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

Before-and-After Field Investigation of the Effects on Pollutant Emissions of Replacing a Signal-Controlled Road Intersection with a Roundabout

The purpose of this study is to assess the effects on air pollution that may derive from replacing a signal-controlled intersection with a roundabout, using a before-and-after approach. Based on field data collected with a test car instrumented with a Portable Emission Measurement System, the two intersection configurations were compared in terms of emissions of CO2, CO, and NOX. The existence of significant differences in emissions between the two types of control was assessed by means of a statistical technique known as two-sample biaspect permutation test. In addition, focusing on trips carried out in peak traffic conditions, binary logistic regression models were developed to identify the factors that significantly affect vehicular emissions and to quantify their effect. The findings of our analyses show that emissions of CO2 and CO are generally lower for the roundabout than for the signal-controlled intersection, while an opposite result arises for NOX emissions. As far as other influential factors are concerned, trip direction (reflecting site-specific conditions) and driver behavior have a considerable impact on the emissions of all three pollutants

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

on road measurement of co2 vehicle emissions under alternative forms of intersection control

Abstract The environmental impact of road intersection operations, and in particular of alternative types of traffic control, has received increasing attention in recent years as a factor to be considered in addition to efficiency and safety. The purpose of this study is to provide experimental evidence about this issue based on direct measurement of CO2 emissions produced by a vehicle under traffic signal versus roundabout control. Carbon Dioxide was chosen as specific target of the analysis because of its important contribution to the "greenhouse effect". Using data collected with a Portable Emission Measurement System (PEMS) installed on a test car, a before-and-after analysis was conducted on an intersection where a roundabout has replaced a traffic signal. A total of 396 trips were carried out by two drivers in different traffic conditions and in opposite directions along a designated route. Using statistical methods, the existence of significant differences in CO2 emissions in relation to the type of intersection control was investigated based on the collected data, also considering the effect of other explanatory variables and focusing in particular on peak traffic conditions. More precisely, the effect of the type of control has been characterized using descriptive statistics and permutation tests applied to the entire data set, while an analysis based on binary logistic regression has been performed with specific reference to trips carried out under peak traffic conditions. The results of these analyses support the conclusion that converting a signal-controlled intersection to a roundabout may lead to a decrease in CO2 emissions

Different views of the multivariate ranking problem

Multivariate ranking problems are characterized by the need of ordering C different items according to several different features. The multivariate nature of these problems makes them quite challenging and flexible multivariate statistical techniques are therefore required. In this study we focus on two different scenarios, where we need to rank C different populations. Under the first scenario, preliminary knowledge about the order of the populations is available, while under the second one no information is available. Two solutions, based on the Nonparametric combination (NPC) technique, are proposed to deal with these scenarios and two case studies are adopted to facilitate the comprehension of the methods and to highlights the main differences between the two considered multivariate ranking problems

Shelf-life prediction: A comparison of methods

The shelf-life assessment of a product is essential to ensuring its safety and integrity. Shelf life is the period of time during which the product retains its required quality level under well-defined storage conditions. To assess the stability of a generic product, a stability test is required: the product is kept under di↵erent storage conditions and the performance of characteris- tics used to assess the quality of the product is monitored. Data collected through stability tests are then used to predict the product’s shelf life under further storage conditions a applying the calculated degradation rate. Ki- netic models, such as the Arrhenius equation, are usually applied for this purpose. Since humidity can accelerate product degradation, it may be of interest to consider methods which quantify the e↵ect of humidity. This pa- per proposes a comparison of several methods used to predict shelf life: the Bracket method, Eyring method, Peck method, Klinger method and Q-rule. Two case studies are performed to compare the performance of the applied methods, in order to determine the most accurate method

Shelf-life prediction: A comparison of methods

The shelf-life assessment of a product is essential to ensuring its safety and integrity. Shelf life is the period of time during which the product retains its required quality level under well-defined storage conditions. To assess the stability of a generic product, a stability test is required: the product is kept under di↵erent storage conditions and the performance of characteris- tics used to assess the quality of the product is monitored. Data collected through stability tests are then used to predict the product’s shelf life under further storage conditions a applying the calculated degradation rate. Ki- netic models, such as the Arrhenius equation, are usually applied for this purpose. Since humidity can accelerate product degradation, it may be of interest to consider methods which quantify the e↵ect of humidity. This pa- per proposes a comparison of several methods used to predict shelf life: the Bracket method, Eyring method, Peck method, Klinger method and Q-rule. Two case studies are performed to compare the performance of the applied methods, in order to determine the most accurate method