Screening for partial conjunction hypotheses

SUMMARY: We consider the problem of testing for partial conjunction of hypothesis, which argues that at least u out of n tested hypotheses are false. It offers an in-between approach to the testing of the conjunction of null hypotheses against the alternative that at least one is not, and the testing of the disjunction of null hypotheses against the alternative that all hypotheses are not null. We suggest powerful test statistics for testing such a partial conjunction hypothesis that are valid under dependence between the test statistics as well as under independence. We then address the problem of testing many partial conjunction hypotheses simultaneously using the false discovery rate (FDR) approach. We prove that if the FDR controlling procedure in Benjamini and Hochberg (1995, Journal of the Royal Statistical Society, Series B 57, 289-300) is used for this purpose the FDR is controlled under various dependency structures. Moreover, we can screen at all levels simultaneously in order to display the findings on a superimposed map and still control an appropriate FDR measure. We apply the method to examples from microarray analysis and functional magnetic resonance imaging (fMRI), two application areas where the need for partial conjunction analysis has been identified.     

A Modified Chi-Square Test for Testing Equality of Two Multinomial Populations Against an Ordering Restricted Alternative

A modified chi-square test for testing the equality of two multinomial populations against an ordering restricted alternative in one sample and two sample cases is constructed. The relation between a concept of dependence called dependence by chi-square and stochastic ordering is established. A tabulation of the asymptotic distribution of the test statistic under the null hypothesis is given. Simulations are used to compare the power of this test with the power of the likelihood ratio test of stochastic ordering of the two multinomial populations.     

Tests of Bivariate Symmetry with a One-sided Alternative in the Analysis of Variance

We consider a two-factor experiment in which the factors have the same number of levels with a natural ordering among levels. We test the hypothesis that the effects of the two treatments are symmetric against a one-sided alternative using the likelihood ratio criteria. Test of the one-sided alternative as a null hypothesis against no restriction has also been studied. Exact distribution theory under the null hypothesis is derived and is shown to be a weighted mixture of chi-square distributions. An example is used to illustrate the procedure.     

Partitioning species diversity across landscapes and regions: a hierarchical analysis of alpha, beta, and gamma diversity

Species diversity may be additively partitioned within and among samples (alpha and beta diversity) from hierarchically scaled studies to assess the proportion of the total diversity (gamma) found in different habitats, landscapes, or regions. We developed a statistical approach for testing null hypotheses that observed partitions of species richness or diversity indices differed from those expected by chance, and we illustrate these tests using data from a hierarchical study of forest-canopy beetles. Two null hypotheses were implemented using individual- and sample-based randomization tests to generate null distributions for alpha and beta components of diversity at multiple sampling scales. The two tests differed in their null distributions and power to detect statistically significant diversity components. Individual-based randomization was more powerful at all hierarchical levels and was sensitive to departures between observed and null partitions due to intraspecific aggregation of individuals. Sample-based randomization had less power but still may be useful for determining whether different habitats show a higher degree of differentiation in species diversity compared with random samples from the landscape. Null hypothesis tests provide a basis for inferences on partitions of species richness or diversity indices at multiple sampling levels, thereby increasing our understanding of how alpha and beta diversity change across spatial scales.     

Optimal Testing for Serial Correlation in a Large Number of Small Samples

In recent years, there has been an increased awareness of the potential one-sided nature of many testing problems in applied sciences. Usually, these testing problems can be reduced, either by conditioning on sufficient statistics or by invariant techniques. COX and SOLOMON (1988) considered testing the serial correlation coefficient of a stationary first order autoregressive process and concentrated on four independent samples, with each of size three. We outline a general method for testing the serial correlation coefficient, using locally best invariant, point optimal invariant and locally most mean powerful invariant test procedures. The first procedure optimizes power near the null hypothesis, the second optimizes it at a pre-determined point away from the null while the third optimizes the average curvature of the power hypersurface in the neighbourhood of the null hypothesis.     

A likelihood-based method for testing for nonstochastic variation of diversification rates in phylogenies

Observed variations in rates of taxonomic diversification have been attributed to a range of factors including biological innovations, ecosystem restructuring, and environmental changes. Before inferring causality of any particular factor, however, it is critical to demonstrate that the observed variation in diversity is significantly greater than that expected from natural stochastic processes. Relative tests that assess whether observed asymmetry in species richness between sister taxa in monophyletic pairs is greater than would be expected under a symmetric model have been used widely in studies of rate heterogeneity and are particularly useful for groups in which paleontological data are problematic. Although one such test introduced by Slowinski and Guyer a decade ago has been applied to a wide range of clades and evolutionary questions, the statistical behavior of the test has not been examined extensively, particularly when used with Fisher's procedure for combining probabilities to analyze data from multiple independent taxon pairs. Here, certain pragmatic difficulties with the Slowinski-Guyer test are described, further details of the development of a recently introduced likelihood-based relative rates test are presented, and standard simulation procedures are used to assess the behavior of the two tests in a range of situations to determine: (1) the accuracy of the tests' nominal Type I error rate; (2) the statistical power of the tests; (3) the sensitivity of the tests to inclusion of taxon pairs with few species; (4) the behavior of the tests with datasets comprised of few taxon pairs; and (5) the sensitivity of the tests to certain violations of the null model assumptions. Our results indicate that in most biologically plausible scenarios, the likelihood-based test has superior statistical properties in terms of both Type I error rate and power, and we found no scenario in which the Slowinski-Guyer test was distinctly superior, although the degree of the discrepancy varies among the different scenarios. The Slowinski-Guyer test tends to be much more conservative (i.e., very disinclined to reject the null hypothesis) in datasets with many small pairs. In most situations, the performance of both the likelihood-based test and particularly the Slowinski-Guyer test improve when pairs with few species are excluded from the computation, although this is balanced against a decline in the tests' power and accuracy as fewer pairs are included in the dataset. The performance of both tests is quite poor when they are applied to datasets in which the taxon sizes do not conform to the distribution implied by the usual null model. Thus, results of analyses of taxonomic rate heterogeneity using the Slowinski-Guyer test can be misleading because the test's ability to reject the null hypothesis (equal rates) when true is often inaccurate and its ability to reject the null hypothesis when the alternative (unequal rates) is true is poor, particularly when small taxon pairs are included. Although not always perfect, the likelihood-based test provides a more accurate and powerful alternative as a relative rates test.     

Smooth tests for the zero-inflated poisson distribution

In this article we construct three smooth goodness-of-fit tests for testing for the zero-inflated Poisson (ZIP) distribution against general smooth alternatives in the sense of Neyman. We apply our tests to a data set previously claimed to be ZIP distributed, and show that the ZIP is not a good model to describe the data. At rejection of the null hypothesis of ZIP, the individual components of the test statistic, which are directly related to interpretable parameters in a smooth model, may be used to gain insight into an alternative distribution.     

Sib-parentage testing using molecular markers when parents are unknown

The formulae for computing the so-called Sib Index using codominant alleles for (1) full-sib and (2) half-sib parentage are given. Hypothesis testing is based on the distribution of conditional likelihood ratio or Bayes' factor. Thresholds for rejecting the null hypothesis and P-values were obtained in function of the number of alleles and their frequency distributions. Simulations showed that a relatively low number of marker systems (e.g. 20) are enough to accept the hypothesis of sib parentage with a reasonable power for usual significance levels, but that a higher number would be necessary if full-sib against half-sib parentage is the contrast to be carried out. The effect of sampling variation on the allele frequencies on power calculations is also analysed.     

生态学假说试验验证的原假说困境

试验方法是生态学假说的主要验证方法之一,但也存在由原假说引发的质疑.Quinn和Dunham(1983)通过对Platt(1964)的假说-演绎模型进行分析,主张生态学不可能存在可以严格被试验验证的原假说.Fisher的证伪主义与Neyman-Pearson(N-P)的非判决性使得统计学原假说不能被严格验证;而生态过程中存在的不同于经典物理学的原假说H₀(α=1,β=0)与不同的备假说H₁′(α′=1,β′=0)的情况,使得生态学原假说也很难得到严格的实验验证.通过降低P值、谨慎选择原假说、对非原假说采取非中心化和双侧验证可分别缓解上述的原假说困境.但统计学的原假说显著性验证(NHST)不应等同于生态学假说中有关因果关系的逻辑证明方法.因此,现有大量基于NHST的生态学假说的方法研究和试验验证的结果与结论都不是绝对的逻辑可靠的.     

A linear rank test for use when the main interest is in differences in cure rates

For diseases with a positive probability of being cured, a family of alternatives to the null hypothesis of equality of survival distributions is introduced, which is designed to focus power against alternatives with differences in cure rates. The optimal linear rank test for this alternative is derived, and found to be substantially more efficient than the log-rank test for this alternative when cure rates are less than 50%, while there is little difference between the tests if the cure rates are 50% or greater. The simple test based on the difference of Kaplan-Meier estimates of the proportion cured is also examined, and found to be fully efficient for this alternative with no censoring, while its efficiency rapidly drops as censoring is increased. The new test is not a pure test of equality of cure rates when the data are censored, but rather is a test of equality of survival distributions that focuses power against late differences in the survival curves.     

Optimal screening for promising genes in 2-stage designs

Detecting genetic markers with biologically relevant effects remains a challenge due to multiple testing. Standard analysis methods focus on evidence against the null and protect primarily the type I error. On the other hand, the worthwhile alternative is specified for power calculations at the design stage. The balanced test as proposed by Moerkerke and others (2006) and Moerkerke and Goetghebeur (2006) incorporates this alternative directly in the decision criterion to achieve better power. Genetic markers are selected and ranked in order of the balance of evidence they contain against the null and the target alternative. In this paper, we build on this guiding principle to develop 2-stage designs for screening genetic markers when the cost of measurements is high. For a given marker, a first sample may already provide sufficient evidence for or against the alternative. If not, more data are gathered at the second stage which is then followed by a binary decision based on all available data. By optimizing parameters which determine the decision process over the 2 stages (such as the area of the "gray" zone which leads to the gathering of extra data), the expected cost per marker can be reduced substantially. We also demonstrate that, compared to 1-stage designs, 2-stage designs achieve a better balance between true negatives and positives for the same cost.     

TRAB: testing whether mutation frequencies are above an unknown background

To rigorously determine whether a gene or a set of genes have alterations that are involved in carcinogenesis requires a comparison of the prevalence of identified changes to a control mutation frequency present in tumor DNA. To facilitate this task, we develop a testing approach and the associated R library, called TRAB, that evaluates whether the frequency of somatic mutation in a given gene is higher than that observed in a control group of genes. Specifically, we test the null hypothesis that the frequency belongs to a control population of frequencies, against the alternative hypothesis that the frequency is higher. Mutation frequencies in the control group are themselves allowed to be variable. TRAB computes the a posteriori probability and the Bayes factor for the hypothesis using a hierarchical Bayesian approach.     

Statistical methods to decide whether mutagenicity test data from Drosophila assays indicate a positive, negative, or inconclusive result

Two alternative hypotheses are used to distinguish among the possibilities of a positive, inconclusive, or negative result in Drosophila mutagenicity tests. In the null hypothesis one assumes that there is no difference in the mutation frequency between control and treated series. The alternative hypothesis postulates a priori that the treatment results in an increased mutation frequency that is m times the spontaneous frequency. To test against the hypotheses, the conditional binomial test according to Kastenbaum and Bowman or the chi 2 test for proportions may be applied. These 2 methods are in principle equivalent. An alternative method which is based on determining confidence limits of observed mutation frequencies also leads to the same conclusions. The practical calculations are formulated and an application is shown with a test example demonstrating the genotoxicity of the pyrrolizidine alkaloid 7-acetylintermedine in the somatic wing mosaic test. In the Appendix, the calculus for the 3 testing methods is explained with a numerical example.     

The mid-domain effect cannot explain the diversity gradient of Nearctic birds

A recent explanation for diversity gradients proposes a ‘null model’ based on how species ranges are constrained by the geometry of bounded domains. We conduct a test of this hypothesis by comparing patterns predicted by two two‐dimensional geometric models against observed diversity patterns for terrestrially feeding Nearctic birds. Consistent with previous tests in two‐dimensional space, we find empirical support for the hypothesis to be very weak. We also point out a fundamental conceptual flaw in the hypothesis with respect to the key assumption that ranges can exist independently of the environment in which they are embedded that undermines its theoretical basis as well. We conclude that the mid‐domain effect has little empirical support and no theoretical support for its existence, and recommend that it be eliminated as a potential explanation for diversity gradients.     

Empirical Relationships between Species Richness, Evenness, and Proportional Diversity

Diversity (or biodiversity) is typically measured by a species count (richness) and sometimes with an evenness index; it may also be measured by a proportional statistic that combines both measures (e.g., Shannon-Weiner index or H'). These diversity measures are hypothesized to be positively and strongly correlated, but this null hypothesis has not been tested empirically. We used the results of Caswell's neutral model to generate null relationships between richness (S), evenness (J'), and proportional diversity (H'). We tested predictions of the null model against empirical relationships describing data in a literature survey and in four individual studies conducted across various scales. Empirical relationships between log S or J' and H' differed from the null model when <10 species were tested and in plants, vertebrates, and fungi. The empirical relationships were similar to the null model when >10 and <100 species were tested and in invertebrates. If >100 species were used to estimate diversity, the relation between log S and H' was negative. The strongest predictive models included log S and J'. A path analysis indicated that log S and J' were always negatively related, that empirical observations could not be explained without including indirect effects, and that differences between the partials may indicate ecological effects, which suggests that S and J' act like diversity components or that diversity should be measured using a compound statistic.     

On the power of some binomial modifications of the Bonferroni multiple test

Widely used in testing statistical hypotheses, the Bonferroni multiple test has a rather low power that entails a high risk to accept falsely the overall null hypothesis and therefore to not detect really existing effects. We suggest that when the partial test statistics are statistically independent, it is possible to reduce this risk by using binomial modifications of the Bonferroni test. Instead of rejecting the null hypothesis when at least one of n partial null hypotheses is rejected at a very high level of significance (say, 0.005 in the case of n = 10), as it is prescribed by the Bonferroni test, the binomial tests recommend to reject the null hypothesis when at least k partial null hypotheses (say, k = [n/2]) are rejected at much lower level (up to 30-50%). We show that the power of such binomial tests is essentially higher as compared with the power of the original Bonferroni and some modified Bonferroni tests. In addition, such an approach allows us to combine tests for which the results are known only for a fixed significance level. The paper contains tables and a computer program which allow to determine (retrieve from a table or to compute) the necessary binomial test parameters, i.e. either the partial significance level (when k is fixed) or the value of k (when the partial significance level is fixed).     

Statistical power when testing for genetic differentiation

A variety of statistical procedures are commonly employed when testing for genetic differentiation. In a typical situation two or more samples of individuals have been genotyped at several gene loci by molecular or biochemical means, and in a first step a statistical test for allele frequency homogeneity is performed at each locus separately, using, e.g. the contingency chi-square test, Fisher's exact test, or some modification thereof. In a second step the results from the separate tests are combined for evaluation of the joint null hypothesis that there is no allele frequency difference at any locus, corresponding to the important case where the samples would be regarded as drawn from the same statistical and, hence, biological population. Presently, there are two conceptually different strategies in use for testing the joint null hypothesis of no difference at any locus. One approach is based on the summation of chi-square statistics over loci. Another method is employed by investigators applying the Bonferroni technique (adjusting the P-value required for rejection to account for the elevated alpha errors when performing multiple tests simultaneously) to test if the heterogeneity observed at any particular locus can be regarded significant when considered separately. Under this approach the joint null hypothesis is rejected if one or more of the component single locus tests is considered significant under the Bonferroni criterion. We used computer simulations to evaluate the statistical power and realized alpha errors of these strategies when evaluating the joint hypothesis after scoring multiple loci. We find that the 'extended' Bonferroni approach generally is associated with low statistical power and should not be applied in the current setting. Further, and contrary to what might be expected, we find that 'exact' tests typically behave poorly when combined in existing procedures for joint hypothesis testing. Thus, while exact tests are generally to be preferred over approximate ones when testing each particular locus, approximate tests such as the traditional chi-square seem preferable when addressing the joint hypothesis.     

Using phylogeographic analyses of gene trees to test species status and processes

A gene tree is an evolutionary reconstruction of the genealogical history of the genetic variation found in a sample of homologous genes or DNA regions that have experienced little or no recombination. Gene trees have the potential of straddling the interface between intra- and interspecific evolution. It is precisely at this interface that the process of speciation occurs, and gene trees can therefore be used as a powerful tool to probe this interface. One application is to infer species status. The cohesion species is defined as an evolutionary lineage or set of lineages with genetic exchangeability and/or ecological interchangeability. This species concept can be phrased in terms of null hypotheses that can be tested rigorously and objectively by using gene trees. First, an overlay of geography upon the gene tree is used to test the null hypothesis that the sample is from a single evolutionary lineage. This phase of testing can indicate that the sampled organisms are indeed from a single lineage and therefore a single cohesion species. In other cases, this null hypothesis is not rejected due to a lack of power or inadequate sampling. Alternatively, this null hypothesis can be rejected because two or more lineages are in the sample. The test can identify lineages even when hybridization and lineage sorting occur. Only when this null hypothesis is rejected is there the potential for more than one cohesion species. Although all cohesion species are evolutionary lineages, not all evolutionary lineages are cohesion species. Therefore, if the first null hypothesis is rejected, a second null hypothesis is tested that all lineages are genetically exchangeable and/or ecologically interchangeable. This second test is accomplished by direct contrasts of previously identified lineages or by overlaying reproductive and/or ecological data upon the gene tree and testing for significant transitions that are concordant with the previously identified lineages. Only when this second null hypothesis is rejected is a lineage elevated to the status of cohesion species. By using gene trees in this manner, species can be identified with objective, a priori criteria with an inference procedure that automatically yields much insight into the process of speciation. When one or more of the null hypotheses cannot be rejected, this procedure also provides specific guidance for future work that will be needed to judge species status.     

Evaluation of Noether's Method of Sample Size Determination for the Wilcoxon-Mann-Whitney Test

NOETHER (1987) proposed a method of sample size determination for the Wilcoxon-Mann-Whitney test. To obtain a sample size formula, he restricted himself to alternatives that differ only slightly from the null hypothesis, so that the unknown variance o² of the Mann-Whitney statistic can be approximated by the known variance under the null hypothesis which depends only on n. This fact is frequently forgotten in statistical practice. In this paper, we compare Noether's large sample solution against an alternative approach based on upper bounds of σ² which is valid for any alternatives. This comparison shows that Noether's approximation is sufficiently reliable with small and large deviations from the null hypothesis.