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Journal of Probability and Statistics
Volume 2015, Article ID 723924, 5 pages
http://dx.doi.org/10.1155/2015/723924
Research Article

Statistical Tests for the Reciprocal of a Normal Mean with a Known Coefficient of Variation

Department of Mathematics and Statistics, Faculty of Science and Technology, Thammasat University, Pathum Thani 12121, Thailand

Received 14 September 2015; Revised 27 October 2015; Accepted 28 October 2015

Academic Editor: Aera Thavaneswaran

Copyright © 2015 Wararit Panichkitkosolkul. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

An asymptotic test and an approximate test for the reciprocal of a normal mean with a known coefficient of variation were proposed in this paper. The asymptotic test was based on the expectation and variance of the estimator of the reciprocal of a normal mean. The approximate test used the approximate expectation and variance of the estimator by Taylor series expansion. A Monte Carlo simulation study was conducted to compare the performance of the two statistical tests. Simulation results showed that the two proposed tests performed well in terms of empirical type I errors and power. Nevertheless, the approximate test was easier to compute than the asymptotic test.

1. Introduction

The reciprocal of a normal mean has been the subject of research in the areas of nuclear physics, agriculture, and economics. For example, Lamanna et al. [1] studied charged particle momentum, , where is the track curvature of a particle. The reciprocal of a normal mean is given bywhere is the population mean. A variety of researchers have studied the reciprocal of a normal mean. For instance, Zaman [2] discussed the estimators without moments in the case of the reciprocal of a normal mean. The maximum likelihood estimate of the reciprocal of a normal mean with a class of zero-one loss functions was proposed by Zaman [3]. Withers and Nadarajah [4] presented a theorem to construct the point estimators for the inverse powers of a normal mean.

Suppose we have prior information for the coefficient of variation; , where is the standard deviation of a population. This phenomenon arises in area of agricultural, biological, environmental, and physical sciences. For instance, in environmental science, Bhat and Rao [5] explain that there are some situations that show the standard deviation of a pollutant is directly related to the mean, which means is known. In clinical chemistry, Bhat and Rao [5] also state that “when the batches of some substance (chemicals) are to be analyzed, if sufficient batches of the substances are analyzed, their coefficients of variation will be known.” Furthermore, in medical, biological, and chemical studies, Brazauskas and Ghorai [6] provide some examples showing problems concerning coefficients of variation that are known in practice. Many statistical problems are due to the study of the mean of a normal distribution with a known coefficient of variation (see, e.g., Searls [7], Khan [8], Arnholt and Hebert [9], and Srisodaphol and Tongmol [10] and the references cited in the mentioned papers).

The estimation and testing of a normal mean with a known coefficient of variation are not equivalent to the case of known variance since the population mean is unknown. Furthermore, let be a random sample of size from a normal distribution. The estimator of is where is the sample mean. The distribution of is not a normal distribution. Therefore, we cannot construct a confidence interval for a normal mean and then transform the confidence interval for the reciprocal of a normal mean. Similarly, the hypothesis testing for a normal mean is not equivalent to the hypothesis testing for the reciprocal of a normal mean because the testing is developed based on the distribution of a sample mean.

Two confidence intervals for the reciprocal of a normal mean with a known coefficient of variation were proposed by Wongkhao et al. [11]. Their confidence intervals can be applied when the coefficient of variation of a control group is known. One of their confidence intervals was developed based on an asymptotic normality of the pivotal statistic , where follows the standard normal distribution. The other confidence interval was constructed based on the generalized confidence interval [12]. Simulation results showed that the coverage probabilities of the two confidence intervals were not significantly different. The limits of the asymptotic confidence interval are difficult to compute since they depend on an infinite summation. However, there has not yet been a study using a statistical test for the reciprocal of a normal mean with a known coefficient of variation. Therefore, we were motivated to propose two statistical tests for the reciprocal of a normal mean with a known coefficient of variation. One of the proposed statistical tests was based on an asymptotic method. The other statistical test was developed using the simple approximate expression for the expectation of the estimator of In addition, we also compared the empirical probability of type I errors and the empirical power of the test using a Monte Carlo simulation.

The structure of this paper is as follows: Section 2 provides the theorem and corollary, which were used for constructing the asymptotic test. An approximate test is proposed in Section 3. The performance of the two proposed statistical tests for is investigated through a Monte Carlo simulation study in Section 4. We then conclude this paper in Section 5.

2. Asymptotic Test for the Reciprocal of a Normal Mean with a Known Coefficient of Variation

The null hypothesis of interest is The theorem and corollary concerning the expectation of   and proposed by Wongkhao et al. [11] were used to construct the asymptotic test as reviewed below.

Theorem 1 (Wongkhao et al. [11]). Let be a random sample of size from a normal distribution with mean and variance The estimator of is where When a coefficient of variation is known, the expectation of   is

Proof of Theorem 1. This theorem was proved in Wongkhao et al. [11].

From (2), and , where Thus, the unbiased estimator of is

Corollary 2. From Theorem 1,

Proof of Corollary 2. This corollary was proved in Wongkhao et al. [11].

From the central limit theorem, we use the fact that Under which is true, we getLet denote the upper quantile of the standard normal distribution. On the basis of the above standard normal distribution, the level- tests conducted are given in Table 1.

Table 1

3. Approximate Test for the Reciprocal of a Normal Mean with a Known Coefficient of Variation

In this section, we present an approximate test using the simple approximate expression for the expectation and variance of To find a simple approximate expression, we use a Taylor series expansion of   around :

Theorem 3. Let be a random sample of size from a normal distribution with mean and variance The estimator of is where The approximate expectation and variance of when a coefficient of variation is known are, respectively,

Proof of Theorem 3. Consider random variable where has support Let find approximations for and using Taylor series expansion of around as in (5). The mean of can be found by applying the expectation operator to the individual terms (ignoring all terms higher than two),An approximation of the variance of is obtained using the first-order terms of the Taylor series expansion:

It is clear from (7) that is asymptotically unbiased and , where Thus, the unbiased estimator of is From (8), is consistent Under , we apply the central limit theorem and Theorem 3,Based on this we can now conduct the level- tests (see Table 2).

Table 2

4. Simulation Results

In this section, we performed simulation experiments to compare the behavior of the two statistical tests in a variety of situations. The first study compared the type I errors of the two statistical tests and checked how well they behave under the nominal level The second study compared their corresponding powers. We take and . We take and estimate the type I errors () and power (). The sample sizes are set at , and 50. To test the following hypothesis, we set the significance level of at 0.05:We repeated the above procedure 20,000 times for each setting using the R statistical software [13] and report the empirical type I errors and powers of the tests in Table 3.

Table 3: The empirical type I errors and powers of the asymptotic test and the approximate test.

As can be seen from Table 3, the empirical type I errors of both statistical tests were close to the given nominal level and were able to control the probability of type I errors for all situations. In addition, the empirical type I errors of the approximate test were not significantly different from those of the asymptotic test for all scenarios. Regarding the power comparisons, we observed that there was no difference in the empirical powers of the two statistical tests. The powers of both the asymptotic test and the approximate test decreased as increased due to the increased variability in the data. Additionally, the empirical powers increased as the sample sizes got larger. However, the empirical powers did not increase or decrease according to the values of when and However, the approximate test was much easier to calculate compared to the asymptotic test because the latter was based on an infinite summation.

5. Conclusion

In this paper, we presented two statistical tests for the reciprocal of a normal population mean with a known coefficient of variation. This situation usually arises when the coefficient of variation of the control group is known. The asymptotic test was based on the expectation and variance of the estimator of the reciprocal of a normal mean. The approximate expectation and variance of the estimator by Taylor series expansion were used to develop the approximate test. The simulation study indicated that the approximate test performs as efficiently as the asymptotic test in terms of empirical type I errors and empirical power. However, the computation of the approximate test was less complicated than the asymptotic test.

Conflict of Interests

The author declares that there is no conflict of interests regarding the publication of this paper.

References

  1. E. Lamanna, G. Romano, and C. Sgarbi, “Curvature measurements in nuclear emulsions,” Nuclear Instruments & Methods in Physics Research, vol. 187, no. 2-3, pp. 387–391, 1981. View at Publisher · View at Google Scholar · View at Scopus
  2. A. Zaman, “Estimators without moments: the case of the reciprocal of a normal mean,” Journal of Econometrics, vol. 15, no. 2, pp. 289–298, 1981. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  3. A. Zaman, “Admissibility of the maximum likelihood estimate of the reciprocal of a normal mean with a class of zero-one loss functions,” Sankhyā, vol. 47, no. 2, pp. 239–246, 1985. View at Google Scholar · View at MathSciNet
  4. C. S. Withers and S. Nadarajah, “Estimators for the inverse powers of a normal mean,” Journal of Statistical Planning and Inference, vol. 143, no. 2, pp. 441–455, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  5. K. Bhat and K. A. Rao, “On tests for a normal mean with known coefficient of variation,” International Statistical Review, vol. 75, no. 2, pp. 170–182, 2007. View at Publisher · View at Google Scholar · View at Scopus
  6. V. Brazauskas and J. Ghorai, “Estimating the common parameter of normal models with known coefficients of variation: a sensitivity study of asymptotically efficient estimators,” Journal of Statistical Computation and Simulation, vol. 77, no. 8, pp. 663–681, 2007. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  7. D. T. Searls, “A note on the use of an approximately known coefficient of variation,” The American Statistician, vol. 21, no. 3, pp. 20–21, 1967. View at Publisher · View at Google Scholar
  8. R. A. Khan, “A note on estimating the mean of a normal distribution with known coefficient of variation,” Journal of the American Statistical Association, vol. 63, no. 323, pp. 1039–1041, 1968. View at Publisher · View at Google Scholar
  9. A. T. Arnholt and J. L. Hebert, “Estimating the mean with known coefficient of variation,” The American Statistician, vol. 49, no. 4, pp. 367–369, 1995. View at Publisher · View at Google Scholar
  10. W. Srisodaphol and N. Tongmol, “Improved estimators of the mean of a normal distribution with a known coefficient of variation,” Journal of Probability and Statistics, vol. 2012, Article ID 807045, 5 pages, 2012. View at Publisher · View at Google Scholar · View at MathSciNet
  11. A. Wongkhao, S. Niwitpong, and S. Niwitpong, “Confidence interval for the inverse of a normal mean with a known coefficient of variation,” International Journal of Mathematical, Computational, Statistical, Natural and Physical Engineering, vol. 7, no. 9, pp. 877–880, 2013. View at Google Scholar
  12. S. Weerahandi, “Generalized confidence intervals,” Journal of the American Statistical Association, vol. 88, no. 423, pp. 899–905, 1993. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  13. R. Ihaka and R. Gentleman, “R: a language for data analysis and graphics,” Journal of Computational and Graphical Statistics, vol. 5, no. 3, pp. 299–314, 1996. View at Google Scholar · View at Scopus