elements of information theory. wiley, new york (2012) math google scholar edgeworth, f.: on observations relating to several quantities. hermathena 6 (13), 279–285 (1887) google scholar efron, b.: regression percentiles using asymmetric squared error loss. stat. sin. 1 (1...
Consider the linear regression model Y = X+ where Y denotes a vector of n observations on the dependent variable, X is a known matrix, is a vector of parameters to be estimated and e is a random vector of uncorrelated errors. If X''X is nearly singular, that is if the smallest ...
where the “latent” explanatory variableξ is not directly observed (hence the Greek letter); x2 is directly observed and measured without error; and the regression error ɛ behaves according to the usual assumptions—in particular, E(ɛi) = 0, and the errors are uncorrelated with each ...
(2000). Contingency Theory and Moderated regression Analysis: The Effect of Measurement Level, Measurement Error and Non-Linear Relation. Serie Research Memoranda, Vol: 25 (2), pp.1-21... M Gelderman - 《Research Memoranda》 被引量: 1发表: 2000年 In Pursuit of Moderation: Nine Common Erro...
We average the performance of nine different distance-3 subgrids and four different distance-5 subgrids to compare with the distance-7 code. Finally, we compute Λ using linear regression of ln[εd] versus d. With our neural network decoder, we observe Λ = 2.14 ± 0.02 and ...
The method involves the familiar log-odds transformation and application of the multivariate delta method. Estimation of the model coefficients after the transformation is a straightforward generalized least squares regression, and the corresponding improved estimate of the variance-covariance matrix is in ...
What is the three-step analysis in determining if a contract meets the requirements of the Statute of Frauds? What is the differences between the Status Frustration theory and Differential opportunity theory? Discuss basic assumptions and requirements of an LP m...
Albertini and de Mello [226] propose a network that integrates features from SOM, GWR, and adaptive resonance theory networks. When an input is presented, the network searches through categories stored for a match. If no match is found, then the input is considered to be novel. A ligand-ba...
For PWS regression, our derivation involves a chaining argument and a decomposition of the covering numbers of PWS classes in terms of the ones of their component functions and the capacity of the classifier partitioning the input space. This yields error bounds with a radical dependency on the ...
and the regression of the anomalous diffusion exponent of single-particle-trajectories. Evaluating their performance, we find that these models can achieve a well-calibrated error estimate while maintaining high prediction accuracies. In the analysis of the output uncertainty predictions we relate these...