Sentences with phrase «covariance matrices»

PRELIS 2.80 gives the estimations of polychoric covariance and asymptotic covariance matrices, which allow ML to provide the Satorra — Bentler scaled chi - squares in the LISREL 8.80 solutions for estimating the non-normal data.

The requirements for a Multivariate Analysis of Variance (MANOVA), multivariate normal distribution of dependent variables and homogeneity of variance and of the covariance matrices, were verified using the Levene test (Levene, 1960) and were found to be unfulfilled only for the broadband aggression / delinquency scale in the CBCL.

These good results were obtained despite using the suboptimal inverse Wishart prior for covariance matrices, so we may expect even better performance when more uninformative priors are developed.

Because the requirements for a MANOVA (multivariate normal distribution of dependent variables and homogeneity of variance and of the covariance matrices) were not fulfilled for the broadband aggression / delinquency scale in the CBCL, the results were re-evaluated using non-parametric tests (not shown).

It shows the discrepancies between perceived and estimated covariance matrices and scores near to zero represents the little bit variations on the observed and estimated scores.

Multivariate significance was examined using Wald χ2 tests based on design - corrected coefficient variance - covariance matrices.

As I read it, spatial covariance matrices are important to the analysis, where perhaps strictly it is the full lagged covariances that matter.

However, the precise way that the temperature field is separated into components would seem to matter as soon as one starts to form the necessary covariance matrices.

Part of this discussion will deal with the estimation of large covariance matrices, which is a key issue in D&A.

The difference between iid and LTP, for example, is confined to the covariance matrices of the corresponding error distribution (the covariance matrix corresponding to iid data is simply an identify matrix multiplied by a scalar variance; for LTP, the off - diagonal elements are non-zero and non-vanishing).

CSF 2005 concentrated on the problem of selecting the number of eigenvectors to retain when estimating inverse covariance matrices from the AOGCM control run data.

They chose principal component analysis (PCA) to overcome the estimation errors inherent in sample covariance matrices.

After excluding stocks with fewer than three years of immediately past data, the researchers used up to five years of monthly returns to estimate covariance matrices and optimize portfolios.

We simulate the data with multivariate normal distributions, with two different block structured covariance matrices, which only have covariance for the marker genes in the corresponding cell type.

A fundamental difficulty with using connectivity as features is that covariance matrices live on the positive semi-definite cone, which renders their elements inter-related.

Outcomes were modeled as repeated measures with a compound symmetry covariance matrix.

From an unstructured covariance matrix for the intercept and race effects, their correlation coefficient was calculated as

All terms in a covariance matrix need to be added to the calculation.

One thing to note is that these correlation / covariance tables are actually pseudo - correlation / covariance tables because the main diagonals do not contain values you'd expect (1s, meaning perfect correlation, in the correlation matrix and variances in the covariance matrix).

These come into the equation through the estimation of the noise (covariance matrix), i.e. the internal variations and their magnitude.

A procedure by which a spatiotemporal data set is decomposed into its leading patterns in both time (see «Principal Component») and space (see «Empirical Orthogonal Function») based on an orthogonal decomposition of the data covariance matrix.

If the data are not transformed (MM), but the principal components are calculated on the correlation matrix rather than the covariance matrix, the results move part way from MM to MBH, with bristlecone pine data moving up from the PC4 to influence the PC2.

a) the CSF 2005 surface control data covariance matrix is virtually singular, indicating that the raw data from the (GFDL) AOGCM control run had been misprocessed.

b) when used with the HadCM2 - derived surface control data covariance matrix from the SFZ 2008 data, which I have largely been able to agree to raw data from the HadCM2 AOGCM control run (which data Dr Forest has confirmed was used for the Forest 2006 main results), the CSF 2005 surface model and observational data produces, irrespective of which upper air and deep - ocean dataset is used, a strongly peaked PDF for climate sensitivity, centred close to S = 1, not S = 3 as per Forest 2006.

Regressing proxies on temperature as in MBH is the correct way to go, even if they inefficienty ignored the covariance matrix across proxies and therefore could not compute correct confidence intervals.

Does the 2D structure of a covariance matrix enter as well here?

Since in reality the values of physical fields at distant locations are not that much correlated, the covariance matrix is tapered off artificially based on the distance, which gives rise to localized EnKF algorithms.

[16][17] These methods modify the covariance matrix used in the computations and, consequently, the posterior ensemble is no longer made only of linear combinations of the prior ensemble.

Matlab PcaCov -LRB--RRB- will work from supplied covariance matrix.

I'm taking a long shot here, (and I don't have time to really research this right now and I haven't been paying close attention), but is it possible that the algorithm being used has inadvertently created an autoassociative, (or possibly heteroassociative memory), where you can retrieve certain individual inputs of the covariance matrix by multiplying by a another input of the covariance matrix?

In any case, I don't think anything in the covariance matrixes, which are what the data use to «tell» us whether the scale is measuring anything and what it might be, care what the «right» answers are.

If Harry is one of the inputs to the covariance matrix, you can retrieve Harry perfectly from the matrix.

I suspect there is a near singularity caused by some subtle identification problem in the model — the symptom of this is that standard errors grow almost linearly through the variables possibly reflecting the difficulty of inverting an ill - conditioned near singular covariance matrix.

To select a small number of parameters conveying the maximal information about the [photon] shower shape, we uncorrelated the above parameters through a principal component analysis in which these seven parameters are transformed into new seven uncorrelated parameters given by the eigenvectors of the covariance matrix.

They impose minimal restrictions on the covariance matrix and can embed multiple linear comparisons, which is a convenience in applied work.

Thus in preparing a paper, authors are generally careful to make use of alternate methodologies (in Steig's case, using the AWS data or the AVHRR data, using PCA or RegEM, trending vs. detrending the data that goes into the covariance matrix etc.).

The first eigenvector of the covariance matrix for this simulation is the red curve in Figure 9 - 2, showing the precise form of the spurious trend that the principal component would introduce into the fitted model in this case.

With 3 principal components derived from the AVHRR covariance matrix, there is just enough resolution to pick up the TranAntarctic mountains separating E from W Antarctica, but not enough to see the Peninsula, with the result that the numerous Peninsula stations get smeared across W. Ant.

A slightly more elaborate computation, but still a standard technique in regression analysis, would be to derive a covariance matrix of the uncertainties in the reconstructions over a sequence of years.

-LSB-...] In the end, the autocorrelation issue turned out to be the least of the original paper's problems: Ryan O'Donnell, Micholas Lewis, Steve McIntyre and Jeff Condon (J. Climate April 2011, 24:2099 - 2115) have shown that the main results of the paper are dependent on oversmoothing that results from retaining too few principal components of the satellite covariance matrix.

The latter method uses the variance - covariance matrix instead of correlation functions (Kagan, 1997).

Because of the multivariate nonnormality of the data and the ordinal, noncontinuous nature of the item data, the CFA model was specified via a polychoric correlation matrix and asymptotic covariance matrix and an unweighted least squares estimation method.39, 40 The matrices were generated in PRELIS 9.2 and analyzed with LISREL 9.241 and are available on request.

Path analysis was used to draw the diagrams and fit the variances and covariances matrix.

Another criterion of goodness of fit index (GFI) is employed which determine the discrepancies between the assumed model and the observed covariance matrix.

Apart from the aim to be consistent with the approach used in earlier articles in this special issue [3, 4], item response modeling differs from SEM in that (i) it models the actual response data rather than the covariance matrix among the variables, and hence, (ii) it allows a finer grain of interpretation and fit analysis.

The multivariate normal prior density of the level - 2 parameters had a mean vector of zeros and a precision matrix (the inverse of the covariance matrix) with diagonal elements equal to 1.0 E \ -LRB-- \) 6 and off - diagonal elements equal to 0.

Relationships between observations at different occasions will be modelled with an unstructured covariance matrix.

If the outcome did not have a normal distribution, then the parameter estimates of the covariance matrix were computed with robust statistics.

An unstructured covariance matrix was used to allow for the most flexible estimation of covariance parameters between each level of spouse, visit, and time.

To test how well the orthogonal two - factor model (model 1) described the internal structure of the FEEL - KJ, we performed a CFA with robust maximum likelihood estimation on the variance - covariance matrix.