statsmodels.nonparametric.kernels_asymmetric.cdf_kernel_asym¶

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

Estimate of cumulative distribution 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:¶

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

Return type:¶

float or ndarray