Applied Mathematics & Information Sciences

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This paper contains a complete procedure for calculating the value of a conditional quantile estimator. The concept is based on the nonparametric kernel estimator method, which frees the algorithm from the random variables’ distributions. The procedure was worked out in a ready-to-use form – specific formulas for functions and the parameter used were given. The practical implementation of this method is very simple, and its computational complexity is linear with respect to random sample size as well as the dimension of conditioning variable. Thanks to a clear, near intuitive interpretation it can easily be modified or generalized depending on the individual needs of atypical applications. In particular, conditioning variables can be taken into account, not only continuous (real), but also binary, discrete and categorized, or any of their combinations.