Startseite A study of chi-square-type distribution with geometrically distributed degrees of freedom in relation to distributions of geometric random sums
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A study of chi-square-type distribution with geometrically distributed degrees of freedom in relation to distributions of geometric random sums

  • Tran Loc Hung
Veröffentlicht/Copyright: 13. Januar 2020
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Abstract

The purpose of this paper is to study a chi-square-type distribution who degrees of freedom are geometric random variables in connection with weak limiting distributions of geometric random sums of squares of independent, standard normal distributed random variables. Some characteristics of chi-square-type random variables with geometrically distributed degrees of freedom including probability density function, probability distribution function, mean and variance are calculated. Some asymptotic behaviors of chi-square-type random variables with geometrically distributed degrees of freedom are also established via weak limit theorems for normalized geometric random sums of squares of independent, standard normal distributed random variables. The rates of convergence in desired weak limit theorems also estimated through Trotter’s distance. The received results are extensions and generalizations of several known results.

  1. Communicated by Gejza Wimmer

6 Appendix

We must recall that Proposition 2.1 in Section 2 could be directly proved as follows:

Let X be a random variable with 𝔼(|X|k) < +∞. Then 𝔼(|X|j) < +∞, for any 1 ≤ jk, and

E X j 1 + E X k .

Proof

We shall begin with showing that

E | X | j = E ( | X | j I ( | X | 1 ) + | X | j I ( | X | > 1 ) ) = E ( | X | j I ( | X | 1 ) ) + E ( | X | j I ( | X | > 1 ) ) for 1 j k . (6.1)

Consider the first term of right-side of (6.1), since |X| < 1 for j ≥ 1 and 𝕀(|X| ≤ 1) ≤ 1, it follows that

| X | j I ( | X | 1 ) 1.

Hence

E ( | X | j I ( | X | 1 ) ) 1.

According to second term of right-side of (6.1), since |X| > 1 and 1 ≤ jk, we have

| X | j | X | k .

Thus, for 1 ≤ jk,

E ( | X | j I ( | X | ) > 1 ) E ( | X | k I ( | X | ) > 1 )

Moreover, it follows that

E ( | X | k I ( | X | ) > 1 ) E ( | X | k I ( | X | ) > 1 ) + E ( | X | k I ( | X | ) 1 ) = E | X | k .

Finally, from (6.1), for each random variable X, if 1 ≤ jk, it may be concluded that

E | X | j 1 + E | X | k .

From above inequality, if 𝔼|X|k < +∞, then 𝔼|X|j < +∞, for 1 ≤ jk. The proof is complete. □

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Received: 2018-12-28
Accepted: 2019-07-02
Published Online: 2020-01-13
Published in Print: 2020-02-25

© 2020 Mathematical Institute Slovak Academy of Sciences

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