The convolution-based model of the electrical breakdown time delay distribution is applied for statistical analysis of experimental results obtained in neon-filled diode tube at 6.5 mbar. At first, the numerical breakdown time delay density distributions are obtained by stochastic modeling as the sum of two independent random variables, the electrical breakdown statistical time delay with exponential, and discharge formative time with Gaussian distribution. Then, the single characteristic breakdown time delay distribution is obtained as the convolution of these two random variables with previously determined parameters. These distributions show good correspondence with the experimental distributions, obtained on the basis of 1000 successive and independent measurements. The shape of distributions is investigated, and corresponding skewness and kurtosis are plotted, in order to follow the transition from Gaussian to exponential distribution.

The statistical analysis of the experimentally observed electrical breakdown time delay distributions in the krypton-filled diode tube at 2.6mbar is presented. The experimental distributions are obtained on the basis of 1000 successive and independent measurements. The theoretical electrical breakdown time delay distribution is evaluated as the convolution of the statistical time delay with exponential, and discharge formative time with Gaussian distribution. The distribution parameters are estimated by the stochastic modelling of the time delay distributions, and by comparing them with the experimental distributions for different relaxation times, voltages, and intensities of UV radiation. The transition of distribution shapes, from Gaussian-type to the exponential-like, is investigated by calculating the corresponding skewness and excess kurtosis parameters. It is shown that the mathematical model based on the convolution of two random variable distributions describes experimentally obtained time delay distributions and the separation of the total breakdown time delay to the statistical and formative time delay.

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