Bayesian density estimation from grouped continuous data
Computational Statistics & Data Analysis
Nonparametric multiplicative bias correction for kernel-type density estimation on the unit interval
Computational Statistics & Data Analysis
On nonparametric local inference for density estimation
Computational Statistics & Data Analysis
Nonparametric density estimation for positive time series
Computational Statistics & Data Analysis
A hierarchical modeling approach for clustering probability density functions
Computational Statistics & Data Analysis
Basic Singular Spectrum Analysis and forecasting with R
Computational Statistics & Data Analysis
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The application of Singular Spectrum Analysis (SSA) to the empirical distribution function sampled at a grid of points spanning the range of the sample leads to a novel and promising method for the computer-intensive nonparametric estimation of both the distribution function and the density function. SSA yields a data-adaptive filter, whose length is a parameter that controls the smoothness of the filtered series. A data-adaptive algorithm for the automatic selection of a general smoothing parameter is introduced, which controls the number of modes of the estimated density. Extensive computer simulations demonstrate that the new automatic bandwidth selector improves on other popular methods for various densities of interest. A general uniform error bound is proved for the proposed SSA estimate of the distribution function, which ensures its uniform consistency. The simulation results indicate that the SSA density estimate with the automatic choice of the filter length outperforms the kernel density estimate in terms of the mean integrated squared error and the Kolmogorov-Smirnov distance for various density shapes. Two applications to problems arising in photovoltaic quality control and economic market research are studied to illustrate the benefits of SSA estimation.