Exact Null Distribution Research Articles

Uniform Approximation Is More Appropriate for Wilcoxon Rank-Sum Test in Gene Set Analysis

Gene set analysis is widely used to facilitate biological interpretations in the analyses of differential expression from high throughput profiling data. Wilcoxon Rank-Sum (WRS) test is one of the commonly used methods in gene set enrichment analysis. It compares the ranks of genes in a gene set against those of genes outside the gene set. This method is easy to implement and it eliminates the dichotomization of genes into significant and non-significant in a competitive hypothesis testing. Due to the large number of genes being examined, it is impractical to calculate the exact null distribution for the WRS test. Therefore, the normal distribution is commonly used as an approximation. However, as we demonstrate in this paper, the normal approximation is problematic when a gene set with relative small number of genes is tested against the large number of genes in the complementary set. In this situation, a uniform approximation is substantially more powerful, more accurate, and less intensive in computation. We demonstrate the advantage of the uniform approximations in Gene Ontology (GO) term analysis using simulations and real data sets.

A Kolmogorov-Smirnov Test for r Samples

We consider the problem of testing whether r ≥ 2 samples are drawn from the same continuous distribution F(x). The test statistic we will study in some detail is defined as the maximum of the circular differences of the empirical distribution functions, a generalization of the classical 2-sample Kolmogorov-Smirnov test to r ≥ 2 independent samples. For the case of equal sample sizes we derive the exact null distribution by counting lattice paths confined to stay in the scaled alcove ${\cal A}$r of the affine Weyl group Ar−1. This is done using a generalization of the classical reflection principle. By a standard diffusion scaling we derive also the asymptotic distribution of the test statistic in terms of a multivariate Dirichlet series. When the sample sizes are not equal the reflection principle no longer works, but we are able to establish a weak convergence result even in this case showing that by a proper rescaling a test statistic based on a linear transformation of the circular differences of the empirical distribution functions has the same asymptotic distribution as the test statistic in the case of equal sample sizes.

Testing the equality of several covariance matrices

Using a new approach based on Meijer G-functions and computer simulation, we numerically compute the exact null distribution of the modified-likelihood ratio statistic used to test the hypothesis that several covariances matrices of normal distributions are equal. Small samples of different sizes are considered, and for the case of two matrices, we introduce a new test based on determinants, with the null distribution of its criterion also fully computable. Comparisons with published results show the accuracy of our approach, which is proved to be more flexible and adaptable to different cases.

Small sample tests for the equality of intraclass correlation coefficients under unequal family sizes

In this paper, the test for equality of several intraclass correlation coefficients is proposed when the sample size is small. First, we consider a case with two populations. The exact null distribution of the test statistic is derived. We use a Monte Carlo simulation study and find that the performance of our new test is superior to the tests that were proposed by Young and Bhandary (1998). Work is then extended to develop a test for three intraclass correlation coefficients. Monte Carlo simulation studies are also conducted to compare our new test and the test that was proposed by Bhandary and Alam (2000). Based on our simulation result, we recommend our new test in practice when the sample sizes are small.

A simple nonparametric test for bivariate symmetry about a line

Over the years many researchers have dealt with testing the hypotheses of symmetry in univariate and multivariate distributions in the parametric and nonparametric setup. In a multivariate setup, there are several formulations of symmetry, for example, symmetry about an axis, joint symmetry, marginal symmetry, radial symmetry, symmetry about a known point, spherical symmetry, and elliptical symmetry among others. In this paper, for the bivariate case, we formulate a concept of symmetry about a straight line passing through the origin in a plane and accordingly develop a simple nonparametric test for testing the hypothesis of symmetry about a straight line. The proposed test is based on a measure of deviance between observed counts of bivariate samples in suitably defined pairs of sets. The exact null distribution and non-null distribution, for specified classes of alternatives, of the test statistics are obtained. The null distribution is tabulated for sample size from n=5 up to n=30. The null mean, null variance and the asymptotic null distributions of the proposed test statistics are also obtained. The empirical power of the proposed test is evaluated by simulating samples from the suitable class of bivariate distributions. The empirical findings suggest that the test performs reasonably well against various classes of asymmetric bivariate distributions. Further, it is advocated that the basic idea developed in this work can be easily adopted to test the hypotheses of exchangeability of bivariate random variables and also bivariate symmetry about a given axis which have been considered by several authors in the past.

The exact null distribution of the generalized Hollander–Proschan type test for NBUE alternatives

In this note we derive the exact null distribution for the test statistic γ j ∗ ( F n ) proposed by Anis and Mitra (2011) for testing exponentiality against NBUE alternatives. As a special case, we obtain the exact null distribution for the test statistic K ∗ proposed by Hollander and Proschan (1975). Selected critical values for different sizes are tabulated for these two statistics. Some remarks concerning the benefits of using the exact distribution are made.

A new heteroskedasticity-consistent covariance matrix estimator for the linear regression model

The assumption that all random errors in the linear regression model share the same variance (homoskedasticity) is often violated in practice. The ordinary least squares estimator of the vector of regression parameters remains unbiased, consistent and asymptotically normal under unequal error variances. Many practitioners then choose to base their inferences on such an estimator. The usual practice is to couple it with an asymptotically valid estimation of its covariance matrix, and then carry out hypothesis tests that are valid under heteroskedasticity of unknown form. We use numerical integration methods to compute the exact null distributions of some quasi-t test statistics, and propose a new covariance matrix estimator. The numerical results favor testing inference based on the estimator we propose.

On the optimality of bivariate ranked set sample design for the matched pairs sign test

An optimal alternative bivariate ranked set sample designs for the matched pairs sign test are obtained. Our investigation revealed that the optimal bivariate ranked set sample designs for matched pairs sign test are those with quantifying order statistics with labels {((r+1)/2, (r+1)/2)} when the set size r is odd and {(r/2, r/2), (r/2 + 1, r/2 + 1)} when the set size r is even. The exact null distributions, asymptotic distributions and Pitman efficiencies of those designs are derived. Numerical analysis of the power of the proposed optimal designs is included. An illustration using real data with a bootstrap algorithm for P-value estimation is used.

Testing a covariance matrix: exact null distribution of its likelihood criterion

For X∼ N p (μ, Σ) testing H o:Σ = Σ 0, with Σ 0 known, relies at present on an approximation of the null-distribution of the likelihood ratio statistic. We present here the exact null distribution and also its computation, hence providing a precise tool that can be used in small sample cases.

Some nonparametric precedence-type tests based on progressively censored samples and evaluation of power

In this paper, we consider the problem of testing the equality of two distributions when both samples are progressively Type-II censored. We discuss the following two statistics: one based on the Wilcoxon-type rank-sum precedence test, and the second based on the Kaplan–Meier estimator of the cumulative distribution function. The exact null distributions of these test statistics are derived and are then used to generate critical values and the corresponding exact levels of significance for different combinations of sample sizes and progressive censoring schemes. We also discuss their non-null distributions under Lehmann alternatives. A power study of the proposed tests is carried out under Lehmann alternatives as well as under location-shift alternatives through Monte Carlo simulations. Through this power study, it is shown that the Wilcoxon-type rank-sum precedence test performs the best.

An Optimal Bivariate Ranked Set Sample Design for the Matched Pairs Sign Test

Abstract An optimal alternative bivariate ranked set sample designs for the matched pairs sign test are obtained. The exact null distributions, asymptotic distributions and Pitman efficiencies of those designs are derived. Numerical analysis of the power of the proposed optimal designs is included. Illustration using real data is used.

Exact computation of the null distribution of a test for multiple outliers in an exponential sample

Through a systematic application of the recursion of Huffer [Huffer, F., 1988. Divided differences and the joint distribution of linear combinations of spacings. Journal of Applied Probability 25, 346–354], we present an algorithm for evaluating the exact null distribution of the test statistic proposed by Kimber [Kimber, A.C., 1982. Tests for many outliers in an exponential sample. Applied Statistics 31, 263–271] for the testing of up to k upper outliers for discordancy in exponential samples. This method presents another way of obtaining the exact null distribution of the test statistic without first obtaining their joint density. The advantage of this approach is in its generality and also in its ease of use. In addition, it can provide critical values for the test statistic that are accurate to any required degree of precision. Similar results for the sequential testing of up to k lower outliers for discordancy are also presented.

Exact null distribution for n≤25 and probability approximations for Spearman's score in an absence of ties

Spearman's rank correlation test is a widely used statistical method for testing the correlation between two samples. Unfortunately, the evaluation of the exact distribution of this statistic is difficult and approximation obtained by using the normal distribution is not accurate for moderate sample sizes. In this paper, the critical values for samples 11≤n≤25 are provided. On the basis of the calculated exact values, the accuracy of different approximation methods is discussed. The use of approximation obtained by using the Edgeworth series is recommended in cases when the exact distribution is not available.

Distribution of the Λ-Criterion for Sphericity Test and Its Connection to a Generalized Dirichlet Model

It is shown that the exact null distribution of the likelihood ratio criterion for sphericity test in the p-variate normal case and the marginal distribution of the first component of a (p − 1)-variate generalized Dirichlet model with a given set of parameters are identical. The exact distribution of the likelihood ratio criterion so obtained has a general format for every p. A novel idea is introduced here through which the complicated exact null distribution of the sphericity test criterion in multivariate statistical analysis is converted into an easily tractable marginal density in a generalized Dirichlet model. It provides a direct and easiest method of computation of p-values. The computation of p-values and a table of critical points corresponding to p = 3 and 4 are also presented.

A distribution free goodness of fit test for a stochastically ordered alternative

A distribution free goodness of fit test based on empirical distribution function is proposed for a one-sample and two-sample scenario under the alternative hypothesis of stochastic ordering. The exact and asymptotic null distribution of the proposed test statistic is derived. A Monte Carlo simulation study has been made to compare the power of the proposed test and other competitive test procedures. The proposed test performs better in the comparative study.

A note on the generalization of Rosenbaum's test for two-sample scale problem

Rosenbaum 12 proposed a distribution-free test for the two-sample scale problem based on a number of X-sample observations lying between the extremes of the Y-sample observations. We propose a distribution-free test for the two-sample scale problem; this test is resistant to possible outliers in both samples and generalizes the tests due to Rosenbaum 12 as well as a test due to Shetty and Bhat 7. The performance of the test is evaluated in terms of Pitman's asymptotic relative efficiency. An alternative expression in terms of ranks of ordered X-sample observations in the joint ranking of X and Y-sample observations is given to facilitate the computation of the test statistic and tabulate the upper cutoff points of the exact null distribution of the test statistic.

A New Weighted Rank Correlation

Problem Statement: There have been many cases in real life where two independent sources have ranked n objects, with the interest focused on agreement in the top rankings. Spearman's rho and Kendall's tau coefficients assigned equal weights to all rankings. As a result, the literature proposed several weighted correlation coefficients with emphasis on the top rankings, including the top-down, weighted Kendall's Tau and Blest's correlation coefficient. Approach: This article introduced a new weighted rank correlation coefficient that was sensitive to agreement in the top rankings. It presented the limiting distribution under the null hypothesis of independence and provided a summary of quantiles of the exact null distribution for n = 3(1)9. Results: The article summarized the power comparison between the new weighted coefficient and other weighted coefficients, and showed that the new weighted rank correlation coefficient provided the locally most powerful rank test. Conclusions/Recommendations: The new weighted correlation should be used along with other weighted coefficients when the interest focused on agreement in the top rankings, in order to make an effective inference. © 2008 Science Publications.

Precedence-type tests based on record values

Precedence-type tests based on order statistics are simple and efficient nonparametric tests that are very useful in the context of life-testing, and they have been studied quite extensively in the literature; see Balakrishnan and Ng (Precedence-type tests and applications. Wiley, Hoboken, 2006). In this paper, we consider precedence-type tests based on record values and develop specifically record precedence test, record maximal precedence test and record-rank-sum test. We derive their exact null distributions and tabulate some critical values. Then, under the general Lehmann alternative, we derive the exact power functions of these tests and discuss their power under the location-shift alternative. We also establish that the record precedence test is the uniformly most powerful test for testing against the one-parameter family of Lehmann alternatives. Finally, we discuss the situation when we have insufficient number of records to apply the record precedence test and then make some concluding remarks.

Some simple nonparametric methods to test for perfect ranking in ranked set sampling

A lot of research on ranked set sampling (RSS) is based on the assumption that the ranking is perfect. Hence, it is necessary to develop some tests that could be used to validate this assumption of perfect ranking. In this paper, we introduce some simple nonparametric methods for this purpose. We specifically define three test statistics, N k , S k and A k , based on one-cycle RSS, which are all associated with the ordered ranked set sample (ORSS). We then derive the exact null distributions and exact power functions of all these tests. Next, by using the sum or the maximum of each statistic over all cycles, we propose six test statistics for the case of multi-cycle RSS. We compare the performance of all these tests with that of the Kolmogorov–Smirnov test statistic proposed earlier by Stokes and Sager [1988. Characterization of a ranked-set sample with application to estimating distribution functions. J. Amer. Statist. Assoc. 83, 35–42] and display that all proposed test statistics are more powerful. Finally, we present an example to illustrate the test procedures discussed here.

On the joint distribution of placement statistics under progressive censoring and applications to precedence test

In reliability and survival analysis, comparison of two or more populations is an important problem. For example, while comparing a treatment group with a control group, one may be interested in determining whether the observations in the treatment group have a longer lifetime than those from the control group, that is, whether the treatment is effective or not. In such a study, it would be extremely valuable to make a decision based on early failures. In this paper, we consider independent progressively Type-II censored random samples from two populations with cumulative distribution function's (cdf) F ( · ) and G ( · ) respectively, and discuss a precedence test for testing the equality of the two distributions based on placements. For this purpose, we derive the joint distribution of the first l placement statistics from the progressively censored sample from F ( · ) . We then derive the exact null distribution of the precedence test statistic which is simply the sum of the placements. We provide the rejection regions for fixed levels of significance and various sample sizes and different progressive censoring schemes.