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:
- xarray_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
orcallable
Kernel name or kernel function. Currently supported kernel names are “beta”, “beta2”, “gamma”, “gamma2”, “bs”, “invgamma”, “invgauss”, “lognorm”, “recipinvgauss” and “weibull”.
- weights
None
orndarray
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.
- xarray_like,
- Returns: