Search

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]

Data depth based nonparametric tests for multivariate multisample location problem are provided which extend the depth based nonparametric test for multivariate two-sample location problem by Shirke and Khorate (2018) [Shirke, D.T. and Khorate, S.D., 2018. Two-sample nonparametric test for testing equality of locations based on data depth. Journal of the Indian Society for Probability and Statistics, 19, pp. 9-23.] to the multi-sample setting. Proposed tests are based on the correlations among the depths of data points. Power comparison of proposed tests with the existing depth based nonparametric tests is provided using a simulation study based on a permutation test. Simulation study reveals that the proposed tests give comparable powers compared to the existing data depth based nonparametric tests. Proposed tests are illustrated with real life data sets. [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.

We propose a new methodology to asses risk spillovers in a time-series framework. Firstly, we introduce an explicit nonparametric measure of cross-sectional conditional tail co-movement, which is intuitively comparable to the Conditional Value-at-Risk (CoVaR). We show that nonlinear CoVaR (NCoVaR) is able to capture even highly nonlinear dependence structures. Secondly, for the purpose of potential contagion analysis, we adapt the measure to be informative about the causality direction between the variables in the Granger causality sense. By showing that the natural estimators of the two metrics are U-statistics, we construct formal nonparametric tests for independence and Granger non-causality. Numerical simulations confirm that in common situations the nonparametric tests have better size and power properties than their parametric counterparts. The methodology is illustrated empirically by assessing risk transmissions between sovereigns and banking sectors in the euro area, which observed highly irregular co-movements between asset prices after the global financial crisis. The new measures seem to be less susceptible to these irregularities than their parametric analogues, providing a clearer overview of the underlying sovereign-bank risk feedback loops. [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.

The evaluation of the possible effects of a treatment on an outcome plays a central role in both theoretical and applied statistical and econometrical literature. This paper focuses on nonparametric tests for possible difference in the distribution of potential outcomes due to receiving or not receiving a treatment. The approach is based on weighting observed data on the basis on the estimated propensity score. Kolmogorov–Smirnov type and Wilcoxon–Mann–Whitney type tests are constructed, and their limiting distributions are studied. Rejection regions are obtained by inverting confidence intervals. This involves the study of appropriate estimators of the limiting variance of test statistics. Approximations of quantiles via subsampling are also considered. The merits of the different tests are studied by Monte Carlo simulation. An application to the construction of tests for stochastic dominance is provided. [ABSTRACT FROM AUTHOR]

Missing data is a common issue in many biomedical studies. Under a paired design, some subjects may have missing values in either one or both of the conditions due to loss of follow-up, insufficient biological samples, etc. Such partially paired data complicate statistical comparison of the distribution of the variable of interest between the two conditions. In this article, we propose a general class of test statistics based on the difference in weighted sample means without imposing any distributional or model assumption. An optimal weight is derived from this class of tests. Simulation studies show that our proposed test with the optimal weight performs well and outperforms existing methods in practical situations. Two cancer biomarker studies are provided for illustration. [ABSTRACT FROM AUTHOR]

We propose a nonparametric test for cooperative behaviour among players in discrete, static games. Assuming that certain exchangeability conditions hold if we match observable characteristics across all players, we obtain testable implications for cooperative (coalitional) behaviour, which we define as occurring when players maximise an unknown joint objective function that is symmetric conditional on the matching. Cooperation implies the existence of a class of outcomes |$\mathcal {Y}$| such that, conditional on the matching, the probability of observing an outcome |$y\in \mathcal {Y}$| must be equal to the probability of observing any permutation of y. We present a nonparametric econometric test and we characterise its asymptotic properties. We apply our test to expansion/entry decisions of Lowe's and Home Depot in the contiguous United States and we find that, while outcomes are consistent with noncooperative behaviour in larger markets, we fail to reject cooperation in smaller markets. [ABSTRACT FROM AUTHOR]

Although Phase I analysis of multivariate processes has been extensively discussed, the discussion on techniques for Phase I monitoring of high‐dimensional processes is still limited. In high‐dimensional applications, it is common to observe that a large number of components but only a limited number of them change at the same time. The shifted components are often sparse and unknown a priori in practice. Motivated by this, this article studies Phase I monitoring of high‐dimensional process mean vectors under an unknown sparsity level of shifts. The basic idea of the proposed monitoring scheme is to first employ the false discovery rate procedure to estimate the sparsity level of mean shifts, and then to monitor the mean changes based on the maximum of the directional likelihood ratio statistics over all the possible shift directions. The comparison results based on extensive simulations favor the proposed monitoring scheme. A real example is presented to illustrate the implementation of the new monitoring scheme. [ABSTRACT FROM AUTHOR]

Traditional control charts are based on the assumption that the process observations are normally distributed. However, in many applications, there is insufficient information to justify this assumption. Thus, nonparametric control charts have been designed in literature to monitor location parameter and scale parameter of a process. In this paper, a single nonparametric control chart based on modified Lepage test is proposed for simultaneously monitoring of location and scale parameters of any continuous process distribution. The charting statistic combines two nonparametric test statistics namely Baumgartner test for location and Ansari-Bradely test for scale. The performance of the proposed chart is examined through simulation studies in terms of the mean, the standard deviation, the median and some percentiles of the run length distribution. The average run length (ARL) performance of the proposed chart is compared with that of the existing nonparametric Shewhart-Cucconi (SC) and Shewhart-Lepage (SL) charts for joint monitoring of location and scale. [ABSTRACT FROM AUTHOR]

In spatial statistics, point processes are often assumed to be isotropic meaning that their distribution is invariant under rotations. Statistical tests for the null hypothesis of isotropy found in the literature are based either on asymptotics or on Monte Carlo simulation of a parametric null model. Here, we present a nonparametric test based on resampling the Fry points of the observed point pattern. Empirical levels and powers of the test are investigated in a simulation study for four point process models with anisotropy induced by different mechanisms. Finally, a real data set is tested for isotropy. [ABSTRACT FROM AUTHOR]

A necessary and sufficient condition for Pitman's asymptotic relative efficiency of the Kendall and Spearman correlation statistics for the independence test to be 1 is given, in terms of certain smoothness and nondegeneracy properties of the model. Corresponding easy-to-use and broadly applicable sufficient conditions are obtained. These conditions hold for most well-known models of dependence. [ABSTRACT FROM AUTHOR]

CCTRN is a Cardiovascular Cell Therapy Research Network. There were three randomized double blinded controlled stem cell clinical trials conducted in its first phase. The main results of these three clinical trials were published with conventional parametric models such as T test and nonparametric test such as Wilcoxon rank sum test without adjusting covariates. In this article, we conducted further analysis of the primary outcomes of these studies using a class of covariate adjusted nonparametric methods. [ABSTRACT FROM AUTHOR]

Motivated by applications in high-dimensional settings, we propose a novel approach for testing the equality of two or more populations by constructing a class of intensity centered score processes. The resulting tests are analogous in spirit to the well-known class of weighted log-rank statistics that are widely used in survival analysis. The test statistics are nonparametric, computationally simple, and applicable to high-dimensional data. We establish the usual large sample properties by showing that the underlying log-rank score process converges weakly to a Gaussian random field with zero mean under the null hypothesis and with a drift under the contiguous alternatives. For the Kolmogorov–Smirnov-type and the Cramer-von Mises-type statistics, we also establish the consistency result for any fixed alternative. Cutoff points for the test statistics are obtained by permutations or a simulation-based resampling method. The new approach is applied to a study of brain activation measured by functional magnetic resonance imaging when performing two linguistic tasks and also to a prostate cancer DNA microarray dataset. for this article are available online. [ABSTRACT FROM AUTHOR]

A three-sample distribution-free test for detecting non-decreasing ordered alternatives is proposed to alleviate some of the problems in the existing tests, including the risk of frequent detection of a trend when it is not actually present. The test is based on the natural U-statistic estimation of the parameter γ = P (Y 1 ≤ Y 2 ≤ Y 3) where Y 1 , Y 2 , Y 3 are three independent continuous random variables. Actual levels of the test and power values in the case of equal variances for a variety of sample sizes and over a wide class of distributions under the null hypothesis and the alternative are obtained via simulation. The results indicate that the proposed test compares favorably with existing tests. In all simulations the nonparametric test provided relatively good power, accurate control over the size of the test, and better protection against false detection of a non-existing trend. [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. [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]

This paper examines the impact of Kenyan elections and election-related events on tourism, financial and media sector stock returns using an event study approach. Daily prices of selected equities are obtained from the Thompson Reuters EIKON and parametric and non-parametric tests carried out. The results indicate that conflict negatively affects tourism sector more than the financial and media sectors, while peaceful elections have a positive effect on tourism and to a smaller extend, the financial sector. Peace initiatives induce a positive effect on tourism. A robust and independent IEBC capable of delivering a credible election is therefore crucial. [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]

Copyright of Finance & Business Economies Review is the property of Finance & Business Economies Review 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.)

Evolutionary algorithms (EAs) are successfully employed to solve complex optimization problems such as network design problems, path finding problems, scheduling problems and social and economic planning. By using intelligent strategies, EAs iteratively improve the initial solution to produce new solutions. Particularly in genetic algorithms (GAs), one of the most known EAs, selection mechanism, has crucial importance due to its effect on the diversity of a population and convergence ability to optimal solution. As most of the selection methods of GAs favor the good solutions for reproductions, average or poor solutions are overlooked. Recently, we proposed a new selection method, called Bipolar Mating Tendency (BMT), that is based on Standard Tournament (ST) selection. Unlike the state-of-the-art selection methods of GAs, BMT diversifies the mating process with the inclusion of less favorable solutions. In this paper, the performance of BMT was compared with the performance of ST and prevalent selection methods that are also based on ST: restricted tournament selection, unbiased tournament selection, fine-grained tournament selection and cooperative selection. For the comparison, twenty-one well-known benchmark optimization functions and sixteen IEEE CEC 2018 test functions were utilized. Also, nonparametric statistical tests were performed to demonstrate the significance of the results. In order to test the effectiveness of GA-BMT (GA that uses BMT as a selection method), three real-world engineering problems were used. In addition to showing predominant results comparing to the other methods in test functions, BMT has also provided successful results in the problems. [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]

Professional ice hockey provides a great environment for studying the antifragile behaviour of teams because of publicly available results and statistics. This study examines three-goal events in which a team gave up a goal but responded by scoring two goals. Thirty-four ice hockey games are studied from the last ten seasons of the Hungarian first league to identify the events' characteristics and to determine whether antifragile behaviour emerged in these events. The results indicate that if the opponent scores first and has a one- or two-goal lead, the team that responds with two goals after strengthening the line exhibits a convex and, therefore, an antifragile behaviour. Antifragility has been found in 22 cases, lending support to the assumption that antifragile behaviour emerges from high-level cooperation. [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]

Proton exchange membrane fuel cells (PEMFCs) have been known to be a feasible method for sustainable power generation to meet the ever‐increasing electricity demand with no greenhouse gases emission. Thus, under varying operating circumstances, the PEMFCs have gained considerable importance as an alternative energy source. The optimal operation of PEMFC needs to be ensured to exploit its advantages to the maximum limit. In this direction, there is an extensive need for parameter extraction of PEMFC. Many researchers have applied evolutionary optimization approaches to estimate the PEMFCs parameters as the precise modeling of these cells are not possible. To optimize unknown parameters of PEMFCs, a metaheuristic algorithm is proposed, that is, chaotic mayfly algorithm (CMA) is being proposed in this manuscript. The model's statistical effects are more aligned with the actual experimental findings, according to an experimental finding. The results so obtained suggest that CMA variants are a valuable and efficient method of estimating the parameters of PEMFCs models. Even the nonparametric tests like the Friedman ranking test, Wilcoxon's rank‐sum test, and Mood's median test suggest that the proposed algorithm shows better accuracy of the extracted parameters as compared to the other algorithms namely considered in this work. [ABSTRACT FROM AUTHOR]

Recent replication crisis has led to a number of ad hoc suggestions to decrease the chance of making false positive findings. Among them, Johnson (Proceedings of the National Academy of Sciences, 110, 19313–19317, 2013) and Benjamin et al. (Nature Human Behaviour, 2, 6–10 2018) recommend using the significance level of α = 0.005 (0.5%) as opposed to the conventional 0.05 (5%) level. Even though their suggestion is easy to implement, it is unclear whether or not the commonly used statistical tests are robust and/or powerful at such a small significance level. Therefore, the main aim of our study is to investigate the robustness and power curve behaviors of independent (unpaired) two-sample tests for metric and ordinal data at nominal significance levels of α = 0.005 and α = 0.05. Through an extensive simulation study, it is found that the permutation versions of the Welch t-test and the Brunner-Munzel test are particularly robust and powerful while the commonly used two-sample tests which utilize t-distribution tend to be either liberal or conservative, and have peculiar power curve behaviors under skewed distributions with variance heterogeneity. [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]

Summary For the modeling and analysis of hydrogen fuel cells, reliable kinetic models are of utmost important. Proton exchange membrane fuel cell (PEMFC) is a complicated, multivariable device. Mathematical simulation of the PEMFC typically results in nonlinear parameter estimation issues, often involving more than one local minima. This paper proposes to solve the PEMFC model estimation issues with a slime mould (SM)‐based optimization algorithm. Findings on several benchmark test functions show that, the SMA outperforms the other rest of the compared algorithms. Furthermore, an experimental finding indicates that the predictive outcomes of the model are in better alignment with the real experimental results. The SMA therefore is a valuable and effective method for estimating the parameters of PEMFC models and can be used for other specific fuel cell issues. After estimating the parameter of fuel cell, nonparametric test is obtained and from this test it is justified that the SMA is better than the rest of the compared algorithms. [ABSTRACT FROM AUTHOR]

The present work aims to study the climate variability as well as the rainfall and temperature trends; it also tries to examine their impact on the annual runoff along with the piezometric level of aquifers in north-western Algeria. The time series that were recorded and analyzed during the period extending from 1927 to 2012 concerned four subwatersheds that belong to the central coastal basin of Oran. Some nonparametric tests (at the 5% level) were carried out; they allowed detecting significant decreasing rainfall and runoff trends and strongly increasing temperature trend, during the period between 1927 and 2012 in the four subbasins. In addition, the breakup dates were detected in 1975 and 1981 for the rainfall time series, with a decrease between 24 and 39 %; the runoff time series were detected 1974 and 1975, in with a drop between 52 % and 70 %; the temperature time series were observed in 1985 with an increase of about 5%. In addition to the agricultural and industrial activities, the rainfall decrease and temperature increase also affected the piezometric level of the aquifers in the western watershed under study. Indeed, this level diminished significantly from 1 to −130m, after the break date (tipping point) in the rainfall time series. [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.