View source: R/kernel_regression_estimator.R
make_pd | R Documentation |
This function can make a function positive-definite using the methods proposed by P. Hall and his coauthors.
make_pd(x, method.1 = TRUE)
x |
A vector of numeric values of an estimated autocovariance function. |
method.1 |
Should method 1 be used (TRUE) or method 2 (FALSE). |
This function perform positive-definite adjustments proposed by P. Hall and his coauthors.
Method 1 is as follows:
Take the discrete Fourier cosine transform,
\widehat{\mathcal{F}}(\theta)
.
Compute a modified spectrum \widetilde{\mathcal{F}}(\theta) = \max(\widehat{\mathcal{F}}(\theta), 0)
for all sample frequencies.
Perform the Fourier inversion to obtain a new estimator.
Method 2 is as follows:
Take the discrete Fourier cosine transform \widehat{\mathcal{F}}(\theta)
.
Find the smallest frequency where its associated value in the spectral domain is negative
\hat{\theta} = \inf\{ \theta > 0 : \widehat{\mathcal{F}}(\theta) < 0\}.
Compute a modified spectrum \widetilde{\mathcal{F}} = \widehat{\mathcal{F}}(\theta)\textbf{1}(\theta < \hat{\theta}),
where \textbf{1}(A)
is the indicator function over the set A.
Perform the Fourier inversion.
A vector that is the adjusted function.
Hall, P. & Patil, P. (1994). Properties of nonparametric estimators of autocovariance for stationary random fields. Probability Theory and Related Fields 99(3), 399-424. https://doi.org/10.1007/bf01199899
Hall, P., Fisher, N. I., & Hoffmann, B. (1994). On the nonparametric estimation of covariance functions. The Annals of Statistics 22(4), 2115-2134. https://doi.org/10.1214/aos/1176325774
Bilchouris, A. & Olenko, A (2025). On Nonparametric Estimation of Covariogram. Austrian Statistical Society 54(1), 112-137. https://doi.org/10.17713/ajs.v54i1.1975
X <- c(1, 2, 3, 4)
make_pd(X)
check_pd(make_pd(X))
check_pd(make_pd(X, method.1 = FALSE))
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