H = PLOT_GAUSSIAN_ELLIPSOIDS(M, C) plots the distribution specified by mean M and covariance C. The distribution is plotted as an ellipse (in 2-d) or an ellipsoid (in 3-d). By default, the distributions are plo
0012]′ and their scaled covariance matrix is ⎡⎢⎢⎢⎢⎣100000.00100001e−800000.1⎤⎥⎥⎥⎥⎦. Also, the prior scale of the disturbance variance is 0.01. Specify the prior information using dot notation. Get PriorMdl.Mu = [-20; 4; 0.001; 2]; PriorMdl.V =...
Covariance matrix of the ARMA(p,q) model innovations εt, specified as a numeric scalar or a numVars-by-numVars numeric matrix. InnovCov must be a positive scalar or a positive definite matrix. The default value is eye(numVars). Example: InnovCov=0.2 Data Types: double NumObs— Forecast...
Object tracks, specified as anobjectTrackobject, an array ofobjectTrackobjects, or a cell array ofobjectTrackobjects. In any of these three formats, you can replace theobjectTrackobject by a track structure containing these fields:SourceIndex,TrackID,State, andStateCovariance. The specifications of...
Use hac to estimate the standard Newey-West coefficient covariance. Get maxLag = floor(4*(T/100)^(2/9)); [NWEstParamCov,~,NWCoeff] = hac(Mdl,Type="HAC", ... Bandwidth=maxLag+1); Estimator type: HAC Estimation method: BT Bandwidth: 4.0000 Whitening order: 0 Effective sample ...
0012]′ and their scaled covariance matrix is ⎡⎢⎢⎢⎢⎣100000.00100001e−800000.1⎤⎥⎥⎥⎥⎦. Also, the prior scale of the disturbance variance is 0.01. Specify the prior information using dot notation. Get PriorMdl.Mu = [-20; 4; 0.001; 2]; PriorMdl.V =...
0012]′ and their scaled covariance matrix is ⎡⎢⎢⎢⎢⎣100000.00100001e−800000.1⎤⎥⎥⎥⎥⎦. Also, the prior scale of the disturbance variance is 0.01. Specify the prior information using dot notation. Get PriorMdl.Mu = [-20; 4; 0.001; 2]; PriorMdl.V =...
If the sequence {ψj} is absolutely summable, yt is a covariance-stationary stochastic process [2]. For a stationary stochastic process, the impact on the process due to a change in εt is not permanent, and the effect of the impulse decays to zero. Otherwise, the process yt is nonstat...
The hat matrix is also known as theprojection matrixbecause it projects the vector of observations y onto the vector of predictionsˆy, thus putting the "hat" on y. Because the sum of the leverage values isp(the number of coefficients in the regression model), an observationican be conside...
If sys does not contain parameter covariance information, then ysd is empty. pOut— Parameter trajectories array Parameter trajectories, returned as an array. When sys is a linear-parameter varying model, pOut contains the evolution of the parameters of sys at each time in t or tOut. The ...