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Dependent Dirichlet Priors and Optimal Linear Estimators for Belief Net Parameters
Dependent Dirichlet Priors Optimal Linear Estimators Belief Net Parameters
2012/9/19
A Bayesian belief network is a model of a joint distribution over a finite set of vari-ables, with a DAG structure representing im-mediate dependencies among the variables.For each node, a table of pa...
Consistent selection of tuning parameters via variable selection stability
kappa coefficient penalized regression selection consistency stability tuning
2012/9/17
Penalized regression models are popularly used in high-dimensional data analysis to conduct variable selection and model fitting simultaneously. Whereas success has been widely reported in literature,...
Demmartingales and the functionnal Hill process for small parameters
Associated random variables demimartingales asymptotic laws func-tional Hill process extreme value theory statistical tests.
2012/9/17
Association of random variables and Demimartingales are recent elds for handling asymptotic behaviors of sums of dependent random vari-ables. We apply their techniques to establish the asymptotic law...
Estimation of Scale and Hurst Parameters of Semi-Selfsimilar Processes
Hurst estimation Discrete self-similarity Fractional Brownian motion Semi-selfsimilar processes Scale parameter.
2012/9/19
The characteristic feature of semi-selfsimilar process is the invariance of its finite dimensional distributions by certain dilation for specific scaling factor. Estimating the scale parameter λand th...