170,568 research outputs found
A review on combination-based tests for shape analysis
No full textThis paper reviews main features of the combination-based approach for shape analysis.
It is worth noting that inference in shape analysis is a crucial point and robust testing
is much needed. Our nonparametric permutation-based approach provides a suitable and
powerful testing also in presence of many correlated landmarks and a limited sample size.
An important feature of combination-based tests is the finite sample consistency property
(Pesarin and Salmaso, 2010 and Brombin and Salmaso, 2013) which allows to gain power
in the testing procedure by increasing the number of variables rather than the sample size,
provided that added variables yield additional information
Employment status and education/employment relationship of PhD graduates from the University of Ferrara
No full textTwo sample surveys of Post-Docs were planned and carried out at the University of Ferrara in 2004 and 2007 aimed at determining the professional status of Post-Docs, the relationship between their PhD education and employment, and their satisfaction with certain aspects of the education and research program. As part of these surveys, two methodological contributions were developed. The first concerns an extension of the non-parametric combination of dependent rankings to construct a synthesis of composite indicators measuring satisfaction with particular aspects of PhD programs [R. Arboretti Giancristofaro and L. Salmaso, Global ranking indicators with application to the evaluation of PhD programs, Atti del Convegno “Valutazione e Customer Satisfaction per la Qualita dei Servizi”, Roma, 8-9 Settembre 2005, pp. 19-22; R. Arboretti Giancristofaro, S. Bonnini, and L. Salmaso, A performance indicator for multivariate data, Quad. Stat. 9 (2007), pp. 1-29; R. Arboretti Giancristofaro, F. Pesarin, and L. Salmaso, Nonparametric approaches for multivariate testing with mixed variables and for ranking on ordered categorical variables with an application to the evaluation of PhD programs, in Real Data Analysis, S. Sawilowsky, ed., a volume in Quantitative Methods in Education and the Behavioral Sciences: Issues, Research and Teaching, Ronald C. Serlin, series ed., Information Age Publishing, Charlotte, North Carolina, 2007, pp. 355-385]. The procedure was applied to highlight differences in the interviewed Post-Docs' multivariate satisfaction profiles in relation to two aspects: education/employment relationship; employment expectations; and opportunities. The second consists of an inferential procedure providing a solution to the problem of hypothesis testing, where the objective is to compare the heterogeneity of two populations on the basis of sampling data [G.R. Arboretti, S. Bonnini, and F. Pesarin, A permutation approach for testing heterogeneity in two-sample categorical variables, Stat. Comput. (2009) doi: 10.1007/S11222-008-9085-8.]. The procedure was applied to compare the degrees of heterogeneity of Post-Doc judgments in the two surveys with regard to the adequacy of the PhD education for the work carried out.employment survey, performance indicators, heterogeneity tests,
Multi-Aspect Procedures for Paired Data with Application to Biometric Morphing
No full textAs is common in case-control studies, treatments have an influence not only on mean values, but also on variance or distributional aspects. That is why several statistics, each one suitable for a specific aspect, are usually assessed (Salmaso and Solari, 2005). According to Farkas (1947, p. 185), different tests of significance are appropriate to test different features of the same null hypothesis (Lehmann, 1993), thus leading to the Multi-Aspect (MA) testing issue (Pesarin and Salmaso, 2010). When dealing with paired data, usually inferences concern differences between the means. However, there are some circumstances in which it is of interest to test for differences between the variances (McCulloch, 1987). Here, we present a nonparametric permutation solution to this problem. Our goal is to develop MA techniques for paired data, thus finding powerful tests, such that both differences in mean and in variance are separately identified. The inferential procedures proposed in the paper and assessed throughout a simulation study are then applied to a real case study in rhinoseptoplasty surgery
Robust non-parametric tests for complex-repeated measures problems in ophthalmology
No full textThe NonParametric Combination methodology (NPC) of dependent permutation tests allows the
experimenter to face many complex multivariate testing problems and represents a convincing and
powerful alternative to standard parametric methods. The main advantage of this approach lies in its
flexibility in handling any type of variable (categorical and quantitative, with or without missing values)
while at the same time taking dependencies among those variables into account without the need of
modelling them. NPC methodology enables to deal with repeated measures, paired data, restricted
alternative hypotheses, missing data (completely at random or not), high-dimensional and small sample
size data. Hence, NPC methodology can offer a significant contribution to successful research in
biomedical studies with several endpoints, since it provides reasonably efficient solutions and clear
interpretations of inferential results. Pesarin F. Multivariate permutation tests: with application in
biostatistics. Chichester-New York: John Wiley &Sons, 2001; Pesarin F, Salmaso L. Permutation tests for
complex data: theory, applications and software. Chichester, UK: John Wiley &Sons, 2010. We focus on nonparametric
permutation solutions to two real-case studies in ophthalmology, concerning complexrepeated
measures problems. For each data set, different analyses are presented, thus highlighting
characteristic aspects of the data structure itself. Our goal is to present different solutions to
multivariate complex case studies, guiding researchers/readers to choose, from various possible
interpretations of a problem, the one that has the highest flexibility and statistical power under a set
of less stringent assumptions. MATLAB code has been implemented to carry out the analyses
Multi-aspect permutation tests in shape analysis with small sample size
No full textInferential methods known in the shape analysis literature make use of configurations of landmarks optimally superimposed using a least-squares procedure or analyze matrices of interlandmark distances. For example, in the two independent sample case, a practical method for comparing the mean shapes in the two groups is to use the Procrustes tangent space coordinates, if data are concentrated, calculate the Mahalanobis distance and then the Hotelling T(2)-test statistic. Under the assumption of isotropy, another simple approach is to work with statistics based on the squared Procrustes distance and then consider the Goodall F-test statistic. Despite their widespread use, on the one hand it is well known that Hotelling's T(2)-test may not be very powerful unless there are a large number of observations available, and on the other hand the underlying model required by Goodall's F-test is very restrictive. For these reasons, an extension of the nonparametric combination (NPC) methodology to shape analysis is proposed. Focussing on the two independent sample case, through a comparative simulation study and an application to the Mediterranean monk seal skulls clataset, the behaviour of some nonparametric permutation tests has been evaluated, showing that the proposed tests are very powerful, for both balanced and unbalanced sample sizes. (C) 2009 Elsevier B.V. All rights reserved
Permutation testing for thick data when the number of variables is much greater than the sample size: recent developments and some recommendations
No full textIn many scientific disciplines datasets contain many more variables than observational units (so-called thick data). A common hypothesis of interest in this setting is the global null hypothesis of no difference in multivariate distribution between different experimental or observational groups. Several permutation-based nonparametric tests have been proposed for this hypothesis. In this paper we investigate the potential differences in performance between different methods used to test thick data. In particular we focus on an extension of the Nonparametric combination procedure (NPC) proposed by Pesarin and Salmaso, a rank-based approach by Ellis, Burchett, Harrar and Bathke, and a distance-based approach by Mielke. The effect of different combining procedures on the NPC is also explored. Finally, we illustrate the use of these methods on a real-life dataset
Functional classifications in phytoplankton ecology: a comparative review of approaches and experiences
No full textEmpirical models of phytoplankton groups and their recurrence in water bodies have traditionally made use of taxonomic classifications, implicitly or explicitly assuming that species classified together could share similar ecological properties. Nevertheless, the use of taxonomy in ecology has many drawbacks. From one side, many broader groups include species with very different ecological properties. From the other side, convergent evolution, the independent evolution of similar characters in different lineages, can explain why distantly phylogenetically related species can be linked together by close analogous ecological affinities. With the aim to obtain a better understanding of the functioning of the freshwater ecosystems, complementary approaches based on ecological criteria have been therefore proposed. The aim of this contribution is to critically review the rationale of the different classifications that have been proposed during the last three decades, highlighting the strengths and weaknesses of the different approaches. Besides the first classifications, which considered broad functional categories based on reproductive (r-K selection) and life strategies (C-S-R), successive formulations included the functional groups (FG), firstly established by C.S. Reynolds, the Morpho-Functional Groups (MFG- Salmaso & Padisák, 2007), and the Morphology-Based Functional Groups (MBFG - Kruk et al., 2010). In the original formulation of FG, species were put together if they showed similar dynamics and ecological requirements, implicitly assuming a similar response to a set of environmental and seasonal changing conditions. With successive refinements, morphological properties have been used to fit hitherto functionally unclassified taxa into existing FG. This classification has been widely used in many aquatic ecosystems, with applications in ecological status assessment. At the opposite, MBFG (totalling 7 groups) are exclusively based on morphological characters, irrespective of the temporal dynamics of the species. The MFG concept use a hybrid approach, integrating morphological, functional and, when ecologically relevant, taxonomic characters in the definition of groups. The comparative evaluation of the above classifications was attempted only very recently, and will be critically examined in this review. Finally, this work will provide an updating of the original MFG classification based on the application of the concept to real case phytoplankton studies
Permutation tests in shape analysis
No full textStatistical shape analysis is a geometrical analysis from a set of shapes in which statistics are measured to describe geometrical properties from similar shapes or different groups, for instance, the difference between male and female Gorilla skull shapes, normal and pathological bone shapes, etc. Some of the important aspects of shape analysis are to obtain a measure of distance between shapes, to estimate average shapes from a (possibly random) sample and to estimate shape variability in a sample[1]. One of the main methods used is principal component analysis. Specific applications of shape analysis may be found in archaeology, architecture, biology, geography, geology, agriculture, genetics, medical imaging, security applications such as face recognition, entertainment industry (movies, games), computer-aided design and manufacturing. This is a proposal for a new Brief on statistical shape analysis and the various new parametric and non-parametric methods utilized to facilitate shape analysis
Multivariate permutation shape analysis with application to aortic valve morphology. Stereology and Image Analysis
No full text- …
