📄 arconf.m
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function [Aerr, werr]=arconf(A, C, w, th)
%ARCONF Confidence intervals for AR coefficients.
%
% For an AR(p) model that has been fitted with ARFIT,
% [Aerr,werr]=ARCONF(A,C,w,th) computes the margins of error Aerr and
% werr such that (A +/- Aerr) and (w +/- werr) are approximate 95%
% confidence intervals for the elements of the coefficient matrix A
% and for the components of the intercept vector w. The input
% arguments of ARCONF are output of AR.
%
% If no intercept vector w has been fitted with ARFIT (i.e., the flag
% 'zero' was an input argument of ARFIT), then [Aerr]=ARCONF(A,C,th)
% computes the margins of error only for the elements of the
% coefficient matrix A.
%
% The confidence intervals are based on Student's t distribution,
% which for small samples yields only approximate confidence
% intervals. Inferences drawn from small samples must therefore be
% interpreted cautiously.
%
% See also ARFIT.
% Modified 30-Dec-99
% Author: Tapio Schneider
% tapio@cims.nyu.edu
ccoeff = .95; % confidence coefficient
m = size(C,1); % dimension of state space
p = size(A,2)/m; % order of model
if (nargin == 3)
% no intercept vector has been fitted
Aaug = A;
th = w;
w = [];
np = m*p; % number of parameter vectors of size m
else
Aaug = [w A];
np = m*p+1; % number of parameter vectors of size m
end
% number of degrees of freedom for residual covariance matrix
dof = th(1,1);
% quantile of t distribution for given confidence coefficient and dof
t = tquant(dof, .5+ccoeff/2);
% Get matrix Uinv that appears in the covariance matrix of the least squares
% estimator
Uinv = th(2:size(th,1), :);
% Compute approximate confidence intervals for elements of Aaug
Aaug_err = zeros(m, np);
for j=1:m
for k=1:np
Aaug_err(j,k) = t * sqrt( Uinv(k ,k)* C(j,j) );
end
end
if (nargin == 3)
% No intercept vector has been fitted
Aerr = Aaug_err;
else
% An intercept vector has been fitted => return margins of error
% for intercept vector and for AR coefficients separately
werr = Aaug_err(:, 1);
Aerr = Aaug_err(:, 2:np);
end
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