statsmodels.nonparametric.kernels_asymmetric.kernel_cdf_bs#

statsmodels.nonparametric.kernels_asymmetric.kernel_cdf_bs(x, sample, bw)[source]#

Birnbaum Saunders (normal) kernel for cdf estimation

Parameters:

xfloat or array_like: Points at which the kernel is evaluated. x can be scalar or 1-dim.
samplearray_like: 1-d sample from which the kernel estimate is computed.
bwfloat: Bandwidth parameter, there is currently no default value for it.

Returns:

ndarray: Kernel values evaluated at x for each point in sample.

References

[1]

Jin, Xiaodong, and Janusz Kawczak. 2003. “Birnbaum-Saunders and Lognormal Kernel Estimators for Modelling Durations in High Frequency Financial Data.” Annals of Economics and Finance 4: 103-24.

[2]

Mombeni, Habib Allah, B Masouri, and Mohammad Reza Akhoond. 2019. “Asymmetric Kernels for Boundary Modification in Distribution Function Estimation.” REVSTAT, 1-27.