bwmethod

bwmethod is a designation used in various statistical software packages to specify the method for selecting the bandwidth in kernel density estimation (KDE) and related smoothing procedures. The bandwidth controls the width of the kernel and thus the smoothness of the estimated density. Because different implementations use different naming and options for bandwidth selection, bwmethod is inherently context dependent and must be interpreted according to the software being used.

In many R packages, bandwidth methods are selected by passing a string such as nrd0, ucv, bcv, or SJ to a function that performs KDE. Each option represents a different rule of thumb or cross-validation approach for estimating the optimal smoothing parameter. The choice of method can affect bias and variance in the resulting density estimate and may perform differently depending on sample size and data distribution.

In Python libraries that implement KDE, such as SciPy's gaussian_kde, the analogous parameter is often named bw_method. It can take strings like 'scott' or 'silverman'—named after well-known rules of thumb—or a numeric scalar that scales a base bandwidth. The exact set of allowed values and their interpretation varies by library, but the central idea remains: bw_method selects how aggressively the data are smoothed.

Overall, bwmethod serves as a crucial tuning knob for nonparametric density estimation. Users should consult the specific library’s documentation to understand available options, defaults, and their implications for inference, especially with small samples or multimodal data. See also kernel density estimation, bandwidth selection, Silverman’s rule, and Scott’s rule.