statsmodels.nonparametric.kernels_asymmetric.pdf_kernel_asym

statsmodels.nonparametric.kernels_asymmetric.pdf_kernel_asym(x, sample, bw, kernel_type, weights=None, batch_size=10)[source]

Density estimate based on asymmetric kernel.

Parameters:

  • x (array_like, float) – Points for which density is evaluated. x can be scalar or 1-dim.

  • sample (ndarray, 1-d) – Sample from which kernel estimate is computed.

  • bw (float) – Bandwidth parameter, there is currently no default value for it.

  • kernel_type (str or callable) – Kernel name or kernel function. Currently supported kernel names are “beta”, “beta2”, “gamma”, “gamma2”, “bs”, “invgamma”, “invgauss”, “lognorm”, “recipinvgauss” and “weibull”.

  • weights (None or ndarray) – If weights is not None, then kernel for sample points are weighted by it. No weights corresponds to uniform weighting of each component with 1 / nobs, where nobs is the size of sample.

  • batch_size (float) –

    If x is an 1-dim array, then points can be evaluated in vectorized form. To limit the amount of memory, a loop can work in batches. The number of batches is determined so that the intermediate array sizes are limited by

    np.size(batch) * len(sample) < batch_size * 1000.

    Default is to have at most 10000 elements in intermediate arrays.

Returns:

pdf – Estimate of pdf at points x. pdf has the same size or shape as x.

Return type:

float or ndarray