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This study is based on the statistical analysis of interlaminar fracture toughness of various laminated polymer composites used in aerospace applications through parametric and non-parametric tests. Tukey's, Dunkan's, two-sample t -test, and Kolmogorov–Smirnov tests are used to analyze tensile mode interlaminar fracture toughness of various fiber-reinforced polymer composites obtained by beam theory, modified beam theory, and modified compliance calibration method. Among the studied composite samples, modified compliance calibration method provided the highest average interlaminar fracture toughness, whereas the modified beam theory showed the lowest one. Room temperature cured carbon fiber-reinforced composite samples exhibited higher interlaminar fracture toughness than the autoclave cured samples. Two-sample t -test show that all methods are found coherent with each other in terms of being significantly different. On the contrary, Kolmogorov–Smirnov test revealed no significant difference. Besides, Tukey's and Duncan's tests exhibited almost the same results in regard to significant differences except those obtained by the modified compliance calibration method. Two-sample t -test method should have to be performed in order to observe significant relations rather than Kolmogorov–Smirnov test, since the results of Tukey's and Duncan's tests are only consistent with each other for the beam theory and modified beam theory method. [ABSTRACT FROM AUTHOR]

Purpose: The study was conducted to explore the perception of healthcare workers and staff towards the risk of COVID-19 vaccination and to study vaccine hesitancy amongst them. Methods: A total of 266 healthcare workers working in a medical college in Nepal were studied using a questionnaire consisting of three sections: demographics, experiences and perception of COVID-19, and COVID-19 vaccine safety. Data were analyzed using IBM SPSS version 26. The total perception score was calculated by noting respondent's agreement with a set of eleven statements using a Likert-type scale. Non-parametric tests (Mann–Whitney U and Kruskal–Wallis) were used for analysis (p< 0.05). Results: Altogether, 13.9% of respondents had been diagnosed COVID-19 positive prior to the survey. Many considered themselves to be at increased risk of contracting COVID-19. Only over one third (38.3%) were willing to be vaccinated. The most common reason for refusal/hesitancy was concern about vaccine safety. The median (interquartile range) total perception score was 36 (4) (maximum possible score=55). The score was significantly higher among those who had been diagnosed COVID positive, those who perceived the pandemic as being moderate or severe and among those willing to be vaccinated Conclusion: Addressing doubts related to vaccine safety and providing more data on the safety of vaccine may be helpful in overcoming hesitancy. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of surface water quality of the sub-basin of the Vargem das Flores reservoir, which is used for public water supply to the metropolitan station of Belo Horizonte (RMBH), Minas Gerais. A total of 5415 data items and eight water quality parameters, common to all monitoring stations, from the period 2002 to 2018 were assessed using multivariate and nonparametric statistical tests. The water quality parameters that were assessed included color, biological oxygen demand (BOD), total phosphorus, total manganese, dissolved oxygen (DO), total suspended solids, and turbidity showed violations of the maximum concentrations allowed in most of the tributaries to the reservoir. Despite this, the stations inside the reservoir appeared to have good quality water, particularly at Station 1, whose water is designated for human consumption. The results of a cluster analysis showed that the water quality of the sub-basin of the Vargem das Flores reservoir is in a condition of environmental degradation relative to the water quality of the water courses that feed the reservoir. The intense and disorderly development in the sub-basin and the discharge of untreated or poorly treated wastewater effluents are factors that can lead to sediment supply and cause the silting and contamination of the water. [ABSTRACT FROM AUTHOR]

The use of classic nonparametric tests (cNPTs), such as the Kruskal–Wallis and Mann–Whitney U tests, in the presence of unequal variance for between-group comparisons of means and medians may lead to marked increases in the rate of falsely rejecting null hypotheses and decreases in statistical power. Yet, this practice remains prevalent in the scientific literature, including nutrition and obesity literature. Some nutrition and obesity studies use a cNPT in the presence of unequal variance (i.e. heteroscedasticity), sometimes because of the mistaken rationale that the test corrects for heteroscedasticity. Herein, we discuss misconceptions of using cNPTs in the presence of heteroscedasticity. We then discuss assumptions, purposes, and limitations of 3 common tests used to test for mean differences between multiple groups, including 2 parametric tests: Fisher's ANOVA and Welch's ANOVA; and 1 cNPT: the Kruskal–Wallis test. To document the impact of heteroscedasticity on the validity of these tests under conditions similar to those used in nutrition and obesity research, we conducted simple simulations and assessed type I error rates (i.e. false positives, defined as incorrectly rejecting the null hypothesis). We demonstrate that type I error rates for Fisher's ANOVA, which does not account for heteroscedasticity, and Kruskal–Wallis, which tests for differences in distributions rather than means, deviated from the expected significance level. Greater deviation from the expected type I error rate was observed as the heterogeneity increased, especially in the presence of an imbalanced sample size. We provide brief tutorial guidance for authors, editors, and reviewers to identify appropriate statistical tests when test assumptions are violated, with a particular focus on cNPTs. [ABSTRACT FROM AUTHOR]

Testing homogeneity of species assemblages has important applications in ecology. Due to the unique structure of abundance data often collected in ecological studies, most classical statistical tests cannot be applied directly. In this article, we propose two novel nonparametric tests for comparing species assemblages based on the concept of data depth. They can be considered as a natural generalization of the Kolmogorov-Smirnov and the Cramér-von Mises tests (KS and CM) in this species assemblage comparison context. Our simulation studies show that the proposed test is more powerful than other existing methods under various settings. A real example is used to demonstrate how the proposed method is applied to compare species assemblages using plant community data from a highly diverse tropical forest at Barro Colorado Island, Panama.

Copyright of Scientific Papers of Electrical & Control Engineering Faculty Technical University of Gdansk / Zeszyty Naukowe Wydzialu Elektrotechniki & Automatyki Politechniki Gdanskiej is the property of Gdansk University of Technology, Faculty of Electrical & Control Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The average quantitative research report in applied linguistics is needlessly complicated. Articles with over fifty hypothesis tests are no exception, but despite such an onslaught of numbers, the patterns in the data often remain opaque to readers well-versed in quantitative methods, not to mention to colleagues, students, and non-academics without years of experience in navigating results sections. I offer five suggestions for increasing both the transparency and the simplicity of quantitative research reports: (1) round numbers, (2) draw more graphs, (3) run and report fewer significance tests, (4) report simple rather than complex analyses when they yield essentially the same results, and (5) use online appendices liberally to document secondary analyses and share code and data. [ABSTRACT FROM AUTHOR]

Testing of equality of scale parameters is one of the fundamental problems in Statistics and is useful in many fields such as agriculture, engineering, experimental methods and dose level testing. In this paper, we propose a new class of nonparametric tests, to test whether the scale parameters of all the populations are equal, or there is an umbrella ordering in the scale parameters. The proposed test is based on weighted linear combination of two-sample U-statistics. The optimal choice of weights is determined so that proposed tests have maximum efficiency. The comparison of proposed test with respect to existing tests is made in terms of Pitman asymptotic relative efficiency. A numerical example is provided for illustrative purpose. Power of the proposed test is also assessed using Monte Carlo simulation study. [ABSTRACT FROM AUTHOR]

Testing of homogeneity of populations is a very useful phenomenon in several fields. After knowing that the two populations have the same variability or not, public/private sector organizations can execute their plans accordingly. In this paper, two new classes of nonparametric tests are proposed to test whether the scale parameters of two populations are the same or not. For each class of tests, we determine which member of the class provides the maximum efficacy; and compare its performance with some of the existing tests. Monte Carlo simulation study is carried to see the performance of both tests in terms of power. An illustrative example is also provided to see the implementation of the proposed tests. [ABSTRACT FROM AUTHOR]

Understanding deficits in motor control through the analysis of pedaling biomechanics plays a key role in the treatment of stroke patients. A thorough study of the impact of different exercise patterns and workloads on the change between pre- and post-treatment movement patterns in the patients is therefore of utmost importance to the clinicians. The objective of this study was to analyze the difference between pre- and post-treatment pedaling torques when the patients are subject to different exercise groups with varying workloads. The effects of affected vs unaffected side along with the covariates age and BMI have also been accounted for in this work. Two different three-way ANOVA-based approaches have been implemented here. In the first approach, a random projection-based ANOVA technique has been performed treating the pedaling torques as functional response, whereas the second approach utilizes distance measures to summarize the difference between pre- and post-treatment torques and perform nonparametric tests on it. Bayesian bootstrap has been used here to perform tests on the median distance. A group of stroke patients have been studied in the Cleveland Clinic categorizing them into different exercise groups and workload patterns. The data obtained have been analyzed with the aforementioned techniques, and the results have been reported here. These techniques turn out to be promising and will help clinicians recommend personalized treatment to stroke patients for optimal results. [ABSTRACT FROM AUTHOR]

This note complements and clarifies part of the work of Hawinkel et al. recently published in the journal and suggests some more or less standard tools and methods for carrying out association studies of the microbiome. [ABSTRACT FROM AUTHOR]

This study investigates the inter-relationships between three different markets - the stock market (S&P500), the Brent oil market, and the foreign exchange market (FX), during different Brent oil price shock periods. We examined mean-reverting properties for Brent oil prices and the volatility relationships between the three markets using the constant conditional correlations (CCC) model, the dynamic conditional correlations (DCC), and the time-varying conditional correlations (VCC). We found evidence that there are arbitrage opportunities in the Brent oil markets and that there are volatility relationships between the three markets. The paper also concluded that there is a long-run dynamic equilibrium between Brent oil, FX, and S&P500. [ABSTRACT FROM AUTHOR]

The nonparametric test of Kruskal–Wallis and relative risk were used to evaluate surface water quality allowed to an identification of the most degraded water bodies in Piracicaba River and Paraopeba River basins, two important hydrographic basins in Brazil. Total manganese, dissolved iron, and fecal contamination indicator were considered the most relevant parameters for the characterization of water quality in the basins. The Peixe River, in Nova Era, and Pedras Creek, in Betim, were considered the most impacted water bodies in the Piracicaba River and Paraopeba River basins, respectively. The analysis of violations and the relative risk confirmed that both basins are subject to impacts resulting from economic activities. On comparing the relative risks, the Paraopeba River basin showed a higher risk of violation for 5-day biological oxygen demand (BOD5), total manganese, total phosphorus, total suspended solids, and turbidity, while the Piracicaba River basin showed a higher risk of violation for fecal contamination indicator. The release of domestic sewage and industrial effluents, mining activities, and diffuse pollution from agriculture and pasture areas were responsible for the surface water quality deterioration in these basins. The results show the need for investment in basic sanitation, improved treatment efficiency for industrial effluents, adequate soil management, riparian vegetation preservation, and environmental education actions. [ABSTRACT FROM AUTHOR]

Modern data collection techniques allow to analyze a very large number of endpoints. In biomedical research, for example, expressions of thousands of genes are commonly measured only on a small number of subjects. In these situations, traditional methods for comparison studies are not applicable. Moreover, the assumption of normal distribution is often questionable for high-dimensional data, and some variables may be at the same time highly correlated with others. Hypothesis tests based on interpoint distances are very appealing for studies involving the comparison of means, because they do not assume data to come from normally distributed populations and comprise tests that are distribution free, unbiased, consistent, and computationally feasible, even if the number of endpoints is much larger than the number of subjects. New tests based on interpoint distances are proposed for multivariate studies involving simultaneous comparison of means and variability, or the whole distribution shapes. The tests are shown to perform well in terms of power, when the endpoints have complex dependence relations, such as in genomic and metabolomic studies. A practical application to a genetic cardiovascular case-control study is discussed. [ABSTRACT FROM AUTHOR]

Considering the obvious relationship between the education level of the population and level of development of the region of residence and its sustainable development was considered interesting to analyze whether the typology of the geographical regions influences the educational attainment level of the population. To highlight the association between geographical territory and educational attainment level of the population, the regions were classified into three different categories, respectively: predominantly urban, intermediate and predominantly rural by respect the Eurostat urban-rural typology, and the educational levels in three categories based on the ISCED11 levels and categories, respectively less than primary, primary and lower secondary education,, upper secondary and postsecondary non-tertiary education, and tertiary education. The study aims to determine whether there is a significant relationship between the three categories of regions and the types of training, education predisposition to the resident population. In this sense, was appealed to Chi-Square test, a nonparametric test used to assess the association between two variables, both based on nominal scales, and specific measure of the degree of association. In the study were used centralized data at the national level referring to the population aged 25-64 by educational attainment level, and urban-rural typology, accessed from Eurostat database, for the year 2015, the latest year for which are available official data.

This paper proposes a nonparametric procedure for testing conditional quantile independence using projections. Relative to existing smoothed nonparametric tests, the resulting test statistic: (i) detects the high frequency local alternatives that converge to the null hypothesis in probability at faster rate and, (ii) yields improvements in the finite sample power when a large number of variables are included under the alternative. In addition, it allows the researcher to include qualitative information and, if desired, direct the test against specific subsets of alternatives without imposing any functional form on them. We use the weighted Nadaraya-Watson (WNW) estimator of the conditional quantile function avoiding the boundary problems in estimation and testing and prove weak uniform consistency (with rate) of the WNW estimator for absolutely regular processes. The procedure is applied to a study of risk spillovers among the banks. We show that the methodology generalizes some of the recently proposed measures of systemic risk and we use the quantile framework to assess the intensity of risk spillovers among individual financial institutions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Kirkuk University for Administrative & Economic Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A notion of data depth is used to measure centrality or outlyingness of a data point in a given data cloud. In the context of data depth, the point (or points) having maximum depth is called as deepest point (or points). In the present work, we propose three multi-sample tests for testing equality of location parameters of multivariate populations by using the deepest point (or points). These tests can be considered as extensions of two-sample tests based on the deepest point (or points). The proposed tests are implemented through the idea of Fisher's permutation test. Performance of earlier tests is studied by simulation. Illustration with two real datasets is also provided. [ABSTRACT FROM AUTHOR]

The two-sample problem usually tests for a difference in location. However, there are many situations, for example in biomedicine, where jointly testing for difference in location and variability may be more appropriate. Moreover, heavy-tailed data, outliers and small-sample sizes are common in biomedicine and in other fields. These considerations make the use of nonparametric methods more appealing than parametric ones. The aim of the paper is to contribute to the literature about nonparametric simultaneous location and scale testing. More precisely, several existing tests are generalized and unified, and a new class of tests based on the Mahalanobis distance between the percentile modified test statistics for location and scale differences is introduced. The asymptotic distributions of the test statistics are obtained, and small-sample size behaviour of the tests is studied and compared to other tests via Monte Carlo simulations. It is shown that the proposed class of tests performs well when there are differences in both location and variability. A practical application is presented. [ABSTRACT FROM AUTHOR]

Prior researchers document no significant abnormal returns around upgrades of credit ratings, suggesting upgrades convey no new information. These studies are limited by lack of data, liquidity screens, and ambiguous predictions. We extend the literature using trading volume. Because trading volume is highly non-normally distributed (especially bond market volume), we derive a new, more powerful nonparametric test statistic that can be used in other applications. Abnormal volume is significant in the stock and bond markets around upgrades and downgrades. Some abnormal volume is attributable to credit ratings-based regulations. Controlling for other effects, we find evidence that upgrade announcements contain information. • There is significant abnormal volume around credit rating changes. • Some abnormal volume is attributable to credit-ratings-based regulations. • Controlling for other effects, upgrade announcements contain information. • Our new non-parametric statistical test has more power than existing tests. [ABSTRACT FROM AUTHOR]

The main objective of this study is to assess the levels of statistical use (basic, intermediate, and advanced) in Applied Linguistics research articles over the past three decades (from 1986 to 2015). The corpus included 4079 quantitative and mixed-methods studies published in ten prominent journals of Applied Linguistics. The articles were analysed and the statistical techniques used were aggregated by two current writers and four PhD students in TEFL. Results showed that descriptive statistics (40.04%) were by far the most commonly used technique followed by one-way ANOVA (14.91%), t-test (10.15%), and Pearson correlation (8.76%). Regarding the sophistication level of statistical use, about 78.77% (n = 4686) of the techniques were classified as basic, 14.49% (n = 862) as intermediate, and 6.74% (n = 401) as advanced. Clearly, most of the techniques were either basic or intermediate, with a significant higher percentage for the former. So, a person with basic knowledge of statistics could understand 69.03% of the papers published during 1986 to 2015. It is discussed that researchers should be updated on recent statistical knowledge if they wish to statistically comprehend research articles published in Applied Linguistics journals. [ABSTRACT FROM AUTHOR]

Sign, Wilcoxon and Mann-Whitney tests are nonparametric methods in one or two-sample problems. The nonparametric methods are alternatives used for testing hypothesis when the standard methods based on the Gaussianity assumption are not suitable to be applied. Recently, the functional data analysis (FDA) has gained relevance in statistical modeling. In FDA, each observation is a curve or function which usually is a realization of a stochastic process. In the literature of FDA, several methods have been proposed for testing hypothesis with samples coming from Gaussian processes. However, when this assumption is not realistic, it is necessary to utilize other approaches. Clustering and regression methods, among others, for non-Gaussian functional data have been proposed recently. In this paper, we propose extensions of the sign, Wilcoxon and Mann-Whitney tests to the functional data context as methods for testing hypothesis when we have one or two samples of non-Gaussian functional data. We use random projections to transform the functional problem into a scalar one, and then we proceed as in the standard case. Based on a simulation study, we show that the proposed tests have a good performance. We illustrate the methodology by applying it to a real data set.

Genotype-by-environment interaction refers to the differential response of different genotypes across different environments. This is a general phenomenon in all living organisms and always has been one of the main challenges for biologists and plant breeders. The nonparametric methods based on the rank of original data have been suggested as the alternative methods after parametric methods to analyze data without perquisite assumptions needed for common analysis of variance. But, the lack of statistical software or package, especially for analysis of two-way data, is one of the main reasons that plant breeders have not greatly used the nonparametric methods. Here, we have explained the nonparametric methods and presented a comprehensive two-parts SAS program for calculation of four nonparametric statistical tests (Bredenkamp, Hildebrand, Kubinger and van der Laan-de Kroon) and all of the valid stability statistics including Hühn’s parameters (Si(1), Si(2), Si(3), Si(6)), Thennarasu’s parameters (NPi(1), NPi(2), NPi(3), NPi(4)), Fox's ranking technique and Kang’s rank-sum.

When the power of different nonparametric tests is evaluated by simulation, the alternative hypothesis should be carefully designed to ensure validity of the results in the specific research field. In the article, we propose a probit-based progressive shift alternative that is more realistic than the simple shift alternative for skewed non-negative data that occur in many research areas. Our motivation comes from parasitology. The progressive shift alternative is used to compare the power of six location-scale tests and seven commonly used location tests for several skewed theoretical and empirical parasite distributions. It is shown that location-scale tests are more powerful than location tests. Programs for applying the methods studied in the article are freely available for download. [ABSTRACT FROM AUTHOR]

The paper aims to investigate if the activity sector matters in explaining and analysing the job satisfaction, its determinants and also the satisfaction related to the performance evaluation process using an empirical research based on gender differences for a sample of 301 employees from seven sectors of activity. The paper aims to respond to the following key research questions: there are statistical differences concerning the job satisfaction and also the perceptions regarding the performance evaluation process in different sectors of activity with certain specificities?. In order to respond to these questions, descriptive statistics, non-parametric correlation and non-parametric tests (Kruskal-Wallis) were used. The empirical results revealed that employees from services, manufacturing industry and agriculture exhibited the highest level of job satisfaction while constructions represents the sector with the lowest level. The analysis of motivational factors revealed that workplace comfort and job stability are considered to be the most important motivating factors in all sectors of activity, while the perspective of hierarchical advancement and logistical support the least motivating. The results of Kruskal-Wallis test pointed out statistical differences among sectors of activity regarding the job satisfaction level and the attractiveness of work done. Concerning the level of satisfaction related to the last employee evaluations, retail trade and manufacturing industry are the sector with the highest degree of satisfaction while services and wholesale trade registered lower levels of satisfaction. As methods used in the evaluation, the analysis based on the superiors' opinion was used in all sectors with the exception of agriculture characterized by the presence of informal free talks. The empirical results highlighted significant differences in different sectors of activity regarding the elements taken into consideration in the employee performance evaluation process: productivity, initiative/innovation, quality of communication, relationships with subordinates, superiors and customers and professional development. [ABSTRACT FROM AUTHOR]

Despite the wide use of statistics in biomedical research, simple ideas are sometimes misunderstood or misinterpreted by medical research workers, who have only limited knowledge of statistics. This article deals with basic biostatistical concepts and their application to enable postgraduate medical students and researchers to analyze and interpret their study data and to critically interpret published literature. The adequate choice of statistical tests has a strong influence on data interpretation. Understanding this choice is important for critical evaluation of biomedical research. The question often arises on whether to use parametric or nonparametric test. If we are planning a study and trying to determine how many patients/cases to include, a nonparametric test will require a slightly larger sample size to have the same power as the corresponding parametric test. In summary, nonparametric procedures are useful in many cases and necessary in individual, but they are not the perfect solution. Fortunately, the most frequently used parametric analyses have their non-parametric counterparts. This can be useful when the assumptions of a parametric test are violated and we can thus choose a nonparametric alternative instead. [ABSTRACT FROM AUTHOR]

Signal detection is routinely applied to spontaneous report safety databases in the pharmaceutical industry and by regulators. As an example, methods that search for increases in the frequencies of known adverse drug reactions for a given drug are routinely applied, and the results are reported to the health authorities on a regular basis. Such methods need to be sensitive to detect true signals even when some of the adverse drug reactions are rare. The methods need to be specific and account for multiplicity to avoid false positive signals when the list of known adverse drug reactions is long. To apply them as part of a routine process, the methods also have to cope with very diverse drugs (increasing or decreasing number of cases over time, seasonal patterns, very safe drugs versus drugs for life-threatening diseases). In this paper, we develop new nonparametric signal detection methods, directed at detecting differences between a reporting and a reference period, or trends within a reporting period. These methods are based on bootstrap and permutation distributions, and they combine statistical significance with clinical relevance. We conducted a large simulation study to understand the operating characteristics of the methods. Our simulations show that the new methods have good power and control the family-wise error rate at the specified level. Overall, in all scenarios that we explored, the method performs much better than our current standard in terms of power, and it generates considerably less false positive signals as compared to the current standard. [ABSTRACT FROM AUTHOR]

Two nonparametric tests are employed to investigate the potential information value of USDA crop and livestock reports. If daily returns on days that reports are released (announcement days) differ when compared to non-announcement days for a sizeable number of commodities from a set of seven futures markets studied, we deem the report to contain market-moving information. The question of report value has been unsettled in the literature with results varying somewhat across studies and across reports. This study finds market-moving value in five of the USDA reports investigated, with six other reports showing little or no market-moving value in the markets examined. While most of our results confirm and add robustness to earlier results, there are some differences both for certain reports and certain commodities.

Bayes factors, practical tools of applied statistics, have been dealt with extensively in the literature in the context of hypothesis testing. The Bayes factor based on parametric likelihoods can be considered both as a pure Bayesian approach as well as a standard technique to compute p -values for hypothesis testing. We employ empirical likelihood methodology to modify Bayes factor type procedures for the nonparametric setting. The paper establishes asymptotic approximations to the proposed procedures. These approximations are shown to be similar to those of the classical parametric Bayes factor approach. The proposed approach is applied towards developing testing methods involving quantiles, which are commonly used to characterize distributions. We present and evaluate one and two sample distribution free Bayes factor type methods for testing quantiles based on indicators and smooth kernel functions. An extensive Monte Carlo study and real data examples show that the developed procedures have excellent operating characteristics for one-sample and two-sample data analysis. [ABSTRACT FROM AUTHOR]

The subject of this paper is to determine the statistical significance of abnormal return that appeared on the New York Stock Exchange after the presidential election in the USA in November 2012. The analysis is focused on securities of the financial institutions listed on the New York Stock Exchange, whereby 85 companies have been included. For the purposes of the analysis a standard methodology of event study has been used. In general, parametric tests show a statistically significant negative impact of the event on stock return, whereby with the nonparametric tests there is no consistent estimation. This paper provides an interpretation of the results. [ABSTRACT FROM PUBLISHER]

Considering the obvious relationship between the education level of the population and level of development of the region of residence and its sustainable development was considered interesting to analyze whether the typology of the geographical regions influences the educational attainment level of the population. To highlight the association between geographical territory and educational attainment level of the population, the regions were classified into three different categories, respectively: predominantly urban, intermediate and predominantly rural by respect the Eurostat urban-rural typology, and the educational levels in three categories based on the ISCED11 levels and categories, respectively less than primary, primary and lower secondary education, upper secondary and postsecondary non-tertiary education, and tertiary education. The study aims to determine whether there is a significant relationship between the three categories of regions and the types of training, education predisposition to the resident population. In this sense, was appealed to Chi-Square test, a nonparametric test used to assess the association between two variables, both based on nominal scales, and specific measure of the degree of association. In the study were used centralized data at the national level referring to the population aged 25-64 by educational attainment level, and urban-rural typology, accessed from Eurostat database, for the year 2015, the latest year for which are available official data. [ABSTRACT FROM AUTHOR]

Interest in biodegradable products such as natural fibers for use in various industries is growing. These fibers are an inexpensive alternative with environmentally friendly appeal in comparison to other materials used. This article reports evaluation of the genetic variability of 13 pineapple genotypes to ascertain the use their fibers as mechanical reinforcement in composites applied in industry and analyzes possible association of 217 bands from 17 ISSR markers with fiber quality characteristics by nonparametric tests (Spearman correlation and Kruskal-Wallis). The study of genetic diversity, using qualitative and quantitative data, shows there are genotypes genetically close to curauá (a close relative of pineapple), whose fibers are already commonly used in the industry. Of the 217 ISSR bands, 11 were selected based on their high correlation (0.63434 * to 0.76169 ** ) regarding all four variables for fiber quality, enabling identification of a set of primers which can be used in early selection of promising genotypes after sequencing and validation. The possibility of using marker assisted selection in pineapple for fiber quality will bring great returns, not only for the industry, but also to ensure the sustainability of this productive sector. This is the first report of the use of nonparametric methods to analyze fiber quality of this plant. [ABSTRACT FROM AUTHOR]

Two hypotheses can explain the declining probability of gaining employment as an unemployment spell wears on: heterogeneity of the unemployed versus duration dependence. The nonparametric tests developed in the literature for testing duration dependence would not account for the fact that an unemployment spell can terminate in other ways than employment. The nonparametric tests developed in this paper extend, under certain conditions, those tests to competing risks. We illustrate our test using US unemployment data in which we find little consistent evidence for duration dependence. © 2017 The Authors. Applied Stochastic Models in Business and Industry published by John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We empirically investigate the short-run impact of anticipated and unanticipated unemployment rates on stock prices. We particularly examine the nonlinearity in the stock market's reaction to the unemployment rate and study the effect at each individual point (quantile) of the stock return distribution. Using nonparametric Granger causality and quantile regression-based tests, we find that only anticipated unemployment rate has a strong impact on stock prices. Quantile regression analysis shows that the causal effects of anticipated unemployment rate on stock returns are usually heterogeneous across quantiles. For the quantile range 0.35, 0.80, an increase in the anticipated unemployment rate leads to an increase in stock market prices. For other quantiles, the impact is generally statistically insignificant. Thus, an increase in the anticipated unemployment rate is, in general, good news for stock prices. Finally, we offer a reasonable explanation for the reason, and manner in which, the unemployment rate affects stock market prices. Using the Fisher and Phillips curve equations, we show that a high unemployment rate is followed by monetary policy action of the Federal Reserve (Fed). When the unemployment rate is high, the Fed decreases the interest rate, which in turn increases the stock market prices. [ABSTRACT FROM AUTHOR]