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
floator array_like Points for which cdf is evaluated.
xcan be scalar or 1-dim.- samplearray_like
1-d sample from which kernel estimate is computed.
- bw
float Bandwidth parameter, there is currently no default value for it.
- kernel_type
strorcallable Kernel name or kernel function. Currently supported kernel names are “beta”, “beta2”, “gamma”, “gamma2”, “bs”, “invgamma”, “invgauss”, “lognorm”, “recipinvgauss” and “weibull”.
- weights
Noneor array_like,optional 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
int,optional 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.
- x
- Returns: