The Interval Property in Multiple Testing of Pairwise Differences

The usual step-down and step-up multiple testing procedures most often lack an important intuitive, practical, and theoretical property called the interval property. In short, the interval property is simply that for an individual hypothesis, among the several to be tested, the acceptance sections of relevant statistics are intervals. Lack of the interval property is a serious shortcoming. This shortcoming is demonstrated for testing various pairwise comparisons in multinomial models, multivariate normal models and in nonparametric models. Residual based stepwise multiple testing procedures that do have the interval property are offered in all these cases.Comment: Published in at http://dx.doi.org/10.1214/11-STS372 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Modelling fish habitat preference with a genetic algorithm-optimized Takagi-Sugeno model based on pairwise comparisons

Species-environment relationships are used for evaluating the current status of target species and the potential impact of natural or anthropogenic changes of their habitat. Recent researches reported that the results are strongly affected by the quality of a data set used. The present study attempted to apply pairwise comparisons to modelling fish habitat preference with Takagi-Sugeno-type fuzzy habitat preference models (FHPMs) optimized by a genetic algorithm (GA). The model was compared with the result obtained from the FHPM optimized based on mean squared error (MSE). Three independent data sets were used for training and testing of these models. The FHPMs based on pairwise comparison produced variable habitat preference curves from 20 different initial conditions in the GA. This could be partially ascribed to the optimization process and the regulations assigned. This case study demonstrates applicability and limitations of pairwise comparison-based optimization in an FHPM. Future research should focus on a more flexible learning process to make a good use of the advantages of pairwise comparisons

Recommendable block sizes: a case study on Finnish official variety trials of barley cultivars

Well-established results in the current statistical literature imply that plant breeders should use incomplete block designs wherever spatial variability exists and the number of treatments is large. But the theoretical position does not indicate the recommendable number of cultivars in an incomplete block. In this study we used data from 28 official variety trials conducted in Finland during the period 2001-2005 to study theffect of block size on the efficiency of testing pairwise yield differences of barley cultivars and cultivar rankings. In previous trials some 6-7 cultivars have usually been included in one block. Our results imply that the efficiency of testing procedures could be improved by using a block size as small as 4-5. The results further imply that if an experiment with an incomplete block design is well planned to mitigate the effects of within-block heterogeneity, the spatial mixed model techniques and the conventional analysis of variance techniques have approximately the same efficiency in testing pairwise yield differences. Thus, if appropriate blocking strategies are used in planning the trials, there is usually no need to change the conventional practice followed in statistical analysis

PWiseHA: Application of Harmony Search Algorithm for Test Suites Generation using Pairwise Techniques

Pairwise testing is an approach that tests every possible combinations of values of parameters. In this approach, number of all combinations are selected to ensure all possible pairs of parameter values are included in the final test suite. Generating test cases is the most active research area in pairwise testing, but the generation process of the efficient test suite with minimum size can be considered as one of optimization problem. In this research paper we articulate the problem of finding a pairwise final test suite as a search problem and the application of harmony search algorithm to solve it. Also, in this research paper, we developed a pairwise software testing tool called PWiseHA that will generate test cases using harmony search algorithm and this PWiseHA is well optimized. Finally, the result obtained from PWiseHA shows a competitive results if matched with the result of existing pairwise testing tools. PWiseHA is still in prototype form, an obvious starting point for future work

An Enhanced Pairwise Search Approach for Generating Optimum Number of Test Data and Reduce Execution Time

In recent days testing considers the most important task for building software that is free from error. Since the resources and time is limited to produce software, hence, it is not possible of performing exhaustive tests (i.e. to test all possible combinations of input data.) An alternative to get ride from this type exhaustive numbers and as well to reduce cost, an approach called Pairwise (2 way) test data generation approach will be effective. Most of the software faults in pairwise approach caused by unusual combination of input data.  Hence, the demand for the optimization of number of generated test-cases and reducing the execution time is growing in software industries. This paper proposes an enhancement in pairwise search approach which generates optimum number of input values for testing purposes.  In this approach it searches the most coverable pairs by pairing parameters and adopts one-test-at-a-time strategy for constructing a final test-suite.  Compared to other existing strategies, Our proposed approach is effective in terms of number of generated test cases and of execution time. Keywords:, Software testing, Pairwise testing, Combinatorial interaction testing, Test case generation

Convex hierarchical testing of interactions

We consider the testing of all pairwise interactions in a two-class problem with many features. We devise a hierarchical testing framework that considers an interaction only when one or more of its constituent features has a nonzero main effect. The test is based on a convex optimization framework that seamlessly considers main effects and interactions together. We show - both in simulation and on a genomic data set from the SAPPHIRe study - a potential gain in power and interpretability over a standard (nonhierarchical) interaction test.Comment: Published at http://dx.doi.org/10.1214/14-AOAS758 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Pairwise Tests of Purchasing Power Parity Using Aggregate and Disaggregate Price Measures

In this paper we adopt a new approach to testing for purchasing power parity, PPP, that is robust to base country effects, cross-section dependence, and aggregation. We test for PPP applying a pairwise approach to the disaggregated data set recently analysed by Imbs, Mumtaz, Ravan and Rey (2005, QJE). We consider a variety of tests applied to all 66 possible pairs of real exchange rate among the 12 countries and estimate the proportion of the pairs that are stationary, for the aggregates and each of the 19 commodity groups. To deal with small sample problems, we use a factor augmented sieve bootstrap approach and present bootstrap pairwise estimates of the proportions that are stationary. The bootstrapped rejection frequencies at 26%-49% based on unit root tests suggest some evidence in favour of the PPP in the case of the disaggregate data as compared to 6%-14% based on aggregate price series