On Inverse Moments for a Class of Nonnegative Random Variables
© Soo Hak Sung. 2010
Received: 1 April 2010
Accepted: 20 May 2010
Published: 15 June 2010
Using exponential inequalities, Wu et al. (2009) and Wang et al. (2010) obtained asymptotic approximations of inverse moments for nonnegative independent random variables and nonnegative negatively orthant dependent random variables, respectively. In this paper, we improve and extend their results to nonnegative random variables satisfying a Rosenthal-type inequality.
where and means that as The left-hand side of (1.2) is the inverse moment and the right-hand side is the inverse of the moment. Generally, it is not easy to compute the inverse moment, but it is much easier to compute the inverse of the moment.
The inverse moments can be applied in many practical applications. For example, they appear in Stein estimation and Bayesian poststratification (see Wooff  and Pittenger ), evaluating risks of estimators and powers of test statistics (see Marciniak and Weso owski  and Fujioka ), expected relaxation times of complex systems (see Jurlewicz and Weron ), and insurance and financial mathematics (see Ramsay ).
For nonnegative asymptotically normal random variables , (1.2) was established in Theorem 2.1 of Garcia and Palacios . Unfortunately, that theorem is not true under the suggested assumptions, as pointed out by Kaluszka and Okolewski . Kaluszka and Okolewski  also proved (1.2) for ( in the i.i.d. case) when is a sequence of nonnegative independent random variables satisfying and (Lyapunov's condition of order 3), that is, with Hu et al.  generalized the result of Kaluszka and Okolewski  by considering for some instead of
Recently, Wu et al.  obtained the following result by using the truncation method and Bernstein's inequality.
Specifically, Wu et al.  proved the following result.
Wang et al.  obtained some exponential inequalities for negatively orthant dependent (NOD) random variables. By using the exponential inequalities, they extended Theorem 1.1 for independent random variables to NOD random variables without condition (1.3).
Note that the Rosenthal inequality holds for NOD random variables (see Asadian et al. ).
In this paper, we improve and extend Theorem 1.2 for independent random variables to random variables satisfying a Rosenthal type inequality. We also extend Wang et al.  result for NOD random variables to the more general case.
2. Main Results
The following theorem gives sufficient conditions under which the inverse moment is asymptotically approximated by the inverse of the moment.
In (2.1), are monotone transformations of If is a sequence of independent random variables, then (2.1) is clearly satisfied from the Rosenthal inequality (1.8). There are many sequences of dependent random variables satisfying (2.1) for all Examples include sequences of NOD random variables (see Asadian et al. ), -mixing identically distributed random variables satisfying (see Shao ), -mixing identically distributed random variables satisfying (see Shao ), negatively associated random variables (see Shao ), and -mixing random variables (see Utev and Peligrad ).
We can extend Theorem 1.1 for independent random variables to the more general random variables by using Theorem 2.1. To do this, the following lemma is needed.
Hence the result is proved by (2.24) and (2.26).
By using Theorem 2.1, we can obtain the following theorem which improves and extends Theorem 1.1 for independent random variables to the more general random variables satisfying the Rosenthal-type inequality (2.1).
Let be a sequence of nonnegative random variables with Let and be defined by (1.1). Assume that the Rosenthal-type inequality (2.1) with holds for all where is the same as in (ii). Furthermore, assume that
Combining (2.36) with (2.37) gives the desired result.
Wang et al.  extended Wu et al.  result (see Theorem 1.1) to NOD random variables without condition (1.3). As observed in Remark 2.2, (2.1) holds for not only independent random variables but also NOD random variables. Hence Theorem 2.4 improves and extends the results of Wu et al.  and Wang et al.  to the more general random variables.
The conditions of Theorem 2.6 are much weaker than those of Theorem 1.2 in the following three directions.
(iii)The condition in Theorem 1.2 is not needed in Theorem 2.6. Therefore Theorem 2.6 improves and extends Wu et al.  result (see Theorem 1.2) to the more general random variables.
The author is grateful to the editor Andrei I. Volodin and the referees for the helpful comments. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2010-0013131).
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