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In this part of the review, statistical tests utilized to examine hypotheses regarding population parameters on a representative sample, which forms the fundamentals of inferential statistics are discussed. The selection of an appropriate statistical test by verifying its assumptions and interpreting the results objectively is crucial for obtaining accurate conclusions. Understanding the terms related to type I and type II errors, p-value, power of the study, effect size, and confidence interval will contribute to the correct interpretation of both the results obtained from statistical tests in scientific research and the findings of articles read from the literature. In addition to univariate tests, the three most commonly employed multiple regression models are also addressed to control for the effect of potential confounding factors and other independent variables utilized in the study. Statistical computing has become much more accessible in recent times, with researchers having access to freeware packages or web applications to perform basic and advanced statistical analyses. Researchers frequently focus on the calculation of statistical tests used in data analysis, whereas, understanding the rationale behind statistical methods should be the primary goal. Therefore, this review emphasizes the logic of selecting appropriate statistical methods and interpreting the results rather than mathematical calculations. It is essential to recognize that biostatistical principles should be considered not only in the data analysis phase but also in all phases of research, from planning to report writing. It should be note that, no statistical analysis method can correct erroneous data obtained from a poorly designed study. [ABSTRACT FROM AUTHOR]

Bracero Marrero, L.I.M., 2024. Analysis of shoreline change in Loíza, Puerto Rico (1902–2018). Journal of Coastal Research, 40(6), 1080–1089. Charlotte (North Carolina), ISSN 0749-0208. Shoreline change analysis is one of the most common methods used to analyze the morphological change—accretional or erosional—in coastal areas. Seven years of shoreline positions (1902, 1931, 1951, 1977, 1990, 2010, and 2018) were analyzed inside the Digital Shoreline Analysis System to measure the shoreline change in Loíza, Puerto Rico. The main results indicated increasing erosional rates for the study area as time passed, with recent events interrupting the pattern. For the periods 1951–77, 1977–90, and 1990–2010, statistics supported the hypothesis that erosional rates were dominant, whereas the periods 1902–31, 1931–51, and 2010–18 were dominated by accretional rates of change. Anthropogenic impacts (i.e. sand extraction events and dam construction) were reflected in the results of 1951–77 and 1977–90, with higher erosion rates and negative outliers. Accretional rate outliers in the latest period, 2010–18, are possibly the result of Hurricane María in 2017, when heavy rains and high river discharge increased the sediment input into the shoreline. This study showed previously unrecorded rate change data for 116 years in Loíza, Puerto Rico. [ABSTRACT FROM AUTHOR]

This paper addresses the urgent challenges posed by limited energy resources and environmental degradation, focusing on the critical issue of excessive energy consumption and advocating for sustainable practices. The study explores the viability of utilizing solar-powered electric vehicles with induction cooking systems as a sustainable, long-term solution for India's travel and tourism sector. The implementation of such systems in urban and suburban regions presents numerous advantages, including decreased dependence on conventional energy sources, reduced greenhouse gas emissions, and financial benefits derived from more affordable transportation and lower fuel costs. Identifying a literature gap in the deployment of electric vehicles within the travel and tourism domain, the research is motivated by environmental considerations, sustainability goals, economic factors, health standards, innovation, system reliability, and regulatory incentives. The authors contribute significantly by meticulously demonstrating the practicality of the electric vehicle mobile food court (EVMFC) model. This demonstration highlights the model's adeptness in harnessing solar energy for both vehicular propulsion and culinary operations. The study's unique approach, characterized by a substantial reduction in total energy consumption and an application-based energy management strategy, presents an innovative solution for mobile food services. The comprehensive recommendations offered by the authors serve as a guide for the widespread adoption of solar-powered EVMFCs, providing a transformative solution that is not only economically viable, but also environmentally conscious. This research signifies a pivotal step toward revolutionizing the mobile food industry, with the authors contributing valuable insights that bridge the gap between sustainability, efficiency, and gastronomic innovation. Each Archetype (actual + projected) during 2013–2032 for EVMFCS, DVMFC NIC, DVMFCG and GVMFCG. [ABSTRACT FROM AUTHOR]

Abstract This research designed a distribution-free mixed exponentially weighted moving average-moving average (EWMA-MA) control chart based on signed-rank statistic to effectively identify changes in the process location. The EWMA-MA charting statistic assigns more weight to information obtained from the recent $$w$$ w samples and exponentially decreasing weights to information accumulated from all other past samples. The run-length profile of the proposed chart is obtained by employing Monte Carlo simulation techniques. The effectiveness of the proposed chart is evaluated under symmetrical distributions using a variety of individual and overall performance measures. The analysis of the run-length profile indicates that the proposed chart performs better than the existing control charts discussed in the literature. Additionally, an application from a gas turbine is provided to demonstrate how the proposed chart can be used in practice.

The monthly and annual trends and variance of rainfall have been studied for five stations in an economically important Bangladeshi district named Sirajganj since 1965 to 2021. Natural disasters have prevalent in Sirajganj which is indispensable to assess. But, several researchers have been normally focused on river bank management and flood risk assessment. However, no extensive research has been conducted on Sirajganj based on non-normally distributed time series meteorological data such as rainfall time series so the current study is very important. In this study, the non-parametric Mann-Kendall and Sen's methods have been used to determine the statistical significance of a positive or negative trend in rainfall data. Also, cumulative sum charts and bootstrapping, one-way ANOVA, Tukey's range tests, and linear regression have been used to discover the incidence of abrupt changes, compare the significant difference in monthly and annual rainfall data, multiple comparisons amidst mentioned stations to find changes, and to investigate the changeover on dry and rainy days, respectively. The analysis showed a statistically significant decreasing trends in monthly and annual rainfall series. As well, changes from positive to negative direction have been recognized in the February, May, July, September, and annual rainfall time sequence. Besides, ANOVA and Tukey's range tests revealed a statistically substantial difference in all monthly and annual rainfall volume excluding January, March, and June. Additionally, these two tests demonstrated momentous differences in all monthly and annual frequency of rainfall categories excepting January and April. However, Linear regression analysis revealed that the number of dry days gradually reduced at the end of the dry winter, though the number of rainy days decreased during the rainy season. As in, the number of rainy days replaces the number of dry days during the dry season and vice versa during the rainy season. Even though, with very few exceptions, the volume of rainfall decreases throughout the year. The outcomes of this research might helpful for implementing the planning and evaluating hydrological projects on Sirajganj district.

Many metaheuristic approaches are inherently stochastic. In order to compare such methods, statistical tests are needed. However, choosing an appropriate test is not trivial, given that each test has some assumptions about the distribution of the underlying data that must be true before it can be used. Permutation tests (P-Tests) are statistical tests with minimal number of assumptions. These tests are simple, intuitive and nonparametric. In this paper, we argue researchers in the field of metaheuristics to adopt P-Tests to compare their algorithms. We define two statistic tests and then present an algorithm that uses them to compute the p-value. The proposed process is used to compare 5 metaheuristic algorithms on 10 benchmark functions. The resulting p-values are compared with the p-values of two widely used statistical tests. The results show that the proposed P-test is generally consistent with the classical tests, but more conservative in few cases. [ABSTRACT FROM AUTHOR]

The purpose of this study was to identify the factors affecting the production companies in the laminate flooring industry in terms of competitiveness using nonparametric tests. The statistical population of this research includes experts and specialists in two areas of competitiveness and structure of the laminate flooring industry; managers in companies producing and importing this product and customers of laminate flooring. The sampling method was randomly classified. The Cochran formula was used to determine the sample size. In order to determine the effective indicators while studying the available sources and using the Porter model, as a basic model and after conducting the necessary studies and factor analysis, the conceptual model of the research was designed in form of three main criteria including environmental factors, intra-organizational factors and factors creating perceptual value as well as 9 sub-criteria. After conducting the necessary studies, preparing a researcher-made questionnaire and data factor analysis, the conceptual research model was designed and by conducting a field research and analyzing the results using student's t-test and Friedman test, the relationship between laminate flooring competitiveness with 3 criteria and 9 sub-criteria was examined. The reliability of the data was confirmed by Cronbach's alpha method. The mean of all studied indices was evaluated as moderate based on the results of descriptive statistics. Based on the results of the t-test, environmental factors, intra-organizational factors and factors creating perceptual value have a more than average role on competitiveness. Also, based on Friedman test, prioritization of indicators was obtained respectively based on environmental factors, factors creating perceptual value and intra-organizational factors. [ABSTRACT FROM AUTHOR]

Due to increasing population growth, inadequate management of surface and subsurface water resources, and the escalation of the water crisis in the coming years, will be inevitable. Therefore, it is necessary to provide scientific and multi-criteria indices capable of more precisely examining the status of surface and subsurface water resources. In this study, the water poverty index (WPI) based on five weighted components of resources, availability, capacity, consumption and environment of the Fasa plain during 2008-2018 were calculated and their trends were evaluated using parametric and nonparametric statistical tests (i.e. Mann-Kendall and Spearman). The results showed that the consumption component (0.47) and the capacity component had highest and lowest role in the WPI index. Trend analysis of the WPI index showed that the WPI based on the linear regression, Mann-Kendall and Spearman tests had a non- significant decreasing trend (with S= -0.01, ZS= -1.03 and ZD= -1.38, respectively). It is natural that the decrease in WPI values ​​reflects an increase in the level of crisis in available water resources. Due to the downward trend of WPI index in the Fasa plain, proper scientific and practical management of water resources is essential to provide the enable long-term sustainable use of resources.

The correct conclusion about the assumptions concerning some phenomena can be obtained only through scientific analysis of statistical data. The scientific procedure of verifying a hypothesis using measurement results is called a statistical test. Depending on whether the hypotheses about the parameters in the feature distribution are tested or the distribution as a whole is tested, a parametric or non-parametric test is selected. The most significant representatives of parametric tests are the Probability Ratio Test, the Neumann - Pearson Lemma and the Bootstrap method, while the Pearson x2 test and the Kolmogorov test are presented as representatives of nonparametric tests. The paper presents the theoretical basis of some methods used in construction of statistical tests with given examples in geodesy.

This study aims to identify the expectations and preferences of consumers of home and office furniture using nonparametric tests. The statistical population consists of customers of the home and office wood furniture industry in Tehran and a stratified sampling method was used in this study. Cochran formula was used to determine the sample size. To determine the effective indicators, the researcher studied the available resources and the Van et al.'s model as the basic model and conducted a factor analysis to design the conceptual model of the research. Using a field research, the student t-test and Friedman test, she examined the relationship between consumer preferences and 5 factors and 17 subindices. Results: The reliability of the questionnaire was confirmed by the data obtained from 100 questionnaires using Cronbach's alpha. The results of descriptive statistics indicated that the mean of all the indices was at an average level. The minimum and maximum values and the standard deviation showed that there were no outliers. The results of inferential statistics in SPSS software indicated that the significance level for Kolmogorov-Smirnov test was greater than 5%, and all variables are assumed to be normally distributed. The results indicated a significant relationship between all indicators and consumer preferences regarding home and office furniture. The results of the one-sample t-test indicated that consumers consider corporate responsibility factors, product and vendor's characteristics and internal factors above average while they consider environmental and safety factors at the average level in the purchase of home and office furniture. The results of the Friedman test indicated the priorities as follows: vendor's characteristics, internal factors, corporate responsibility factors, product characteristics and safety and environmental characteristics. [ABSTRACT FROM AUTHOR]

In this study, it was aimed to determine the chicken meat consumption habits of students studying at Siirt University and the factors affecting these habits. The study material consists of data obtained by face to face survey method with a total of 217 associate and undergraduate students studying at Siirt University. The survey application was conducted in April-May 2019. When the preferred parts of chicken meat of the students were examined, the breast meat was the most preferred part with 34.46%, while the giblets part was the least preferred part with 1.87%. Chicken doner took first place with 64.4% in processed chicken meat preferences. The most important point when purchasing chicken meat was the expiration date with 62.92%. The most preferred places to buy chicken meat were supermarkets with 58.80%, while the least preferred places were grocery stores and neighborhood markets with 3.75%. It was determined that 68.5% of the university students participating in the survey knew the nutritional value of chicken meat, while 31.5% did not know its nutritive value. However, contrary to these results, 91.0% of the students think that hormones are used in chickens and 82.4% of them think that antibiotics are used as a growth promoter in chickens. When the results of the factors that cause changes in chicken meat consumption are examined according to gender and some knowledge levels; It was concluded that parameters such as the use of antibiotics in animals, TV-radio and newspaper news, the risk of animal diseases, low-fat rate, and the effect of the media in the poultry sector, cause significant (p

We introduce a novel projection mean variance (PMV) measure to construct a nonparametric test for the multisample hypothesis of equal distributions for univariate or multivariate responses. The proposed PMV measure generalizes the mean variance index using the projection technique. We obtain the theoretical properties of the PMV measure and its empirical counterpart. The PMV measure yields an analogous variance component decomposition. Using this decomposition, an ANOVA F statistic is derived to test the multisample problem. The proposed test is statistically consistent against the general alternatives and robust to heavytailed data. The test is free of tuning parameters and does not require moment conditions on the response. Our simulation results demonstrate that the PMV test has higher power than the classical Wilks-type methods and DISCO test, especially when the dimension of the response is relatively large or the moment conditions required by the DISCO test are violated. We further illustrate our method using empirical analyses of two real data sets. [ABSTRACT FROM AUTHOR]

Joint variations of precipitation (P) and temperature (T) under the influence of climate variability are examined in this study. Individual and joint influences of Atlantic multidecadal oscillation (AMO) and El Niño Southern Oscillation (ENSO) on P and T variability in a tropical region (i.e. Florida) are investigated. A rank-based association measure, nonparametric hypothesis tests, and Latin hypercube bootstrap sampling are used for assessments of hydroclimatic data at 78 observation stations. Spatially non-uniform influences of these two oscillations on the P-T associations are evident. While P-T associations were stronger during La Niña compared to El Niño, relatively lower precipitation is observed during La Niña. Temperature range-specific variations in the frequency and magnitude of precipitation are noted in two phases of AMO and ENSO. Climate variability has a stronger influence than local meteorological conditions in generating higher precipitation. The changes in P-T associations due to climate variability are mostly elevation invariant. [ABSTRACT FROM AUTHOR]

Background: Classic nonparametric tests (cNPTs), like Kruskal–Wallis or Mann–Whitney U, are sometimes used to detect differences in central tendency ( i.e., means or medians). However, when the tests’ assumptions are violated, such as in the presence of unequal variance and other forms of heteroscedasticity, they are no longer valid for testing differences in central tendency. Yet, sometimes researchers erroneously use cNPTs to account for heteroscedasticity. Objective: To document the appropriateness of cNPT use in obesity literature, characterize studies that use cNPTs, and evaluate the citation and public sharing patterns of these articles. Methods: We reviewed obesity studies published in 2017 to determine whether the authors used cNPTs: (1) to correct for heteroscedasticity (invalid); (2) when heteroscedasticity was clearly not present (correct); or (3) when it was unclear whether heteroscedasticity was present (unclear). Open science R packages were used to transparently search literature and extract data on how often papers with errors have been cited in academic literature, read in Mendeley, and disseminated in the media. Results: We identified nine studies that used a cNPT in the presence of heteroscedasticity (some because of the mistaken rationale that the test corrected for heteroscedasticity), 25 articles that did not explicitly state whether heteroscedasticity was present when a cNPT was used, and only four articles that appropriately reported that heteroscedasticity was not present when a cNPT was used. Errors were found in observational and interventional studies, in human and rodent studies, and only when studies were unregistered. Studies with errors have been cited 113 times, read in Mendeley 123 times, and disseminated in the media 41 times, by the public, scientists, science communicators, and doctors. Conclusions: Examples of inappropriate use of cNPTs exist in the obesity literature, and those articles perpetuate the errors via various audiences and dissemination platforms.

All study designs in medical research, barring descriptive studies, involve comparative analysis to achieve its objective/s. Comparisons between groups based on outcomes, or risk factors, or interventions, are the basis of hypotheses in evidence-based medicine. Statistical tests support in making criterion-based decisions about the validity of the hypothesis. A basic knowledge of statistical comparisons helps medical researchers to apply valid statistical tests. Novice medical researchers struggle to decide when, and which statistical test must be applied. There is a need to further reduce the mathematical jargon in statistics related publications for medical researchers. In this article, we aim to provide the readers with practical pointers about the applied aspects of basic statistical comparisons in medical research. [ABSTRACT FROM AUTHOR]

The reasons for buying online have occupied the literature of consumer behavior on the Internet for quite some time. Different theories seek to understand the relationships between different variables of purchasing behavior using this channel. However, the literature has not been sufficiently focused on making comparative analysis between countries, especially between emerging and Latin American countries. For this reason, a comparative study that seeks to understand differences between the reasons to purchase online by country. In 2017, we did survey 552 students, professors and administrative staff in two universities: one in Bogotá, Colombia, and one in Puebla, México using a convenience sampling. We designed a semi-structured questionnaire of 7 items, mostly categorical. Using nonparametric association tests, we discovered that in five of the six questions that are carried out there are significant statistical differences for the two countries such as why they buy in internet, payment methods, shipment, and why they would not buy in the internet . The implications for marketing for those companies interested in penetrating or improving their participation in these markets, is that they should be aware of their differences and use, depending on the country, adapted strategies.

Introduction In the pursuit of detecting the trend and the shift in trend in hydro-meteorological variables, various statistical methods have been developed and used over the years. Of the two methods commonly used (parametric and non-parametric), the non-parametric method has been favored over parametric methods. Long term trend analysis can reveal the beginning of the trend year, trend changes over time, and abrupt trend detection in a time-series. It is expected that the findings of this study will bring about more insights on understanding the regional hydrologic behavior over the last several decades in Iran. Methodology This paper analyzes the behavior of annual and seasonal temperatures, precipitation, and relative humidity. Datasets of 37 stations in Iran were analyzed from 1961 to 2010. The pre-whitening technique was used to eliminate the effect of the autocorrelation of data series. The Mann–Kendall (MK) test and the Sen's slope estimator were applied to quantify the significance of the trend and the magnitude of the trend at a 95% confidence level, respectively. The surface interpolation technique was used to prepare a spatial temperature, precipitation, and relative humidity data map over Iran from the point data measuring stations within the ArcGIS framework. For spatial distribution of trends in maps, contours are generated using an inverse-distance-weighted (IDW) algorithm.

Network inference algorithms are valuable tools for the study of large-scale neuroimaging datasets. Multivariate transfer entropy is well suited for this task, being a model-free measure that captures nonlinear and lagged dependencies between time series to infer a minimal directed network model. Greedy algorithms have been proposed to efficiently deal with high-dimensional datasets while avoiding redundant inferences and capturing synergistic effects. However, multiple statistical comparisons may inflate the false positive rate and are computationally demanding, which limited the size of previous validation studies. The algorithm we present—as implemented in the IDTxl open-source software—addresses these challenges by employing hierarchical statistical tests to control the family-wise error rate and to allow for efficient parallelization. The method was validated on synthetic datasets involving random networks of increasing size (up to 100 nodes), for both linear and nonlinear dynamics. The performance increased with the length of the time series, reaching consistently high precision, recall, and specificity (>98% on average) for 10,000 time samples. Varying the statistical significance threshold showed a more favorable precision-recall trade-off for longer time series. Both the network size and the sample size are one order of magnitude larger than previously demonstrated, showing feasibility for typical EEG and magnetoencephalography experiments.

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]

Many metaheuristic approaches are inherently stochastic. In order to compare such methods, statistical tests are needed. However, choosing an appropriate test is not trivial, given that each test has some assumptions about the distribution of the underlying data that must be true before it can be used. Permutation tests (P-Tests) are statistical tests with minimal number of assumptions. These tests are simple, intuitive and nonparametric. In this paper, we argue researchers in the field of metaheuristics to adopt P-Tests to compare their algorithms. We define two statistic tests and then present an algorithm that uses them to compute the p-value. The proposed process is used to compare 5 metaheuristic algorithms on 10 benchmark functions. The resulting p-values are compared with the p-values of two widely used statistical tests. The results show that the proposed P-test is generally consistent with the classical tests, but more conservative in few cases.

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]

The monthly and annual trends and variance of rainfall have been studied for five stations in an economically important Bangladeshi district named Sirajganj since 1965 to 2021. Natural disasters have prevalent in Sirajganj which is indispensable to assess. But, several researchers have been normally focused on river bank management and flood risk assessment. However, no extensive research has been conducted on Sirajganj based on non-normally distributed time series meteorological data such as rainfall time series so the current study is very important. In this study, the non-parametric Mann-Kendall and Sen's methods have been used to determine the statistical significance of a positive or negative trend in rainfall data. Also, cumulative sum charts and bootstrapping, one-way ANOVA, Tukey's range tests, and linear regression have been used to discover the incidence of abrupt changes, compare the significant difference in monthly and annual rainfall data, multiple comparisons amidst mentioned stations to find changes, and to investigate the changeover on dry and rainy days, respectively. The analysis showed a statistically significant decreasing trends in monthly and annual rainfall series. As well, changes from positive to negative direction have been recognized in the February, May, July, September, and annual rainfall time sequence. Besides, ANOVA and Tukey's range tests revealed a statistically substantial difference in all monthly and annual rainfall volume excluding January, March, and June. Additionally, these two tests demonstrated momentous differences in all monthly and annual frequency of rainfall categories excepting January and April. However, Linear regression analysis revealed that the number of dry days gradually reduced at the end of the dry winter, though the number of rainy days decreased during the rainy season. As in, the number of rainy days replaces the number of dry days during the dry season and vice versa during the rainy season. Even though, with very few exceptions, the volume of rainfall decreases throughout the year. The outcomes of this research might helpful for implementing the planning and evaluating hydrological projects on Sirajganj district., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

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.

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]

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.

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.

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]

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.

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]

In semiparametric and nonparametric statistical inference, the asymptotic normality of estimators has been widely established when they are √n-consistent. In many applications, nonparametric estimators are not able to achieve this rate. We have a result on the asymptotic normality of nonparametric M-estimators that can be used if the rate of convergence of an estimator is n-1/2 or slower. We apply this to study the asymptotic distribution of sieve estimators of functionals of a mean function from a counting process, and develop nonparametric tests for the problem of treatment comparison with panel count data. The test statistics are constructed with spline likelihood estimators instead of nonparametric likelihood estimators. The new tests have a more general and simpler structure and are easy to implement. Simulation studies show that the proposed tests perform well even for small sample sizes. We find that a new test is always powerful for all the situations considered and is thus robust. For illustration, a data analysis example is provided. [ABSTRACT FROM AUTHOR]

Cuckoo search (CS) is one of the well-known evolutionary techniques in global optimization. Despite its efficiency and wide use, CS suffers from premature convergence and poor balance between exploration and exploitation. To address these issues, a new CS extension namely snap-drift cuckoo search (SDCS) is proposed in this study. The proposed algorithm first employs a learning strategy and then considers improved search operators. The learning strategy provides an online trade-off between local and global search via two snap and drift modes. In snap mode, SDCS tends to increase global search to prevent algorithm of being trapped in a local minima; and in drift mode, it reinforces the local search to enhance the convergence rate. Thereafter, SDCS improves search capability by employing new crossover and mutation search operators. The accuracy and performance of the proposed approach are evaluated by well-known benchmark functions. Statistical comparisons of experimental results show that SDCS is superior to CS, modified CS (MCS), and state-of-the-art optimization algorithms in terms of convergence speed and robustness. [ABSTRACT FROM AUTHOR]

The election of the smoothing parameter or bandwidth is, probably, the most important concern in the statistical smoothed techniques. The relevance of this parameter, on the obtained results difficult, the use of these methods in statistical inference, because the final decision could be determined for the used bandwidth. The main goal of this paper is discussing and studying some algorithms for the automatic computation of the bandwidth in k–sample problems.Una de las principales limitaciones de las técnicas de suavizamiento es la necesidad de elegir un parámetro de suavizado o ventana. La influencia de este parámetro sobre los resultados obtenidos obliga a que el uso de estos métodos en inferencia sea delicado, ya que la decisión final puede verse determinada por la elección del parámetro. El objetivo principal de este trabajo es el estudio de algunos algoritmos para el cálculo automático del parámetro ventana en problemas de contrastes de hipótesis para la igualdad de k poblaciones independientes.