报告人： Irina Shevtsova（Moscow State University, Faculty of Computational Mathematics and Cybernetics, Department of Mathematical Statistics）
题目：Moment-type estimates for the rate of convergence in weak limit theorems for sums of independent random variables
We present moment-type estimates for the rate of convergence in weak limit theorems, including the classical central limit theorem, for sums of independent random variables. We consider two schemes of summation, with the number of summands being fixed or being a random variable, independent of the random summands, and following a mixed Poisson distribution. The latter includes, in particular, the usual Poisson, the inverse gamma-Poisson, the geometric and the negative binomial distributions with the corresponding limit laws being the Gaussian, the Student, the Laplace distributions and the gamma-mixture of Gaussian laws. All the estimates will be presented in an explicit form up to concrete numerical values of the entering absolute constants which allows to use them in practice.