoscedastic variances.
Shonosuke Sugasawa, Tatsuya Kubokawa
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Smoothing Spline Estimation of Variance Functions [PDF]
Journal of Computational and Graphical Statistics, 2007 This article considers spline smoothing of variance functions. We focus on selection of the smoothing parameters and develop three direct data-driven methods: unbiased risk (UBR), generalized approximate cross-validation (GACV), and generalized maximum likelihood (GML).
Anna Liu, Tiejun Tong, Yuedong Wang
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Variance and Bias for General Loss Functions [PDF]
Machine Learning, 2003 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Functional Analysis of Variance for Association Studies
PLoS ONE, 2014 While progress has been made in identifying common genetic variants associated with human diseases, for most of common complex diseases, the identified genetic variants only account for a small proportion of heritability. Challenges remain in finding additional unknown genetic variants predisposing to complex diseases.
Olga A Vsevolozhskaya +4 more
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Bt-Transformation and Variance Function
MathematicsThis study investigates the Bt-transformation of probability measures within the framework of free probability. A primary focus is the invariance under this transformation of two fundamental families: the free Meixner family and the free analog of the Letac–Mora class.
Abdulmajeed Albarrak +2 more
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Use of (Co)Variance Functions to Describe (Co)Variances for Test Day Yield
Journal of Dairy Science, 1999 Abstract (Co)variance functions for milk, fat, and protein yields during first lactation were developed from (co)variance components for four lactation stages of 75 d each that had been previously estimated using test day data from 17,190 Holstein cows from 37 herds in Pennsylvania and Wisconsin.
Tijani, A. +4 more
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Natural Exponential Families with Quadratic Variance Functions
The Annals of Statistics, 1982 The normal, Poisson, gamma, binomial, and negative binomial distributions are univariate natural exponential families with quadratic variance functions (the variance is at most a quadratic function of the mean). Only one other such family exists. Much theory is unified for these six natural exponential families by appeal to their quadratic variance ...
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Estimating Variance Function with Kernel Machine
Communications for Statistical Applications and Methods, 2009 In this paper we propose a variance function estimation method based on kernel trick for replicated data or data consisted of sample variances. Newton-Raphson method is used to obtain associated parameter vector. Furthermore, the generalized approximate cross validation function is introduced to select the hyper-parameters which affect the performance ...
Jong-Tae Kim +3 more
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r-Free Convolution and Variance Function
AxiomsThe concept of r-free convolution (which is represented by r) was introduced for 0≤r≤1. It is equal to the Boolean additive convolution ⊎ if r=0 and reduced to the free additive convolution ⊞ when r=1. This paper presents certain features of the r-free convolution in relation to the CSK families and their associated variance functions.
Shokrya S. Alshqaq +2 more
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Variance function additive partial linear models
Electronic Journal of Statistics, 2015 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Fang, Yixin +3 more
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