The Convergence Rate in Precise Asymptotics for Series of Large Deviations




Precise asymptotics, Convergence rate, Series of large deviations


Background. In the paper the sequence of independent identically distributed random variables   is considered. We are interested in conditions for the convergence of series   for different values of parameters , and any . Such series appears while studying complete convergence as well as investigating various problems on large deviations in limit theorems of probability theory. The new approach to study such series is proposed by C. Heyde, who showed that if  then Further, this result was extended by O. Klesov, and finally generalized by A. Gut, J. Steinebach and J. Hi for the series   with  , and .

Objective. We consider the series  for one-side deviations. The main purpose of the paper is to study precise asymptotics of function  while 

Methods. The methods used to prove the main result is as follows: first we find the asymptotics for the partial case, i.e. we assume that random variables are Gaussian random variables, further we extend obtained result to the general case by means of estimations of rate of convergence in the Central limit theorem.

Results. In the paper the precise asymptotics of series  while  for  is obtained. Strict assumptions imposed on parameter  are connected with the application of Nagaev inequality on the rate of convergence in the Central limit theorem.

Conclusions. The asymptotics  ought to be considered for other values of  as well. This requires though new techniques since the use of Nagaev inequality leads to some restrictions upon .  Since series  converges (under some moment conditions) for any  then further investigations may concern the behavior of this series for  and  

Author Biography

Юлія Олександрівна Грегуль, NTUU KPI

Yuliya O. Gregul.
Assistant at the Department of Mathematical Analysis and Probability Theory


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