[Nonparametric estimation of 1-dimensional continuous distribution density functions using the continuous LOLINREG approximation].
A nonparametric method for estimation of one-dimensional continuous probability distribution functions is presented. Procedures for calculation of estimation of the unknown distribution function and the distribution density will be discussed in their application. 2 items are what type of weight function may be chosen for the proposed local-linear continuous approximation of the empirical distribution function by the least squares method (LOLINREG), and upon what value of bandwidth- or smoothing parameter one optimally should settle. The latter problem is practically very important with respect to the quality of the estimation results. Examples of simulated measurements which come from a standardized normal distribution as random numbers serve to demonstrate the mode of working, the advantages as well as the limits of the presented continuous LOLINREG-approximation.
D006618:Hip Dislocation, Congenital / Q000453:epidemiology; D006801:Humans; D007231:Infant, Newborn; D008433:Mathematics; D008962:Models, Theoretical
Nichtparametrische Schätzung eindimensionaler stetiger Verteilungsdichten mittels stetiger LOLINREG-Approximation.