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The methods of nonparametric statistics are very useful in data analysis. One of the most popular methods is called Parzen-Rosenblatt approximation. This method turns out to be effective, for example, in a problem of estimation of longevity of pipelines or in the analysis of the statistical characteristics of traffic flows. This paper discusses the recommendations for application of a method, which was performed by Parzen and Rosenblatt, in a problem of recovering a probability density function from a sample of random data with a bounded scattering region. It was shown that there are some difficulties during calculation of information functional. This paper gives an explanation of causes which lead to a nonmonotonicity of an information functional and which are based on a finite precision of computer calculations. It was proved a choice of initial value of smoothing parameter for different kernel types and was proposed an algorithm for finding a maximal value of information functional. 1. Preface The main problem of mathematical statistics is recovering the distribution function from a sample of random data obtained as a result of some experiments [1]. This problem is of great practical importance, for example, when solving the problems of strength reliability of elements and objects of oil and gas equipment [2]. The problem has the following statement: using experimental sample values of a random variable , 1, i X i N  from general population to find out distribution function     Pr F y X y   which is connected with probability density function ( ) f y by the following integral statement:

journal of physics. conference series

About specialities of numerical estimation of smoothing parameter of probability density functions of random sequences in Parzen-Rosenblatt approximation