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Testing instantaneous causality in presence of non constant unconditional variance
VAR model Unconditionally heteroscedastic errors Instantaneous causal-ity.
2012/9/19
The problem of testing instantaneous causality between variables with time-varying unconditional variance is investigated. It is shown that the classi-cal tests based on the assumption of stationary p...
MMANOVA: A general multilevel framework for multivariate analysis of variance
Bayesian inference Constraints Mixed model Variance components
2012/9/19
Classical analysis of variance requires that model terms be labeled as xed or random and typically culminate by comparing variability from each batch (factor) to variability from errors; without a st...
Let X be any absolutely continuous random variable from the integrated Pearson family and assume that X has finite moments of any order. Equivalently, X is a linear (non-constant) transformation of Y ...
On Normal Variance-Mean Mixtures
convex contours distribution theory generalized inverse Gaussian distribution
2011/7/5
Normal variance-mean mixtures encompass a large family of useful distributions such as the generalized hyperbolic distribution, which itself includes the Student t, Laplace, hyperbolic, normal inverse...
Testing for homogeneity of variance in the wavelet domain
Testing homogeneity variance wavelet domain
2011/7/5
The danger of confusing long-range dependence with non-stationarity has been pointed out by many authors.
Simultaneous estimation of the mean and the variance in heteroscedastic Gaussian regression
Gaussian regression heteroscedasticity model selection Kullback risk convergence rate
2009/9/16
Let $Y$ be a Gaussian vector of $R^{n}$ of mean $s$ and diagonal covariance matrix $Gamma$. Our aim is to estimate both $s$ and the entries $sigma_{i}=Gamma_{i,i}$, for $i=1,dots,n$, on the basis of t...