Estimate Of Central Tendency Research Articles

Earthquakes Parameters from Citizen Testimonies: A Retrospective Analysis of EMSC Database

Abstract We aim to compute macroseismic parameters (location and magnitude) using the BOXER code for the first time on the citizen testimonies, that is, individual intensity data points (IDPs) at the global scale collected and made available by the LastQuake system of the European–Mediterranean Seismological Centre (EMSC). IDPs available for different earthquakes are selected to eliminate those that are geographically inconsistent with most data; then they are clustered spatially based on various methods. For each cluster with at least three IDPs, a macroseismic data point (MDP), corresponding to an intensity value assessed for given localities as in classical macroseismic studies, is computed by various central tendency estimators (average, median, and trimmed averages). Finally, macroseismic parameters are obtained by MDP distribution using two location methods of BOXER code. For each earthquake, we used raw and corrected intensities and 132 different combinations of grouping methods, estimators, and BOXER methods. We assigned a ranking to the combinations that best reproduce instrumental parameters and used such a ranking to select preferred combinations for each earthquake. We analyzed retrospectively the reliability of the parameters as a function of time and space. The results are essentially identical using original and corrected intensities and show higher reliability for BOXER’s method 1 than for method 0; they are dependent on the geographical area, and generally improve over time and with the number of IDPs collected. These findings are useful for the future real-time analyses, and for evaluating the location and magnitude of earthquakes whenever a sufficient number of IDPs are available and with a distribution such that MDPs can be derived and the BOXER method applied.

How much does that cost? Examining the economic costs of crime in North America attributable to people with psychopathic personality disorder.

Cost of illness research has established that mental disorders lead to significant social burden and massive financial costs. A significant gap exists for the economic burden of many personality disorders, including psychopathic personality disorder (PPD). In the current study, we used a top-down prevalence-based cost of illness approach to estimate bounded crime cost estimates of PPD in the United States and Canada. Three key model parameters (PPD prevalence, relative offending rate of individuals with PPD, and national costs of crime for each country) were informed by existing literature. Sensitivity analyses and Monte Carlo simulations were conducted to provide bounded and central tendency estimates of crime costs, respectively. The estimated PPD-related costs of crime ranged from $245.50 billion to $1,591.57 billion (simulated means = $512.83 to $964.23 billion) in the United States and $12.14 billion to $53.00 billion (simulated means = $25.33 to $32.10 billion) in Canada. These results suggest that PPD may be associated with a substantial economic burden as a result of crime in North America. Recommendations are discussed regarding the burden-treatment discrepancy for PPD, as the development of future effective treatment for the disorder may decrease its costly burden on health and justice systems. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Monte Carlo Simulation of a Modified Chi Distribution Considering Asymmetry in the Generating Functions: Application to the Study of Health-Related Variables

Random variables in biology, social and health sciences commonly follow skewed distributions. Many of these variables can be represented by exGaussian functions; however, in practice, they are sometimes considered as Gaussian functions when statistical analysis is carried out. The asymmetry can play a fundamental role which can not be captured by central tendency estimators such as the mean. By means of Monte Carlo simulations, the effect of a small asymmetry in the generating functions of the chi distribution is studied. To this end, the k generating functions are taken as exGaussian functions. The limits of this approximation are tested numerically for the practical case of three health-related variables: one physical (body mass index) and two cognitive (verbal fluency and short-term memory). This work is in line with our previous works on a physics-inspired mathematical model to represent the reaction times of a group of individuals.

Left-Handers Are Less Lateralized Than Right-Handers for Both Left and Right Hemispheric Functions.

Many neuroscientific techniques have revealed that more left- than right-handers will have unusual cerebral asymmetries for language. After the original emphasis on frequency in the aphasia and epilepsy literatures, most neuropsychology, and neuroimaging efforts rely on estimates of central tendency to compare these two handedness groups on any given measure of asymmetry. The inevitable reduction in mean lateralization in the left-handed group is often postulated as being due to reversed asymmetry in a small subset of them, but it could also be due to a reduced asymmetry in many of the left-handers. These two possibilities have hugely different theoretical interpretations. Using functional magnetic resonance imaging localizer paradigms, we matched left- and right-handers for hemispheric dominance across four functions (verbal fluency, face perception, body perception, and scene perception). We then compared the degree of dominance between the two handedness groups for each of these four measures, conducting t-tests on the mean laterality indices. The results demonstrate that left-handers with typical cerebral asymmetries are less lateralized for language, faces, and bodies than their right-handed counterparts. These results are difficult to reconcile with current theories of language asymmetry or of handedness.

Potential for Manganese-Induced Neurologic Harm to Formula-Fed Infants: A Risk Assessment of Total Oral Exposure.

Background:High oral exposure and biological vulnerabilities may put formula-fed infants at risk for manganese-induced neurotoxicity.Objectives:We sought to characterize manganese concentrations in public drinking water and prepared infant formulas commonly purchased in the United States, integrate information from these sources into a health risk assessment specific to formula-fed infants, and examine whether households that receive water with elevated manganese concentrations avoid or treat the water, which has implications for formula preparation.Methods:Manganese was measured in 27 infant formulas and nearly all Minnesota community public water systems (CPWS). The risk assessment produced central tendency and upper-end exposure estimates that were compared to a neonatal animal-based health reference dose (RfD) and considered possible differences in bioavailability. A survey study assessed esthetic concerns, treatment, and use of water in a Twin Cities community with various levels of manganese in drinking water.Results:Ten percent of CPWSs were estimated to exceed the EPA health advisory level of . Manganese concentrations in formula ranged from 69.8 to , with amino formula concentrations. Central tendency estimates of soy and amino acid formula reconstituted with water at the CPWS 95th percentile manganese concentration exceeded the neonatal-based RfD. Upper-end estimates of manganese intake from formula alone, independent of any water contribution, equaled or exceeded the neonatal-based RfD. In the survey study, we observed increased awareness of esthetic issues and water avoidance at higher manganese concentrations, but these concentrations were not a reliable consumption deterrent, as the majority of households with inside tap drinking water results above reported drinking the water.Discussion:Excessive exposure to manganese early in life can have long-lasting neurological impacts. This assessment underscores the potential for manganese overexposure in formula-fed infants. U.S. agencies that regulate formula and drinking water must work collaboratively to assess and mitigate potential risks. https://doi.org/10.1289/EHP7901

Soil ingestion among young children in rural Bangladesh.

Ingestion of soil and dust is a pathway of children’s exposure to several environmental contaminants, including lead, pesticides, and fecal contamination. Empirically based estimates of central tendency for soil consumption by children in high-income countries range from 9 to 135 dry mg/day. Using a Monte Carlo simulation, we modeled the mass of soil directly and indirectly ingested per day by rural Bangladeshi children and identified the parameters that influence the mass ingested. We combined data from observations of direct and indirect ingestion among children with measurements of soil mass on the children’s hands, mother’s hands, and objects to quantify soil ingestion/day. Estimated geometric mean soil ingestion was 162 dry mg/day for children 3–5 months, 224 dry mg/day for children 6–11 months, 234 dry mg/day for children 12–23 months, 168 dry mg/day for children 24–35 months, and 178 dry mg/day for children 36–47 months old. Across all age groups, children placing their hands in their mouths accounted for 46–78% of total ingestion and mouthing objects contributed 8–12%. Direct ingestion of soil accounted for nearly 40% of soil ingested among children 6–23 months old. Sensitivity analyses identified that the parameters most affecting the estimates were the load of soil on the child’s hand, the frequency of hand-to-mouth contacts while not eating, and, for children 6–23 months old, the frequency of direct soil ingestion. In a rural, low-income setting, children’s soil consumption was substantially more than the estimates for children in high-income countries. Further characterizing soil ingestion of children in low-income contexts would improve assessments of the risks they face from soil-associated contaminants.

A statistical study on lognormal central tendency estimation in probabilistic seismic assessments

Lognormal distribution is one of the most applicable distributions in many different probabilistic seismic assessments such as seismic hazard analysis, fragility analyses, and reliability evaluations. This study compares the sample median (SM), sample geometric mean (SGM) and Sample Bias Corrected Geometric Mean (SBCGM) as three different estimators of lognormal central estimator from different aspects. To this end, the efficiency of each estimator is investigated by calculating their bias and minimum squared error (MSE) using Monte Carlo Simulations. Further, probabilistic performance of four Steel Moment Resisting Frames (SMRF) is investigated using Incremental Dynamic Analysis (IDA), and the stochastic seismic performance of one of them (5 story frame) is investigated considering the aleatoric and epistemic uncertainties. Additionally, the probabilistic seismic performance of a sample jacket type offshore platform based on the mentioned statistical estimators of lognormal central tendency is investigated implementing the IDA procedure. The results show that the efficiency of SM is significantly lower than the other two estimators. In general, SBCGM is the most efficient one for small sample sizes. However, SBCGM and SGM show the same efficiency as the sample size increases. Also, noticeable differences can be observed in the seismic performance of the structures based on each estimator. According to the results of goodness of fit tests and the likelihood ratio evaluations, SBCGM is recommended for achieving more reliable seismic assessments when using small number of records. As another marginal conclusion, the conducted statistical study revealed that the lognormal assumption is approved for collapse fragility curves considering both aleatoric and epistemic sources of uncertainty.

Source attribution at the food sub-product level for the development of the Canadian Food Inspection Agency risk assessment model

Decreasing the health burden caused by foodborne pathogens is challenging and it depends on the identification of the most significant hazards and food sources causing illnesses, so adequate mitigation strategies can be implemented. In this regard, the Canadian Food Inspection Agency (CFIA) has developed the Establishment-based Risk Assessment (ERA) model, so that a more effective and efficient allocation of resources can be assigned to the highest food safety risk areas. To assess risk, the model considers the type of food sub-products being manufactured by establishments and its scope is limited to the 17 most important foodborne pathogens representing the highest level of food safety risk. However, the information on source attribution at the sub-product level based on a structured approach is limited. To overcome this challenge, an expert elicitation was conducted in 2016 to estimate the relative contribution and associated certainty of each sub-product for 31 pathogen-commodity combinations to the total Canadian health burden associated with foodborne illnesses (expressed in DALYs). These DALYs represent 78% of the total Canadian health burden associated with federally-regulated food commodities considered within the model. A total of 49 Canadian experts recruited using a “snow ball” sampling strategy participated in the study by completing an electronic survey. Results of the elicitation displayed variable levels of health burden allocation between the pathogens and the different commodity sub-products. Assessment of the certainty levels showed some combinations being evaluated with more confidence (e.g., Campylobacter and eggs/poultry sub-products) than others, where a bimodal distribution of certainty was observed (e.g., Toxoplasma in pork sub-products). Furthermore, no participant raised concerns on the food classification scheme, suggesting their agreement with the proposed sub-products categorization of the elicitation. Relative contribution estimates will be included in the CFIA ERA model and used to enhance its applicability for risk prioritization and effective resource allocation during food establishment inspections. While substantial uncertainty around the central tendency estimates was found, these estimates provide a good basis for regulatory oversight and public health policy.

Novel calculator for estimation of Freundlich partitioning coefficient

An estimation method for the Freundlich organic carbon normalized partitioning coefficient (Kfoc) is presented. It is based on regressions developed from batch equilibrium experiments submitted to U.S. EPA for pesticide registration. The regressions represent 18 specific agricultural soils from the U.S. and Europe, and are based on 41 pesticide active ingredients or metabolites. The predictive variable used is the subcooled liquid solubility (Sscl), whose calculation requires the compound's solubility, and if a solid (mp > 25 °C), its melting point. Sscl represents the compound's solubility as if it were a liquid at room temperature and does not assume that the compound is neutral. The estimation method was tested on 333 pesticides, and estimated means matched published Kfoc values to within 10× for 83%, 3× for 65%, and 2× for 43%. The estimation method (Kfoc Estimator v0.1) was further tested against data for 94 herbicides, 55 Superfund Priority Pollutants and 22 polychlorinated biphenyls. The estimated mean and median Kfoc values were highly correlated (R2 > 0.63 to 0.87) with the measured values. Matching of reported to estimated values was generally good. The herbicides were 51%–54% within 2×, 74%–77% within 3×, and 93%–94% within 10×. The Priority Pollutants were 22%–29% within 2×, 38%–55% within 3×, and 73%–75% within 10×. The estimates for the PCBs were low, but the maximum estimated Kfoc was consistently within 10× of the reported Koc. This may be due to the PCB data representing a single soil, rather than the central-tendency estimates produced by Kfoc Estimator v0.1.

Double Perspective DEA on Real Estate Appraisal by Brazilian Standards

Objective: This article innovates when we incorporate the statistical analysis to the method of Double-Perspective Data Envelopment Analysis (DP-DEA), with the objective of obtaining an estimate of greater accuracy and reliability according to the assumptions of the Best Unbiased Estimator (BUE). Design / Research Method: Double Perspective Data Envelopment Analysis (DP-DEA) is an extension to Classical Data Envelopment Analysis (DEA) for estimating efficiency, asset values, indicators, and other attributes from two perspectives, achieving a common result is the main objective. This article innovates in DEA methodology, in two aspects: 1. To demonstrate the ability of the DP-DEA to perform at intervals the estimation of values from a random sample; 2. Through the statistical analysis, making estimates of central tendency according to the assumptions of the Best Unbiased Estimator (BUE – Best Unbiased Estimator). Conclusions / findings: The practical procedures performed step by step through DP-DEA according to the assumptions of the BUE, presented in its main findings and conclusions are: 1. Incorporation of statistical analysis to the DP-DEA method, which assumes assumptions of properties of the Best Estimator Non-biased; 2. Within the scope of the DEA, it presents an innovative capacity to make estimates from random samples, and; 3. At the end of the article, by simulation, able to validate modeling through the variation of property characteristics, demonstrating that the estimation of the corresponding values is consistent according to the market’s expectations. Originality / value of the method: This article opens new avenues to be explored by the DEA community. Firstly, as a tool for valuing assets, according to the Comparative Market Data Method. In Brazil, DP-DEA has been approved by the Brazilian Association of Technical Standards for this purpose. Another innovation is to evaluate performance which results from common gain according to two perspectives, which interact in the process or procedure under analysis.

The Precision and Accuracy of Urinary Creatinine Analysis for Correction of Urine Dilution in Biological Monitoring Measurements

Biomonitoring has been widely used to assess environmental exposure, and spot urine is commonly studied due to its easy sample collection. The variability of urine dilution has been crucial for assessing the exposure magnitude. Previous studies indicated that creatinine-adjusted concentrations are better correlated with the parent chemical concentrations in blood, plasma or serum than unadjusted concentrations. More recent studies have documented problems with creatinine-adjustment in pregnant women and diverse populations because creatinine varies with age, gender, race, body size, and diet.In addition to the inter- and intra-person variability in creatinine, we found significant variability in the testing methods and the testing laboratories. Two immunoassays gave decidedly different concentrations on the same samples in two different laboratories and even had a greater than 35% bias on NIST Standard Reference Materials. In an attempt to standardize the creatinine measurements, both laboratories adopted the same immunoassay.The inter-laboratory relative percent differences (RPD) ranged from 0.3-13.4% with 5.2% median, and the values determined in each laboratory were highly correlated (Pearson correlation coefficient: r=0.98). Although the results were correlated and relatively precise, 15% biases were found between laboratories with values determined by laboratory B being uniformly higher. The directional significant differences were confirmed by paired t-test (p-value<0.001). Thus, a systemic error can occur among different laboratories which might alter the point estimates of central tendency and percentile concentrations.The results show that in addition to the inherent limitations of biological variation in using creatinine-adjustment in biomonitoring, laboratory error from non-standardized measurements can further introduce variation. Taken together, we suggest that the standardized creatinine measurement or cross-laboratories validation is needed.

Lack of group-to-individual generalizability is a threat to human subjects research

Only for ergodic processes will inferences based on group-level data generalize to individual experience or behavior. Because human social and psychological processes typically have an individually variable and time-varying nature, they are unlikely to be ergodic. In this paper, six studies with a repeated-measure design were used for symmetric comparisons of interindividual and intraindividual variation. Our results delineate the potential scope and impact of nonergodic data in human subjects research. Analyses across six samples (with 87-94 participants and an equal number of assessments per participant) showed some degree of agreement in central tendency estimates (mean) between groups and individuals across constructs and data collection paradigms. However, the variance around the expected value was two to four times larger within individuals than within groups. This suggests that literatures in social and medical sciences may overestimate the accuracy of aggregated statistical estimates. This observation could have serious consequences for how we understand the consistency between group and individual correlations, and the generalizability of conclusions between domains. Researchers should explicitly test for equivalence of processes at the individual and group level across the social and medical sciences.

The particles around us

The particles around us

Statistical assessment of seismic fragility curves for steel jacket platforms considering global dynamic instability

ABSTRACTMany probabilistic studies in the field of offshore structures assume that the drift demand distributes lognormally around its median at all the intensity levels. However, this assumption may not be formally validated, and the purpose of this study is to investigate the assumption of lognormal distribution of the drift demand for the fixed offshore platforms, using Anderson–Darling goodness of fit test. The lognormal hypothesis for the drift demand can be investigated at two different regions as: (1) low intensity levels without any collapse case and (2) higher intensity levels with collapse cases. To this end, both the sample median and sample geometric mean are considered as the estimators of lognormal central tendency. The results indicate that the lognormal hypothesise is accepted based on the sample geometric mean at all the intensity levels. Nevertheless, the lognormal hypothesise is rejected at some intensity levels if the sample median is the central estimator.

2018 tallied facial soft tissue thicknesses for adults and sub-adults

The tallied facial soft tissue thicknesses (or T-Tables) represent grand means of published facial soft tissue thickness sample means. These sample means have been drawn from across the full-breadth of the facial soft tissue thickness (FSTT) literature, including forensic science, anthropology and odontology. The report of new summary statistics for >1290 new sub-adults and >2200 new adults since the last T-Table calculation, in 2008 for sub-adults and 2013 for adults respectively, makes their update timely. The maximum sample sizes at any landmark now stand at 3023 for individuals aged 0–11 years old (g–g′); 3145 for individuals aged 12–17 years old (n–se′); and 10,333 for adults (n–se′). Following the recalculation of grand weighted means and comparison to the original 2008 data, some shifts in the T-Table statistics are evident at specific landmarks, namely: 2–2.5mm increases at gonion (go–go′) and mid-mandibular border (mmb–mmb′) for adults; 3.5mm decrease at gonion (go–go′) for 12–17year olds; and 2.0mm decrease at menton (me–me′) for 0–11year olds. Differences at all other landmarks (91–100% depending on the dataset) were minimal being <1.0mm. Performance tests of the new grand means as point estimators (using individuals with known FSTT size from the C-Table), show the 2018 T-Table statistics to produce marginally less error than the 2013 means: 2018 standard error of the estimate=3.7mm in contrast to 2013 standard error of the estimate=3.9mm. The long run nature of the T-Table statistics (i.e., big data) and quantified performance test accuracies on known subjects, earmark the 2018 T-Table as the premier FSTT standard for craniofacial identification casework. In the distant future, this is likely to change as the C-Table raw data repository grows, allowing shorths and shormaxes to be calculated for large samples. Given current raw data repository sample sizes of 0–1574 for T-Table landmarks (notably lower for younger individuals), there is some way to go before enhanced central tendency estimators can entirely replace untrimmed arithmetic means.

Attribution of global foodborne disease to specific foods: Findings from a World Health Organization structured expert elicitation.

BackgroundRecently the World Health Organization, Foodborne Disease Burden Epidemiology Reference Group (FERG) estimated that 31 foodborne diseases (FBDs) resulted in over 600 million illnesses and 420,000 deaths worldwide in 2010. Knowing the relative role importance of different foods as exposure routes for key hazards is critical to preventing illness. This study reports the findings of a structured expert elicitation providing globally comparable food source attribution estimates for 11 major FBDs in each of 14 world subregions.Methods and findingsWe used Cooke’s Classical Model to elicit and aggregate judgments of 73 international experts. Judgments were elicited from each expert individually and aggregated using both equal and performance weights. Performance weighted results are reported as they increased the informativeness of estimates, while retaining accuracy. We report measures of central tendency and uncertainty bounds on food source attribution estimate.For some pathogens we see relatively consistent food source attribution estimates across subregions of the world; for others there is substantial regional variation. For example, for non-typhoidal salmonellosis, pork was of minor importance compared to eggs and poultry meat in the American and African subregions, whereas in the European and Western Pacific subregions the importance of these three food sources were quite similar. Our regional results broadly agree with estimates from earlier European and North American food source attribution research. As in prior food source attribution research, we find relatively wide uncertainty bounds around our median estimates.ConclusionsWe present the first worldwide estimates of the proportion of specific foodborne diseases attributable to specific food exposure routes. While we find substantial uncertainty around central tendency estimates, we believe these estimates provide the best currently available basis on which to link FBDs and specific foods in many parts of the world, providing guidance for policy actions to control FBDs.

Application of Enhanced Point Estimators to a Sample of In Vivo CT-derived Facial Soft Tissue Thicknesses.

Facial approximations based on facial soft tissue depth measurement tables often utilize the arithmetic mean as a central tendency estimator. Stephan et al. (J Forensic Sci 2013;58:1439) suggest that the shorth and 75-shormax statistics are better suited to describe the central tendency of non-normal soft tissue depth data, while also accommodating normal distributions. The shorth, 75-shormax, arithmetic mean, and other central tendency estimators were evaluated using a CT-derived facial soft tissue depth dataset. Differences between arithmetic mean and shorth mean for the tissue depths examined ranged from 0 mm to +2.3 mm (average 0.6 mm). Differences between the arithmetic mean plus one standard deviation (to approximate the same data points covered by the 75-shormax) and 75-shormax values ranged from -0.8 mm to +0.7 mm (average 0.2 mm). The results of this research suggest that few practical differences exist across the central tendency point estimators for the evaluated soft tissue depth dataset.

A neurophysiological explanation for biases in visual localization.

Observers show small but systematic deviations from equal weighting of all elements when asked to localize the center of an array of dots. Counter-intuitively, with small numbers of dots drawn from a Gaussian distribution, this bias results in subjects overweighting the influence of outlier dots - inconsistent with traditional statistical estimators of central tendency. Here we show that this apparent statistical anomaly can be explained by the observation that outlier dots also lie in regions of lower dot density. Using a standard model of V1 processing, which includes spatial integration followed by a compressive static nonlinearity, we can successfully predict the finding that dots in less dense regions of an array have a relatively greater influence on the perceived center.

Evolutionary divergence of reaction norms in ecological context: a commentary.

Evolutionary divergence of reaction norms in ecological context: a commentary.

The effect of alcohol hangover on choice response time.

The effect of alcohol hangover on cognitive processing has received little attention. We explored the effect of alcohol hangover on choice response time (RT), a dominant dependent variable (DV) in cognitive research. Prior research of the effect of hangover on RT has produced mixed findings; all studies reviewed relied exclusively on estimates of central tendency (e.g. mean RT), which has limited information value. Here we present novel analytical methods by going beyond mean RT analysis. Specifically, we examined performance in hangover conditions (n=31) across the whole RT distribution by fitting ex-Gaussian models to participant data, providing a formal description of the RT distribution. This analysis showed detriments to performance under hangover conditions at the slower end of the RT distribution and increased RT variance under hangover conditions. We also fitted an explicit mathematical process model of choice RT - the diffusion model - which estimates parameters reflecting psychologically-meaningful processes underlying choice RT. This analysis showed that hangover reduced information processing efficiency during response selection, and increased response caution; changes in these parameters reflect hangover affecting core decisional-components of RT performance. The implications of the data as well as the methods used for hangover research are discussed.