If A is a vector of observations, C is the scalar-valued variance. 如果A是一个观测向量,那么C是一个标量值的方差。 If A is a matrix whose columns represent random variables and whose rows represent observations, C is
For a matrix A whose columns are each a random variable made up of observations, the covariance matrix is the pairwise covariance calculation between each column combination. In other words, C(i,j)=cov(A(:,i),A(:,j)). Variance For a finite-length vector A made up of N scalar observ...
Covariance, returned as a scalar or matrix. For single matrix input,Chas size[size(A,2) size(A,2)]based on the number of random variables (columns) represented byA. The variances of the columns are along the diagonal. IfAis a row or column vector,Cis the scalar-valued variance. ...
C= cov(A)returns thecovariance. C= cov(A)返回协方差。 IfAis a vector of observations,Cis the scalar-valuedvariance. 如果A是一个观测向量,那么C是一个标量值的方差。 IfAis a matrix whose columns represent random variables and whose rows represent observations,Cis the covariance matrix with the ...
Here, Σ is the standard covariance estimate, τ is the average sample variance, and α∈[0,1] is the intensity parameter computed using α=1NN∑i=1trace([zizTi−Σ]2)trace([Σ-τ]2) where zi is the i th row of the centered sample matrix Z and N is the sample size. ...
robustfittreatsNaNvalues inXoryas missing values.robustfitomits observations with missing values from the robust fit. Algorithms robustfituses iteratively reweighted least squares to compute the coefficientsb. The inputwfunspecifies the weights. robustfitestimates the variance-covariance matrix of the coefficien...
Perform Covariance Denoising Copy Code Copy Command This example shows how to use covariance denoising to reduce noise and enhance the signal of the empirical covariance matrix. In mean-variance portfolio optimization, a noisy estimate of the covariance estimate results in unstable solutions that cause...
The numerical results indicate that the benefit of utilizing spatial correlation in the covariance matrix estimation can be significant especially when the total number of snapshots in the secondary data is small. From applications viewpoint, the suggested model is well suited for the adaptive target ...
If the data inyis random, then an estimate of the covariance matrix ofpis(Rinv*Rinv')*normr^2/df, whereRinvis the inverse ofR. Centering and scaling values, specified as a two-element vector. This vector is an optional output from[p,S,mu] = polyfit(x,y,n)that is used to improve ...
If the data in y is random, then an estimate of the covariance matrix of p is (Rinv*Rinv')*normr^2/df, where Rinv is the inverse of R. If the errors in the data in y are independent and normal with constant variance, then [y,delta] = polyval(...) produces error bounds that ...