Computes several kernel functions(truncated, Bartlett, Daniell, QS, Parzen). These kernels are for constructing test statistics for testing pairwise independence.
kernelFun(type, z)A value that lies in the interval \([-1, 1]\).
A character string which indicates the name of the smoothing kernel.
kernelFun can be: 'truncated', 'bartlett', 'daniell', 'QS', 'parzen'.
No default is given.
A real number.
Maria Pitsillou and Konstantinos Fokianos.
kernelFun computes several kernel functions including truncated,
Bartlett, Daniell, QS and Parzen.
The exact definition of each of the above functions are given below:
Truncated $$ k(z) = \left\{ \begin{array}{ll} 1, & |z| \leq 1, \\[1ex] 0, & \mbox{otherwise}. \end{array} \right. $$
Bartlett $$ k(z) = \left\{ \begin{array}{ll} 1 - |z|, & |z| \leq 1, \\[1ex] 0, & \mbox{otherwise}. \end{array} \right. $$
Daniell $$ k(z) = \frac{\mbox{sin}(\pi z)}{\pi z}, z \in \Re - \{0\} $$
QS $$ k(z)=(9/5\pi^2z^2)\{\mbox{sin}(\sqrt{5/3}\pi z)/\sqrt{5/3}\pi z-\mbox{cos}(\sqrt{5/3}\pi z)\}, z \in \Re $$
Parzen $$ k(z) = \left\{ \begin{array}{ll} 1-6(\pi z/6)^2 + 6|\pi z/6|^3, & |z| \leq 3/\pi, \\[1ex] 2(1-|\pi z/6|)^3, & 3/\pi \leq |z| \leq 6/\pi, \\[1ex] 0, & \mbox{otherwise} \end{array} \right. $$
All these kernel functions are mainly used to smooth the generalized spectral density function, firstly introduced by Hong (1999). Assumptions and theoretical properties of these functions can be found in Hong (1996;1999) and Fokianos and Pitsillou (2017).
Edelmann, D, K. Fokianos. and M. Pitsillou. (2019). An Updated Literature Review of Distance Correlation and Its Applications to Time Series. International Statistical Review, 87, 237-262.
Fokianos K. and M. Pitsillou (2017). Consistent testing for pairwise dependence in time series. Technometrics, 159, 262-3270.
Pitsillou M. and Fokianos K. (2016). dCovTS: Distance Covariance/Correlation for Time Series. R Journal, 8, 324-340.
Hong, Y. (1996). Consistent testing for serial correlation of unknown form. Econometrica, 64, 837-864.
Hong, Y. (1999). Hypothesis testing in time series via the empirical characteristic function: A generalized spectral density approach. Journal of the American Statistical Association, 94, 1201-1220.
k1 <- kernelFun( "bartlett", z = 1/3 )
k2 <- kernelFun( "bar", z = 1/5 )
k3 <- kernelFun( "dan", z = 0.5 )
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