kerneltæthedskurver

Kernel density estimation is a non-parametric way to estimate the probability density function of a random variable. It is a smoothing technique that helps to visualize the distribution of data without making strong assumptions about the underlying shape of the distribution. Instead of assuming a specific distribution like a normal curve, kernel density estimation uses a kernel function to place a "bump" of probability around each data point. The sum of these bumps then forms the estimated probability density function.

The choice of kernel function is important but often has a less significant impact on the final shape than the bandwidth parameter. The bandwidth controls the width of the bumps. A small bandwidth will result in a highly localized estimate, which might be too noisy and capture individual data points. A large bandwidth will produce a very smooth estimate, potentially obscuring important features of the distribution. Selecting an appropriate bandwidth is crucial for obtaining a meaningful representation of the data's density. Common kernel functions include the Gaussian, Epanechnikov, and uniform kernels. The resulting curve is often referred to as a kerneldæthedskurve in Danish. This method is widely used in exploratory data analysis, visualization, and as a step in more complex statistical modeling.