# 假设data是包含样本数据的矩阵 cov_matrix <- cov(data) eigen_values <- eigen(cov_matrix)$values if (any(eigen_values <= 0)) { print("样本协方差矩阵不是正定的") } else { print("样本协方差矩阵是正定的") } 4. 解决样本协方差矩阵非正定问题的建议 检查并处理多重共线性:使...
Also, I get the following warning messages: WARNING: THE LATENT VARIABLE COVARIANCE MATRIX (PSI) IN CLASS 1 IS NOT POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/ RESIDUAL VARIANCE FOR A LATENT VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE BETWEEN TWO LATENT VARIABLES, OR A LINEA...
I recive the following error: "The data X must have a covariance matrix that is positive definite." Could you please tell me where is the problem? Is it due to low mutual dependancy among the used variables? In addition, what I can do about it? Thank you. Regards, Vaclav Ho...
"WARNING: THE LATENT VARIABLE COVARIANCE MATRIX (PSI) IS NOT POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL VARIANCE FOR A LATENT VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE BETWEEN TWO LATENT VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO LATENT VARIABLES. CHECK ...
When a sample set is added to the received signal, a processor augments the square root matr... AS Khayrallah 被引量: 106发表: 2008年 Bayesian updating revisited However, in solving the traditional updating equations, an updated covariance matrix may be generated that is not positive-definite,...
The probability that the estimated between-group covariance matrix is not positive definite is computed exactly under certain conditions given in Hill and Thompson (1978) for the balanced single classification multi-vanate analysis of variance with random effects. The computation is done using the ...
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where det(Exx) is the determinant of Exx, det(Exx)≠ 0 and Exx is positive definite. The problem is to estimate the mean and the covariance matrix of x under assumption that not all of the m components are necessarily observed. In what follows the observations are denoted by a set X ...
However, the unbiased empirical covariance matrix is not a good estimate of the covariance matrix when the number of snapshots is small compared to the number of variables, as pointed out in50,51. This is because the sample covariance matrix S might not be positive definite anymore when only...
4.2.2 Covariance matrix C To find the correlation between the different dimensions d in the n samples, we first calculate the covariance or correlation matrix. The correlation matrix is needed when dimensions are not of unit length [2]. Since the feature vectors are normalized, the covariance ...