Received date: May 31, 2017; Accepted date: June 16, 2017; Published date: June 30, 2017
Citation: Banik S, Golam Kibria BM (2017) Size and Power Properties of Some Test Statistics for Testing the Population Correlation Coefficient ρ. J Biom Biostat 8: 353. doi: 10.4172/2155-6180.1000353
Copyright: © 2017 Banik S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Correlation measures the strength of association between two variables, which plays an important role in various fields, such as Health Science, Economics, Finance, Engineering, Environmental science among others. Several tests for testing the population correlation coefficient are proposed in a literature by various researchers at different time points. This paper evaluates the performance of some of the prominent test statistics for testing the population correlation coefficient based on empirical size and power of the tests. Some bivariate distributions, such as normal, lognormal, gamma and chi-square are considered to compare the performance of the test statistics. We believe that the findings of this paper will make an important contribution to select some good test statistics to find the relationship between two variables.
Bivariate distribution; Bootstrapping; Correlation coefficients; Hypothesis tests; Monte Carlo simulation; Power; Size
One of the most useful statistical tools for quantifying the relationship between two continuous variables is the coefficient of correlation that developed by Pearson  from a related idea introduced by Galton . In statistics, Pearson’s correlation coefficient is used to find the linear relationship between two quantitative variables (say) X and Y. It gives a value between -1 and +1 inclusive, where -1 indicates a perfect negative correlation, 0 is no correlation and +1 indicates a perfect positive correlation between X and Y. Since the population correlation coefficient, ρ is usually unknown, it is necessary to estimate it by estimator, r from the observed data or sample information. Even the sample correlation coefficient (r) is a biased estimator of population correlation coefficient ρ, the biasness disappears with the increase of sample size. When there is a question of estimation, its estimation accuracy and thus the validity through the hypothesis testing is essential. Several researchers considered several confidence intervals for estimating the population correlation coefficient ρ . However, a comparison of several test statistics for testing the population correlation coefficient is limited in literature. In this paper, we have made an attempt to consider several test statistics for testing the population correlation coefficient. Since, a theoretical comparison among the test procedures is not possible, a simulation study will be conducted to compare the performance of the test statistics based on empirical size and power of the test. We believe that the findings of this study will make an important contribution to literature to choose appropriate test statistics for testing the population correlation coefficient for practitioners.
The paper is organized as follows: One proposed and some existing methods for testing the population correlation coefficient are described in section 2. A Monte Carlo simulation study along with results is discussed in section 3. Finally, some concluding remarks are given in section 4.
Suppose we are interested to find the linear relationship between two variables X and Y. Then the population correlation coefficient between two variables X and Y is denoted by ρ and is defined by
The corresponding sample correlation coefficient is defined by
It can be shown that −1 ≤ ρ ≤ 1. A value of 1 implies that a linear equation describes the relationship between X and Y perfectly, with all data points lying on a line for which Y increases as X increases. A value of −1 implies that all data points lie on a line for which Y decreases as X increases. A value of 0 implies that there is no linear association between X and Y. Several methods for testing for population correlation coefficient, H0: ρ=0 vs. H1: ρ ≠ 0 are given as follows.
The classical test statistic
Suppose, the sample correlation coefficient, r is a point estimator of ρ. The distribution of r when ρ is zero was for the first time studied by a student . Thus, a common test is that of whether or not a linear relationship exists between two variables X and Y. The test statistic is defined as follows:
where n is the sample size and (n-2) is the degrees of freedom(df). Thus the critical value for this test statistic can be obtained from t-distribution with (n-2) degrees of freedom.
Fisher’s large sample test statistic
Since the sampling distribution of Pearson's r is not normally distributed, Pearson's r is converted to Fisher's z and the test statistic for testing H0: ρ=0 vs. H1: ρ ≠ 0 is computed using Fisher's  transformation and is given as follows:
Where and z0 is the value of z under the null hypothesis where
The distribution of tz has a standard normal distribution.
Gorsuch and Lehmann test statistics
To improve the performances of the classical statistic, Gorsuch and Lehmann  modified the classical statistic and the Fisher statistic and proposed the following four statistics for testing H0: ρ=0 vs. H1: ρ ≠ 0 based on different standard errors of r:
Modified classical statistics:
Where the critical value of tGL1 is assumed to be 2 (details see Gorsuch and Lehmann ) and the distribution of tGL2 follows t distribution with (n-1) df.
Modified Fisher statistics:
where the critical value of tGL3 is assumed to be 2 (details see Gorsuch and Lehmann ) and tGL4 has a t-distribution with (n-1) degrees of freedom.
Proposed test statistic
We know that , where b1 is estimator of β1 for the model yt=β0+β1xt+et, β0 is the constant, β1 is the regression coefficient of y on x and et ~ N (0, σ2). The test statistic for testing H0: ρ=0 vs. H1: ρ ≠ 0 is given by
The distribution of tSK has t-distribution with (n-2) df.
Parametric bootstrap test statistic
Let where ith random samples are denoted by x(i)and y(i) for i =1,2, …, B and B is the number of bootstrap samples . The test statistic for testing H0: ρ=0 vs. H1: ρ ≠ 0 is given by
Where critical values of the above statistic is the which is the (α/2)th sample quintiles of tpboot.
Parametric bootstrap Fisher z test statistic
The test statistic for testing H0: ρ=0 vs. H1: ρ ≠ 0is computed using Fisher’s z  transformation and is given as follows:
Where critical values of the above statistic is the which is the (α/2)th sample quintiles of tFboot
Parametric bootstrap version of proposed test statistic
The test statistic for testing H0: ρ=0 vs. H1: ρ ≠ 0is computed follows:
Where critical values of the above statistic is the , which is the (α/2)th sample quintiles of tSKboot
Bootstrap bias corrected acceleration test statistic
This method is introduced by Efron and Tibshirani . The test statistic for testing H0: ρ=0 vs. H1: ρ ≠ 0is the t-statistic defined in eqn. (1) and the critical value is calculated by , where r() is the (100α)% percentile of the distribution of is the standard normal cumulative distribution function, bias correction is the inverse function of cumulative distribution function of the Z distribution, acceleration factor , r is the correlation between x and y and ri is the correlation between x and y of (n-1) observations without the ith observation.
The main goal of this paper is to evaluate the performance of test statistics for testing population correlation coefficient based on size and power properties, discussed in section 2. Since a theoretical comparison among the tests is not possible, a simulation study has been conducted in this section.
MATLAB (2015) programming language was used to run simulations and to make necessary tables. The most common level of significance α=0.05 is considered and assumed random sample sizes n=10, 30, 50, 80 and 100 and ρ1=-0.5, -0.9, 0.3, 0.8 and 0.99. We have considered 2500 replications for our simulation experiments and 1500 bootstrap samples for each selected random samples sizes. Random samples produced from the following population distributions:
(a) Bivariate normal with μ1=15, μ2=20 and σ1=10, σ2=4
(b) Bivariate log normal with μ1=15, μ2=20 and σ1=10, σ2=4
(c) Bivariate gamma with shape parameters 1 and mean parameters 2
(d) Bivariate chi-square with dfs 1 and 3
Table 1 presents estimated sizes of the selected test statistics for selected values of n and ρ, when random samples are generated from the bivariate normal distribution. For a visual expression, simulation results are presented graphically in Figure 1.
Table 1: Estimated sizes at the 5% level of significance for bivariate normal data.
We can see from Figure 1 and Table 1 is that for all sample sizes, all proposed test statistics except GL1 and SK Boot have empirical sizes close to the 5% nominal level.
We have presented estimated sizes when data are generated from the bivariate lognormal distribution in Table 2 and depicted results for visual inspection in Figure 2. From Table 2 and Figure 2, we observe that for moderate to large sample sizes, tBoot, FBoot, SKBoot and BCABoot have sizes close to the nominal level, while rest of the tests achieve nominal level only when sample sizes are large.
Table 2: Estimated sizes at the 5% level of significance for bivariate lognormal data.
In Tables 3 and 4, we have reported estimated sizes when data generated from the bivariate gamma and bivariate chi-square distribution respectively. We find that all tests have correct sizes expect GL1 and SKboot. GL1 test has small sizes than the nominal level and SKboot has higher sizes than the nominal level (Figures 3 and 4).
Table 3: Estimated sizes at the 5% level of significance for the bivariate gamma data.
Table 4: Estimated sizes at the 5% level of significance for the bivariate chi-square data.
In Table 5, we have presented the estimated powers when data are generated from the bivariate normal distribution for various sample sizes and various values of ρ. We observed that for small sample size n=10 (Figure 5), Fisher, G0L3, GL4, FBoot and SKBoot have good powers as compare to other test statistics. For sample sizes 50 or above, (Figure 6 for n=50) we found that all test statistics have good powers except for ρ=0.3. We noted that for weak positive correlation, SKboot has highest power as compare to rest of the test statistics.
Table 5: Powers at the 5% level of significance for bivariate normal data.
Figure 8 presents estimated powers when data are generated from the bivariate lognormal distribution for n=80. We observed that all tests have good powers except bootstrap versions of t, Fisher, SK and BCA for ρ=0.3.
we have tabulated estimated power of various test statistics when data are generated from bivariate gamma and bivariate chi-square distributions (see Figure 9 (n =10) and Figure 10 (n=30) for better understanding). It is observed that power properties of the selected tests are similar when data generated from bivariate normal distribution or lognormal.
In Figures 11 and 12, we have plotted estimated powers for various values of n for ρ=-0.5 and ρ=0.8 to check effects of n on the selected tests. It is observed from these graphs that as n increases powers are also increases for all selected tests. We noted that for small sample sizes, Fisher, GL2, Fboot and SKboot tests are more powerful than the other considered tests. It is also noted that our proposed bootstrap version SKboot is more powerful than the other considered tests.
In Figures 13 and 14, we have plotted estimated powers for selected values of n and two selected values of ρ. Here also we observed same patterns like Figures 11 and 12 as sample size increases, estimated powers also increases. As compare to the Figure 11, we noted very low powers n=10 when data generated from the bivariate lognormal distribution. We noted that tboot and BCAboot tests have very low power compared to the other tests.
Figures 15-18 present estimated powers for various values of n and two selected values of ρ when data are generated from the bivariate gamma distribution and bivariate chi-square distribution respectively. Similar interpretation can be drawn from these figures, as we observed when data are generated from the bivariate normal distribution.
In this paper, we study the performance of several methods for testing the population correlation coefficient by means of a simulation study. Data were generated randomly from several bivariate distributions, namely, bivariate normal, bivariate lognormal, bivariate gamma and bivariate chi-square with a range of sample sizes. Overall, we found that test statistics, t, Fisher, GL2, GL3, GL4, SK and FBoot have sizes close to the 5% nominal level. Fisher, GL3, GL4, FBoot and SKBoot have good powers as compare to other test statistics. It appears from the simulation study is that the test statistics, Fisher, GL3, GL4, FBoot and SKBoot can be recommended for practitioners because these test statistics have good sizes and powers compare to the rest of selected test statistics.