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The KDE procedure performs either univariate or bivariate kernel density estimation. Statistical density estimation involves approximating a hypothesized probability density function from observed ...

Density estimation is a fundamental component in statistical analysis, aiming to infer the probability distribution of a random variable from a finite sample without imposing restrictive parametric ...

Estimation of the value of a density function at a point of continuity using a kernel-type estimator is discussed and improvements of the technique are presented. The generalized jackknife method is ...

where K 0 (·) is a kernel function, is the bandwidth, n is the sample size, and x i is the i th observation. The KERNEL option provides three kernel functions (K 0): normal, quadratic, and triangular.

Chronic kidney disease (CKD) is a growing problem in Nigeria, presenting challenges to the nation's health and economy. This study presents an analysis of 442 patients with CKD referred to the renal ...

Gordon Lee et al introduce a data-driven and model-agnostic approach for computing conditional expectations. The new method combines classical techniques with machine learning methods, in particular ...

In this paper we propose a novel approach to obtain the predictive density of global GDP growth. It hinges upon a bottom-up probabilistic model that estimates and combines single countries’ predictive ...