Nonparametric Sign Test Research Articles

Model-free feature screening for categorical outcomes: Nonlinear effect detection and false discovery rate control.

Feature screening has become a real prerequisite for the analysis of high-dimensional genomic data, as it is effective in reducing dimensionality and removing redundant features. However, existing methods for feature screening have been mostly relying on the assumptions of linear effects and independence (or weak dependence) between features, which might be inappropriate in real practice. In this paper, we consider the problem of selecting continuous features for a categorical outcome from high-dimensional data. We propose a powerful statistical procedure that consists of two steps, a nonparametric significance test based on edge count and a multiple testing procedure with dependence adjustment for false discovery rate control. The new method presents two novelties. First, the edge-count test directly targets distributional difference between groups, therefore it is sensitive to nonlinear effects. Second, we relax the independence assumption and adapt Efron’s procedure to adjust for the dependence between features. The performance of the proposed procedure, in terms of statistical power and false discovery rate, is illustrated by simulated data. We apply the new method to three genomic datasets to identify genes associated with colon, cervical and prostate cancers.

Optimization of peak area precision of a GC–MS drug screening method using a nonparametric sign test

The optimization of a number ofgas chromatography-mass spectrometry (GC-MS) parameters in order to improve peak area precision through the application of a nonparametric statistical test (the sign ...

A variables neighborhood search algorithm for solving fuzzy quadratic programming problems using modified Kerre’s method

To solve a fuzzy optimization problem, we need to compare fuzzy numbers. Here, we make use of our recently proposed modified Kerre’s method as an effective approach for comparison of LR fuzzy numbers. Using our new results on LR fuzzy numbers, we show that to compare two LR fuzzy numbers, we do not need to compute the fuzzy maximum of two numbers directly. We propose a new variable neighborhood search approach for solving fuzzy number quadratic programming problems by using the modified Kerre’s method. In our algorithm, a local search is performed using descent directions, found by solving five crisp mathematical programming problems. In several available methods, a fuzzy optimization problem is converted to a crisp problem, but in our proposed approach, using our modified Kerre’s method, the fuzzy optimization problem is solved directly, without changing it to a crisp program. We give some examples to compare the performance of our proposed algorithm with some available methods and show the effectiveness of our proposed algorithm by using the nonparametric statistical sign test.

Problem-Solving Among English Language Learners: A Cognitive Linguistic Approach

New mathematics standards ask teachers to strengthen mathematics instruction while still building upon communication skills. In today's classroom, this is complicated by the growing number of English language learners (ELLs) across the country who because they are still learning English struggle with the language of mathematics. Researchers who have addressed the question of problem-solving among ELLs have explored schema-based instruction or the use of math journals. Of these two approaches, a discussion of the ELL population is not included in research on math journals and problem solving. Within the research on schema-based instruction, research limit their findings to ELLs with math difficulties (MD). This study addresses the gap in the research on problem-solving among ELLs. A writing structure referred to as Source, Path, Goal (SPG) was used as a linguistic scaffold and type of schema-based instruction. Instruction was set in an elementary ELL classroom. Each group received a different level of scaffolded instruction: 1) instruction only treatment group, 2) instruction plus practice treatment group. Three non-parametric sign tests were conducted (one for each group) to compare pre- and post-test results and indicated strong support for the use of scaffolding plus practice. An analysis of the students’ written explanations of how they solved their math problems only indicated an increase in problem-solving skills for instruction only treatment group, but both treatment groups increased in the depth of their mathematical thinking. Implications for practice and future research are shared.

Examining the Effects of Narrative Commentary on Evaluators' Summative Assessments of Resident Performance.

Anchor-based, end-of-shift ratings are commonly used to conduct performance assessments of resident physicians. These performance evaluations often include narrative assessments, such as solicited or "free-text" commentary. Although narrative commentary can help to create a more detailed and specific assessment of performance, there are limited data describing the effects of narrative commentary on the global assessment process. This single-group, observational study examined the effect of narrative comments on global performance assessments. A subgroup of the clinical competency committee, blinded to resident identity, assigned a single, consensus-based performance score (1-6) to each resident based solely on end-of-shift milestone scores. De-identified narrative comments from end-of-shift evaluations were then included and the process was repeated. We compared milestone-only scores to milestone plus narrative commentary scores using a nonparametric sign test. During the study period, 953 end-of-shift evaluations were submitted on 41 residents. Of these, 535 evaluations included free-text narrative comments. In 17 of the 41 observations, performance scores changed after the addition of narrative comments. In two cases, scores decreased with the addition of free-text commentary. In 15 cases, scores increased. The frequency of net positive change was significant (p = .0023). The addition of narrative commentary to anchor-based ratings significantly influenced the global performance assessment of Emergency Medicine residents by a committee of educators. Descriptive commentary collected at the end of shift may inform more meaningful appraisal of a resident's progress in a milestone-based paradigm. The authors recommend clinical training programs collect unstructured narrative impressions of residents' performance from supervising faculty.

Qualitative Coding Boot Camp: An Intensive Training and Overview for Clinicians, Educators, and Administrators

Qualitative coding is a tool for analyzing data involving strings of meaningful words. While many schools and universities have staff who can assist faculty with quantitative data analysis, qualitative data analysis is interpretive and requires both content-specific knowledge and research methodology tools. In this qualitative coding boot camp, we introduce clinician-educators, staff, and administrators to a general overview of qualitative coding and analysis. We designed and implemented an in-person training to help researchers who had limited exposure to qualitative research gain a general orientation to it. We provided an overview of qualitative data collection and qualitative coding and developed focused research questions related to sample interviews for participants to use in working together to develop a codebook. We concluded by discussing the iterative process of coding, how to work from codes to themes for a manuscript, and how to present and disseminate results. To examine participants' learning during the boot camp session, we used a series of nonparametric sign tests to compare pre- and postsession responses on our evaluation form. The results of these tests showed significant growth in participant comfort with undertaking qualitative analysis. Qualitative coding is an important skill for clinicians and their research teams to have, as it can help them to understand the experiences of those around them through an empirical lens. With this 2-hour training, we were able to increase participants' comfort level with the set of skills required to analyze qualitative data rigorously.

Application of a density based clustering technique on biomedical datasets

The detection of the number of clusters in a biomedical dataset is very important for generating high quality clusters from the biomedical dataset. In this paper, we aim to evaluate the performance of a density based K-Means clustering technique called DenClust on biomedical datasets. DenClust produces the number of clusters and the high quality initial seeds from a dataset through a density based seed selection approach without requiring an user input on the number of clusters and the radius of the clusters. The high quality initial seeds for K-Means results in high quality clusters from a dataset. The performance of DenClust is compared with six other existing clustering techniques namely CRUDAW-F, CRUDAW-H, AGCUK, GAGR, K-Means, and K-Means++ on the twenty biomedical datasets in terms of two external cluster evaluation criteria namely Entropy and Purity and one internal cluster evaluation criteria called Sum of Squared Error (SSE). We also perform a statistical non-parametric sign test on the cluster evaluation results of the techniques. Both the cluster evaluation results and statistical non-parametric sign test results indicate the superiority of DenClust over the existing techniques on the biomedical datasets. The complexity of DenClust is O(n2) but the overall execution time of DenClust on the datasets is less than the execution time of AGCUK and GAGR having O(n) complexity.

E-BRM: A constraint handling technique to solve optimization problems with evolutionary algorithms

This work presents an enhanced technique to handle constraints in optimization problems solved by evolutionary algorithms: the extended balanced ranking method (E-BRM). It comprises a self-adaptive procedure that deals with two ranking lists, for feasible and infeasible solutions, which are merged during the search process according to the amount of feasible and infeasible candidates within the population.The method is designed to work uncoupled from the optimization algorithm, thus being suitable for implementation along with distinct algorithms. Therefore, to evaluate the proposed constraint-handling technique (CHT), and compare its performance with other CHTs that follow the same uncoupled approach, we have implemented them all into a canonical Genetic Algorithm (GA). Several tests were executed over well-known suites of benchmark functions and also engineering problems. The results provided by the different CHTs are compared not only by direct comparisons, but also by nonparametric statistical Sign test, and Wilcoxon Signed-Rank test. Those comparisons indicate that the E-BRM presented an improved overall performance.

An examination of the perceived impact of a continuing interprofessional education experience on opiate prescribing practices

ABSTRACTChronic pain is increasingly recognized as a public health problem. We assessed the effectiveness of a multi-modal, interprofessional educational approach aimed at empowering healthcare professionals to make deliberative changes, especially in opiate prescribing practices. Education activities included enduring webcasts, regional interprofessional roundtable events, and state-level conference presentations within targeted Kentucky and West Virginia regions of the United States. Over 1,000 participants accessed the various activities. For the live events, the largest groups reached included nurses (38.1%), nurse practitioners (31.2%), and physicians (22.1%). In addition to our reach, higher levels of educational effectiveness were measured, specifically, learner’s intentions to change practice patterns, confidence in meeting patient’s needs, and knowledge of pain management guidelines. The majority of the conference (58%) and roundtable (69%) participants stated they intend to make a practice change in one or more areas of chronic pain patient management in post-event evaluation. Differences in pre- and post-activity responses on the measures of confidence and knowledge, with additional comparison to a control population who were not in attendance, were analyzed using non-parametric tests of significance. While neither activity produced significant changes in confidence from pre-activity, participants were more confident post-activity than their control group peers. There were significant changes in knowledge for both live event and webcast participants. Impactful chronic pain continuing the education that emphasizes collaborative care is greatly needed; these results show that the approaches taken here can impact learner’s knowledge and confidence, and hold potential for creating change in how opioid prescribing is managed.

Fake News and Indifference to Truth: Dissecting Tweets and State of the Union Addresses by Presidents Obama and Trump

State of the Union Addresses (SOUA) by two recent US Presidents, President Obama (2016) and President Trump (2018), and a series of recent of tweets by President Trump, are analysed by means of the data mining technique, sentiment analysis. The intention is to explore the contents and sentiments of the messages contained, the degree to which they differ, and their potential implications for the national mood and state of the economy. President Trump's 2018 SOUA and his sample tweets are identified as being more positive in sentiment than President Obama's 2016 SOUA. This is confirmed by bootstrapped t tests and non-parametric sign tests on components of the respective sentiment scores. The issue of whether overly positive pronouncements amount to self-promotion, rather than intrinsic merit or sentiment, is a topic for future research.

ФОРМУВАННЯ У МАЙБУТНІХ УЧИТЕЛІВ УМІНЬ РАЦІОНАЛЬНО ОБИРАТИ ПРОГРАМНИЙ ЗАСІБ: ПРАКСЕОЛОГІЧНИЙ ПІДХІД

From positions of praxeology the aspects of the forming of the future teachers’ abilities to choose among the variety of software an exactly that one that turns out to be the most rational for the solving of the professional task are considered in the article (on the example of informatics teachers’ preparation to use dynamic mathematics software (DMS). The technology of forming of such abilities that is based on a formula "one task – different software" is described. The methodology of organization of such experimental studies on the base of Sumy State Pedagogical University (Ukraine) is described. The results of the statistical analysis of data are given on the basis of non-parametric sign test for dependent sample. It is educed that taking into account of such approach provides the positive dynamics of level of future informatics teachers’ preparation at the significance level of 0.05.

Nonparametric Significance Test for Discovery of Network‐Constrained Spatial Co‐Location Patterns

Spatial co‐location patterns are useful for understanding positive spatial interactions among different geographical phenomena. Existing methods for detecting spatial co‐location patterns are mostly developed based on planar space assumption; however, geographical phenomena related to human activities are strongly constrained by road networks. Although these methods can be simply modified to consider the constraints of networks by using the network distance or network partitioning scheme, user‐specified parameters or priori assumptions for determining prevalent co‐location patterns are still subjective. As a result, some co‐location patterns may be wrongly reported or omitted. Therefore, a nonparametric significance test without priori assumptions about the distributions of the spatial features is proposed in this article. Both point‐dependent and location‐dependent network‐constrained summary statistics are first utilized to model the distribution characteristics of the spatial features. Then, by using these summary statistics, a network‐constrained pattern reconstruction method is developed to construct the null model of the test, and the prevalence degree of co‐location patterns is modeled as the significance level. The significance test is evaluated using the facility points‐of‐interest data sets. Experiments and comparisons show that the significance test can effectively detect network‐constrained spatial co‐location patterns with less priori knowledge and outperforms two state‐of‐the‐art methods in excluding spurious patterns.

Non-interactive approach to solve multi-objective thermal power dispatch problem using composite search algorithm

This paper deals with a non-interactive approach to solve multi-objective thermal power load dispatch (MTPLD), where either decision maker is not involved or preference information is available in prior. To reduce the computational complexities due to generation of Pareto-front and selection of satisficing solution, this paper adopts no-preference approach. A satisficing function to resolve the conflict of non-commensurable objectives is proposed, which reformulates the MTPLD problem as scalar thermal power load dispatch (MTPLD) problem. Owing to ambiguous or vague in objectives, the proposed method exploits fuzzy theory. MTPLD problems’ satisfying solution is obtained by implementing hybrid chaotic differential evolution algorithm and Powell’s pattern search algorithm (CDEPS). The chaotic differential evolution algorithm is responsible for the diversification of feasible solutions and provides global solution. Whereas Powell’s pattern search, method improves the exploitation by performing local search. The paper investigates the performance of two CDEPS variants based on Gauss map and Tent map, respectively. The performance of the proposed solution procedure is analyzed using generalized benchmark test functions and complex MTPLD problems. The exhaustive analysis using non-parametric significance test and descriptive statistics shows that the Tent map based CDEPS solution procedure has better ability to generate quality generation schedule and faster convergence rate.

Fake News and Indifference to Truth: Dissecting Tweets and State of the Union Addresses by Presidents Obama and Trump

State of the Union Addresses (SOUA) by two recent US Presidents, President Obama (2016) and President Trump (2018), and a series of recent of tweets by President Trump, are analysed by means of the data mining technique, sentiment analysis. The intention is to explore the contents and sentiments of the messages contained, the degree to which they differ, and their potential implications for the national mood and state of the economy. President Trump's 2018 SOUA and his sample tweets are identified as being more positive in sentiment than President Obama's 2016 SOUA. This is confirmed by bootstrapped t tests and non-parametric sign tests on components of the respective sentiment scores. The issue of whether overly positive pronouncements amount to self-promotion, rather than intrinsic merit or sentiment, is a topic for future research.

Fake News and Indifference to Truth: Dissecting Tweets and State of the Union Addresses by Presidents Obama and Trump

State of the Union Addresses (SOUA) by two recent US Presidents, President Obama (2016) and President Trump (2018), and a series of recent of tweets by President Trump, are analysed by means of the data mining technique, sentiment analysis. The intention is to explore the contents and sentiments of the messages contained, the degree to which they di_er, and their potential implications for the national mood and state of the economy. President Trump's 2018 SOUA and his sample tweets are identi_ed as being more positive in sentiment than President Obama's 2016 SOUA. This is con_rmed by bootstrapped t tests and non-parametric sign tests on components of the respective sentiment scores. The issue of whether overly positive pronouncements amount to self-promotion, rather than intrinsic merit or sentiment, is a topic for future research.

MEASURING VOLATILITY SPILLOVERS BETWEEN DEVELOPED AND SOUTHEAST ASIAN EMERGING STOCK MARKETS: A MULTIVARIATE GARCH APPROACH

In this paper, we measure volatility spillovers among eleven stock markets, including five developed markets (the United States, Japan, Germany, the United Kingdom, Hong Kong) and six Southeast Asian developing markets (Indonesia, Malaysia, Philippines, Singapore, Thailand and Vietnam) over the 25-year period from January 1, 1993 to December 31, 2017. Employing the GARCH-DCC model and non-parametric sign tests on the correlations between developed markets and emerging markets, we find that correlations between developed markets and the Southeast Asian markets have risen sharply during periods of crisis, indicating the existence of volatility spillover effects from the developed markets to emerging ones. Full sample analysis suggests that volatility spillover from Japanese and the UK markets to the Southeast Asian emerging markets is stronger and more apparent than those transmitted from the US and Germany markets. Sub-sample analysis is able to identify the markets transmitting shocks to others. Results also suggest that Vietnam market is not fully integrated to the regional and global markets.

Nonparametric phenotypic stability analysis in advanced barley (Hordeum vulgare L.) genotypes

The development of genotypes with adaptation to a wide range of environments is one of the most important goals of plant breeding programs. In order to compare nonparametric stability measures and to identify promising high-yield and stable barley (Hordeum vulgare L.), 20 barley genotypes selected from the Iran/ICARDA joint project and grown in nine environments during 2009-11 in Iran. Four nonparametric statistical tests of significance for genotype × environment (GE) interaction and 10 nonparametric measures of stability were used to identify stable genotypes in nine environments. Results of nonparametric tests of G×E interaction (Kubinger, Hildebrand, and Kroon/ Laan) and a combined ANOVA across environments, indicated the presence of both crossover and non-crossover interactions. Also, only TOP and rank-sum values were positively associated with high yield. Thus, in the simultaneous selection for high yield and stability, only the rank-sum and TOP methods were useful in terms of the principal component analysis results, and correlation analysis of nonparametric stability statistics and yield. According to these stability parameters (TOP and rank-sum), three genotypes (G13, G12, and G17) were the most stable for grain yield. The results also revealed that based on nonparametric test results, stability could be classified into three groups, according to agronomic and biological concepts of stability.

Selected Statistical Tests for Median and Their Properties

In the paper, a selection of statistical tests for median are presented. In particular, parametric and nonparametric significance tests are considered. In the case of parametric tests the critical regions are constructed on the basis of the known population distribution and the form of the alternative hypothesis. For chosen distributions the critical values are presented. In the case of nonparametric tests we consider tests for which the sample median dispersion is estimated based on order statistics of appropriate ranks. The use of the bootstrap method for the median dispersion estimation in the test statistic construction is the author’s own proposal. The simulation analysis of the nonparametric tests’ properties allows to compare these tests with each other, showing better results for the bootstrap variant, especially for small samples.

Adaptive detection of statistically significant regional spatial co-location patterns

Regional spatial co-location patterns refer to subsets of spatial features that often co-occur in close geographical proximity in certain localities of space. Discovering regional spatial co-location patterns is still very challenging because it is difficult to specify appropriate thresholds for prevalence measures without prior knowledge and to detect natural localities of regional spatial co-location patterns automatically. On that account, an adaptive method is proposed in this study. First, a non-parametric significance test is constructed to evaluate the prevalence of spatial co-location patterns. Then, an adaptive pattern clustering approach is developed to detect hotspots of each candidate regional spatial co-location pattern. Finally, all statistically significant regional spatial co-location patterns and their localities are detected by iteratively expanding these hotspots. Comparisons between this adaptive method and two state-of-the-art methods are carried out with both simulated and ecological datasets (i.e. a wetland species dataset in northeast China). Experiments show that the proposed adaptive method allows detecting regional spatial co-location patterns effectively and with less prior knowledge than the state-of-the-art methods.

Does government transparency contribute to improved eco-efficiency performance? An empirical study of 262 cities in China

Considering the properties of environment from the perspective of a public good, this paper aims to examine the relationship between government transparency and eco-efficiency performance (EEP). By applying a conditional slack-based measure (SBM) model, local linear regression (LLR) and a nonparametric significance test, we conduct an empirical analysis based on 262 cities in China during the period of 2005–2012. The results demonstrated that: (1) the overall eco-efficiency of China was low due to the fact that the EEP scores of most of the cities were below 0.5; (2) the eco-efficiency performances of a majority of the municipalities & sub-provincial (M&S) cities were better than those of the prefecture-level cities, and the former showed greater improvements than the latter on eco-efficiency performances, although the M&S cities experienced faster paces of urbanization and consumed more resources; (3) there exists a nonlinear relationship between government transparency and eco-efficiency performance. In other words, if the government information was more transparent, it would be more beneficial to the improvement of eco-efficiency, and the phenomena were more significant in M&S cities such as Shanghai, Beijing and Guangzhou. Policy implications are thus drawn in this paper for promoting eco-efficiency improvement in China's M&S cities and prefecture-level cities.