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Dynamic Covariance Models for Multivariate Financial Time Series
Dynamic Covariance Models Multivariate Financial Time Series
2013/6/14
The accurate prediction of time-changing covariances is an important problem in the modeling of multivariate financial data. However, some of the most popular models suffer from a) overfitting problem...
Spectral Methods for Learning Multivariate Latent Tree Structure
Spectral Methods Learning Multivariate Latent Tree Structure
2011/7/19
This work considers the problem of learning the structure of a broad class of multivariate latent variable tree models, which include a variety of continuous and discrete models (including the widely ...
Multivariate stratified sampling by stochastic multiobjective optimisation
Multivariate stratified random sampling multiobjective E-model
2011/7/5
This work considers the allocation problem for multivariate stratified random sampling as a problem of integer non-linear stochastic multiobjective mathematical programming.
Risk,VaR,CVaR and their associated Portfolio Optimizations when Asset Returns have a Multivariate Student T Distribution
VaR CVaR Portfolio Optimization VaR Optimization CVaR Optimization Optimisation
2011/3/25
We show how to reduce the problem of computing VaR and CVaR with Student T return distributions to evaluation of analytical functions of the moments. This allows an analysis of the risk properties of ...
Identification of the Multivariate Fractional Brownian Motion
Self similarity Multivariate process Long-range dependence Discrete variations Parametric estimation
2011/3/23
This paper deals with the identification of the multivariate fractional Brownian motion, a recently developed extension of the fractional Brownian motion to the multivariate case. This process is a $p...
Multivariate Goodness of Fit Procedures for Unbinned Data: An Annotated Bibliography
Multivariate Goodness An Annotated Bibliography
2011/3/21
Unbinned maximum likelihood is a common procedure for parameter estimation. After parameters have been estimated, it is crucial to know whether the fit model adequately describes the experimental data...
Multivariate Student $t$-Statisti
multivariate Student t-statistic multivariate operator self-normalized sums genealized normal domain of attraction
2009/4/28
It is proved that if the multivariate Student $t$-statistic based on i.i.d. symmetric random vectors is asymptotically standard normal, then these random vectors are in the generalized domain of attra...