87,263 research outputs found
Permutation testing for goodness of fit and stochastic ordering
No full textProblems of testing for ordered categorical variables are of great interest in
many application disciplines, where a finite number of Q 1 of such variables are observed
on each individual unit (Pesarin and Salmaso (2006) (Pesarin and Salmaso, 2010a) and
(Pesarin and Salmaso, 2010b)). In particular, Goodness of Fit tests are used to measuring
how well do the observed data correspond to the assumption model. Several parametric
solutions to univariate case have been proposed in literature. In particular, when dealing
with categorical variables, the most used methods are Pearson’s Chi-squared and Deviance
statistic. However, these methods, usually based on the restricted maximum likelihood ratio
test, are generally criticized because their asymptotic null and alternative distributions are
mixtures of chi-squared variables whose weights essentially depend on underlying population
distribution F and so the related degree of accuracy is difficult to assess and to characterize;
thus their use when F is unknown is somewhat questionable in practice. Moreover, is well
known the difficulty or impossibility to use them in multivariate cases. In many situations
it can be of interest testing for a set of restricted alternatives to H0 (Kim and Foutz (1997)
and Chapman (1958)). In these cases we can refer to Stochastic Ordering. Parametric
solutions don’t allow this kind of tests. By working within the Non-parametric combination
of dependent permutation tests, it is possible to find exact solutions to these problems.
The NPC approach works as a general methodology for most multivariate situations, as for
instance in cases where sample sizes are smaller than the number of observed variables, or
where there are non-ignorable missing values, or when some of the variables are categorical
(ordered and nominal) and others are quantitative and in many other complex situations.
In this work, NPC tests for stochastic dominance are presented, both for two sample
directional testing and for testing for a stochastic ordering in a multivariate setting. A
simulation study is reported to show the NPC approach efficacy
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,
Repeated Measures Designs: a Permutation Approach for Testing for Active Effects
No full textIn this paper we present some extensions of the synchronized permutation solution for testing for active effects in two-way ANOVA designs (F. Pesarin [Multivariate Permutation Tests with Applications in Biostatistics, John Wiley, Chichester, 2001] and L. Salmaso [Comm. Statist. Theory Methods 32 (2003), 1419-1437]). Such solutions are related to balanced and unbalanced repeated measures designs based on I*J ANOVA designs. Furthermore a simulation study to evaluate the performance of partial tests for testing for effects in a 3*3 design is also presented
A Comparison of Efficient Permutation Tests for Unbalanced Anova in Two by Two Designs and Their Behavior Under Heteroscedasticity
No full textWe outlined various procedures that aim to compensate shortcomings of classical
ANOVA. Some procedures are only valid under normality and possibly
heteroscedastic variances (ATS). CSP and USP are valid under non-normally
distributed error terms and homoscedastic variances. Both the WTS and WTPS are
asymptotically valid even under non-normality and heteroscedasticity, respectively.
Most of these procedures are intended to be used for small samples (ATS, WTPS,
CSP, and USP), only the WTS requires a sufficiently large sample size.
In the following simulation we vary additionally the aspect of balanced vs.
unbalanced designs, as heteroscedasticity is especially problematic in the latter one
Finite-sample consistency of combination-based permutation tests with application to repeated measures designs
No full textIn several application fields, e.g. genetics, image and functional analysis, several biomedical and social experimental and observational studies, etc. it may happen that the number of observed variables is much larger than that of subjects. It can be proved that, for a given and fixed number of subjects, when the number of variables increases and the noncentrality parameter of the underlying population distribution increases with respect to each added variable, then power of multivariate permutation tests based on Pesarin's combining functions [Pesarin, F. (2001), Multivariate Permutation Tests with Applications in Biostatistics, New York: Wiley, Chichester] is monotonically increasing. These results confirm and extend those presented by [Blair, Higgins, Karniski and Kromrey (1994), 'A Study of Multivariate Permutation Tests which May Replace Hotelling's T2 Test in Prescribed Circumstances', Multivariate Behavioral Research 29, 141-163]. Moreover, they allow us to introduce the property of finite-sample consistency for those kinds of combination-based permutation tests. Sufficient conditions are given in order that the rejection rate converges to one, for fixed sample sizes at any attainable -values, when the number of variables diverges. A simulation study and a real case study are presented
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
Targeted Cyclodextrins.
No full textSince 1970s cyclodextrins (CDs) and their derivatives, in virtue of their peculiar inclusion properties, have been proposed as pharmaceutical excipients and have found application in several marketed products. They are deemed unique products since natural and semi-synthetic CDs can modulate the physical, chemical and biopharmaceutical properties of guest molecules, eventually ameliorating critical drug properties such as water solubility, dissolution rate, stability and bioavailability. In addition, cyclodextrins have been studied as functional excipients in controlled release system. The advances in chemical strategies for CDs derivatization along with the exciting results obtained with site selective colloidal drug carriers, namely liposomes and nanoparticles, has brought to development of targeted cyclodextrins. In latest years, cyclodextrins have been regarded not only as simple functional excipients but rather as multifunctional supramolecular carriers. Two requirements have to fulfil to obtain such targeted CDs: 1. conjugation of targeting moieties on the CD scaffold, without impairing their specific recognition of cellular targets: 2. adequate complexation of the guest molecule that have to be efficiently transported to the specific disease site.
In this chapter, a critical overview of some of the most interesting approaches aimed at develop targeted cyclodextrins is presented. Particular emphasis will be dedicated to tumor targeting, a field where selective treatments are strongly required and expectations from targeted therapies are extremely high. The chapter reviews also a number of other modified cyclodextrins developed as molecular carriers designed to attain tissues, organs and body districts where a more specific and efficient treatment is required.
Taking into account the latest evolution of semi-synthetic cyclodextrins, the applications of targeted cyclodextrins will increase in the near future. The new opportunities offered by biotechnology, both in terms of biological targets and potent but fragile bioactive molecules, are expected to sustain the evolution in the pharmaceutical field with targeted delivery systems playing a pivotal role
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