Multivariate small sample tests for two-way designs with applications to industrial statistics

In this paper, we present a novel nonparametric approach for multivariate analysis of two-way crossed factorial design based on NonParametric Combination applied to Synchronized Permutation tests. This nonparametric hypothesis testing procedure not only allows to overcome the shortcomings of MANOVA test like violation of assumptions such as multivariate normality or covariance homogeneity, but, in an extensive simulation study, reveals to be a powerful instrument both in case of small sample size and many response variables. We contextualize its application in the field of industrial experiments and we assume a linear additive model for the data set analysis. Indeed, the linear additive model interpretation well adapts to the industrial production environment because of the way control of production machineries is implemented. The case of small sample size reflects the frequent needs of practitioners in the industrial environment where there are constraints or limited resources for the experimental design. Furthermore, an increase in rejection rate can be observed under alternative hypothesis when the number of response variables increases with fixed number of observed units. This could lead to a strategical benefit considering that in many real problems it could be easier to collect more information on a single experimental unit than adding a new unit to the experimental design. An application to industrial thermoforming processes is useful to illustrate and highlight the benefits of the adoption of the herein presented nonparametric approach

Nonparametric Pooling And Testing Of Preference Ratings For Full-Profile Conjoint Analysis Experiments

The problem of pooling customer preference ratings within a conjoint analysis experiment has been addressed. A method based on the nonparametric combination of rankings has been proposed to compete with the usual method based on the arithmetic mean. This method is nonparametric with respect to the underlying dependence structure and so no dependence model must be assumed. The two methods have been compared using Spearman’s rank correlation coefficient and related test. Moreover, a further nonparametric testing method has been considered and proposed; this method takes both correlation and distance between ranks into account. By means of a simulation study it has been shown that the NPC Ranking method performs better than the arithmetic mean

Chapter Nonparametric methods for stratified C-sample designs: a case study

Several parametric and nonparametric methods have been proposed to deal with stratified C-sample problems where the main interest lies in evaluating the presence of a certain treatment effect, but the strata effects cannot be overlooked. Stratified scenarios can be found in several different fields. In this paper we focus on a particular case study from the field of education, addressing a typical stochastic ordering problem in the presence of stratification. We are interested in assessing how the performance of students from different degree programs at the University of Padova change, in terms of university credits and grades, when compared with their entry test results. To address this problem, we propose an extension of the Non-Parametric Combination (NPC) methodology, a permutation-based technique (see Pesarin and Salmaso, 2010), as a valuable tool to improve the data analytics for monitoring University students’ careers at the School of Engineering of the University of Padova. This new procedure indeed allows us to assess the efficacy of the University of Padova’s entry tests in evaluating and selecting future students

Nonparametric combination tests for comparing two survival curves with informative and non-informative censoring

This paper looks at permutation methods used to deal with hypothesis testing within the survival analysis framework. In the literature, several attempts have been made to deal with the comparison of survival curves and, depending on the survival and hazard functions of two groups, they can be more or less efficient in detecting differences. Furthermore, in some situations, censoring can be informative in that it depends on treatment effect. Our proposal is based on the nonparametric combination approach and has proven to be very effective under different configurations of survival and hazard functions. It allows the practitioner to test jointly on primary and censoring events and, by using multiple testing methods, to assess the significance of the treatment effect separately on the survival and the censoring process

Alkaline Water and Longevity: A Murine Study

The biological effect of alkaline water consumption is object of controversy. The present paper presents a 3-year survival study on a population of 150 mice, and the data were analyzed with accelerated failure time (AFT) model. Starting from the second year of life, nonparametric survival plots suggest that mice watered with alkaline water showed a better survival than control mice. Interestingly, statistical analysis revealed that alkaline water provides higher longevity in terms of \u201cdeceleration aging factor\u201d as it increases the survival functions when compared with control group; namely, animals belonging to the population treated with alkaline water resulted in a longer lifespan. Histological examination of mice kidneys, intestine, heart, liver, and brain revealed that no significant differences emerged among the three groups indicating that no specific pathology resulted correlated with the consumption of alkaline water. These results provide an informative and quantitative summary of survival data as a function of watering with alkaline water of long-lived mouse models

Functional classifications in phytoplankton ecology: a comparative review of approaches and experiences

3openEmpirical 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.openSalmaso, N.; Naselli Flores, L.; Padisák, J.Salmaso, N.; Naselli Flores, L.; Padisák, J