A Nonparametric Procedure to Assess the Accuracy of the Normality Assumption for Annual Rainfall Totals, Based on the Marginal Statistics of Daily Rainfall: An Application to the NOAA/NCDC Rainfall Database

We develop a nonparametric procedure to assess the accuracy of the normality assumption for annual rainfall totals (ART), based on the marginal statistics of daily rainfall. The procedure is addressed to practitioners and hydrologists that operate in data-poor regions. To do so we use 1) goodness-of-fit metrics to conclude on the approximate convergence of the empirical distribution of annual rainfall totals to a normal shape and classify 3007 daily rainfall time series from the NOAA/NCDC Global Historical Climatology Network database, with at least 30 years of recordings, into Gaussian (G) and non-Gaussian (NG) groups; 2) logistic regression analysis to identify the statistics of daily rainfall that are most descriptive of the G/NG classification; and 3) a random-search algorithm to conclude on a set of constraints that allows classification of ART samples on the basis of the marginal statistics of daily rain rates. The analysis shows that the Anderson–Darling (AD) test statistic is the most conservative one in determining approximate Gaussianity of ART samples (followed by Cramer–Von Mises and Lilliefors’s version of Kolmogorov–Smirnov) and that daily rainfall time series with fraction of wet days fwd < 0.1 and daily skewness coefficient of positive rain rates skwd > 5.92 deviate significantly from the normal shape. In addition, we find that continental climate (type D) exhibits the highest fraction of Gaussian distributed ART samples (i.e., 74.45%; AD test at α = 5% significance level), followed by warm temperate (type C; 72.80%), equatorial (type A; 68.83%), polar (type E; 62.96%), and arid (type B; 60.29%) climates.

Psychologists Should Use Brunner-Munzel’s Instead of Mann-Whitney’s U Test as the Default Nonparametric Procedure

To investigate whether a variable tends to be larger in one population than in another, the t test is the standard procedure. In some situations, the parametric t test is inappropriate, and a nonparametric procedure should be used instead. The default nonparametric procedure is Mann-Whitney’s U test. Despite being a nonparametric test, Mann-Whitney’s test is associated with a strong assumption, known as exchangeability. I demonstrate that if exchangeability is violated, Mann-Whitney’s test can lead to wrong statistical inferences even for large samples. In addition, I argue that in psychology, exchangeability is typically not met. As a remedy, I introduce Brunner-Munzel’s test and demonstrate that it provides good Type I error rate control even if exchangeability is not met and that it has similar power as Mann-Whitney’s test. Consequently, I recommend using Brunner-Munzel’s test by default. To facilitate this, I provide advice on how to perform and report on Brunner-Munzel’s test.

Monitoring patient flow in a hospital emergency department: ARMA-based nonparametric GLRT scheme

Overcrowding in emergency departments (EDs) is a primary concern for hospital administration. They aim to efficiently manage patient demands and reducing stress in the ED. Detection of abnormal ED demands (patient flows) in hospital systems aids ED managers to obtain appropriate decisions by optimally allocating the available resources following patient attendance. This paper presents a monitoring strategy that provides an early alert in an ED when an abnormally high patient influx occurs. Anomaly detection using this strategy involves the amalgamation of autoregressive-moving-average (ARMA) time series models with the generalized likelihood ratio (GLR) chart. A nonparametric procedure based on kernel density estimation is employed to determine the detection threshold of the ARMA-GLR chart. The developed ARMA-based GLR has been validated through practical data from the ED at Lille Hospital, France. Then, the ARMA-based GLR method’s performance was compared to that of other commonly used charts, including a Shewhart chart and an exponentially weighted moving average chart; it proved more accurate.

Psychologists Should Use Brunner-Munzel's Instead of Mann-Whitney's U test as the Default Nonparametric Procedure

For comparing two groups with regard to their central tendencies, the t-test is the standard procedure. In some situations, the parametric t-test is inappropriate, and a nonparametric procedure should be used instead. The default nonparametric procedure is Mann-Whitney's U test. Despite being a nonparametric test, Mann-Whitney's U test is associated with a strong assumption, known as exchangeability. I demonstrate that if exchangeability is violated, Mann-Whitney's U test can lead to wrong statistical inferences even for large samples. Additionally, I argue that in psychology, exchangeability is often not met. As a remedy, I introduce Brunner-Munzel's test and demonstrate that it provides good type I error rate control even if exchangeability is not met, and has almost equal power as Mann-Whitney's U test. Consequently, I recommend using Brunner-Munzel's test by default. To facilitate this, I provide advice on how to perform and report on Brunner-Munzel's test.

A sector-based nonparametric decomposition of economic growth

Sectors have gained in importance when studying economic growth and convergence of countries. In this letter, we suggest a simple and intuitive nonparametric procedure to obtain the decomposition of economic growth of countries in terms of sector-level indicators. It turns out that the new decomposition can be used to investigate the role of the sectors in the economic growth and convergence of countries. We propose an application to the case of 19 countries and nine sectors in Europe.

A nonparametric procedure for exploring differences in rating quality across test-taker subgroups in rater-mediated writing assessments

Differences in rater judgments that are systematically related to construct-irrelevant characteristics threaten the fairness of rater-mediated writing assessments. Accordingly, it is essential that researchers and practitioners examine the degree to which the psychometric quality of rater judgments is comparable across test-taker subgroups. Nonparametric procedures for exploring these differences are promising because they allow researchers and practitioners to examine important characteristics of ratings without potentially inappropriate parametric transformations or assumptions. This study illustrates a nonparametric method based on Mokken scale analysis (MSA) that researchers and practitioners can use to identify and explore differences in the quality of rater judgments between subgroups of test-takers. Overall, the results suggest that MSA provides insight into differences in rating quality across test-taker subgroups based on demographic characteristics. Differences in the degree to which raters adhere to basic measurement properties suggest that the interpretation of ratings may vary across subgroups. The implications of this study for research and practice are discussed.

A New Method to Detect Outliers in High-frequency Time Series

The objective of this research is to develop a fast, simple method for detecting and replacing extreme spikes in high-frequency time series data. The method primarily consists&nbsp; of a nonparametric procedure that pursues a balance between fidelity to observed data and smoothness. Furthermore, through examination of the absolute difference between original and smoothed values, the technique is also able to detect and, where necessary, replace outliers with less extreme data. Unlike other filtering procedures found in the literature, our method does not require a model to be specified for the data. Additionally, the filter makes only a single pass through the time series. Experiments&nbsp; show that the new method can be validly used as a data preparation tool to ensure that time series modeling is supported by clean data, particularly in a complex context such as one with high-frequency data.

Using Guttman errors to explore rater fit in rater-mediated performance assessments

Model-data fit indices for raters provide insight into the degree to which raters demonstrate psychometric properties defined as useful within a measurement framework. Fit statistics for raters are particularly relevant within frameworks based on invariant measurement, such as Rasch measurement theory and Mokken scale analysis. A simple approach to examining invariance is to examine assessment data for evidence of Guttman errors. I used real and simulated data to illustrate and explore a nonparametric procedure for evaluating rater errors based on Guttman errors and to examine the alignment between Guttman errors and other indices of rater fit. The results suggested that researchers and practitioners can use summaries of Guttman errors to identify raters who exhibit misfit. Furthermore, results from the comparisons between summaries of Guttman errors and parametric fit statistics suggested that both approaches detect similar problematic measurement characteristics. Specifically, raters who exhibit many Guttman errors tended to have higher-than-expected Outfit MSE statistics and lower-than-expected estimated slope statistics. I discuss implications of these results as they relate to research and practice for rater-mediated assessments.