Resampling Simulation Research Articles

Adjusting effective multiplicity ( ) for family-wise error rate in functional near-infrared spectroscopy data with a small sample size.

The advancement of multichannel functional near-infrared spectroscopy (fNIRS) has enabled measurements across a wide range of brain regions. This increase in multiplicity necessitates the control of family-wise errors in statistical hypothesis testing. To address this issue, the effective multiplicity ( ) method designed for channel-wise analysis, which considers the correlation between fNIRS channels, was developed. However, this method loses reliability when the sample size is smaller than the number of channels, leading to a rank deficiency in the eigenvalues of the correlation matrix and hindering the accuracy of calculations. We aimed to reevaluate the effectiveness of the method for fNIRS data with a small sample size. In experiment 1, we used resampling simulations to explore the relationship between sample size and values. Based on these results, experiment 2 employed a typical exponential model to investigate whether valid could be predicted from a small sample size. Experiment 1 revealed that the values were underestimated when the sample size was smaller than the number of channels. However, an exponential pattern was observed. Subsequently, in experiment 2, we found that valid values can be derived from sample sizes of 30 to 40 in datasets with 44 and 52 channels using a typical exponential model. The findings from these two experiments indicate the potential for the effective application of correction in fNIRS studies with sample sizes smaller than the number of channels.

Optical interpretation of oil emulsions in the ocean - Part III: A three-dimensional unmixing model to quantify oil concentration

Knowledge of oil distribution and characteristics (e.g., oil type, oil thickness, concentration, volume, etc.) is valuable for oil spill response and post-spill assessment. Optical remote sensing has the advantage of collecting information in multiple spectral bands, thus being able to classify and quantify different oil emulsions. However, most spectral characteristics of oil emulsions have been obtained from laboratory measurements, and it is unclear how to apply these lab-based findings to airborne or satellite observations of highly heterogeneous oil patches. This study proposes a three-dimensional (3D) unmixing model to explain AVIRIS image spectra of oil-containing pixels with both horizontal and vertical mixing effects. Based on the 3D unmixing model, an oil concentration lookup table (LUT) and LUT-based oil concentration mapping method are developed to quantify oil concentrations from AVIRIS observations, with the results validated through the use of a spectral similarity index. Using spatial and spectral resampling simulations, we also propose an approach to extend the LUT to different optical sensors (e.g., Landsat) in order to map oil concentrations, yet such an approach requires further research to validate and improve.

EpiReSIM: A Resampling Method of Epistatic Model without Marginal Effects Using Under-Determined System of Equations.

Simulation experiments are essential to evaluate epistasis detection methods, which is the main way to prove their effectiveness and move toward practical applications. However, due to the lack of effective simulators, especially for simulating models without marginal effects (eNME models), epistasis detection methods can hardly verify their effectiveness through simulation experiments. In this study, we propose a resampling simulation method (EpiReSIM) for generating the eNME model. First, EpiReSIM provides two strategies for solving eNME models. One is to calculate eNME models using prevalence constraints, and another is by joint constraints of prevalence and heritability. We transform the computation of the model into the problem of solving the under-determined system of equations. Introducing the complete orthogonal decomposition method and Newton's method, EpiReSIM calculates the solution of the underdetermined system of equations to obtain the eNME model, especially the solution of the high-order model, which is the highlight of EpiReSIM. Second, based on the computed eNME model, EpiReSIM generates simulation data by a resampling method. Experimental results show that EpiReSIM has advantages in preserving the biological properties of minor allele frequencies and calculating high-order models, and it is a convenient and effective alternative method for current simulation software.

Network reconstruction based on synthetic data generated by a Monte Carlo approach

Background: Network models are useful tools for researchers to simplify and understand investigated systems. Yet, the assessment of methods for network construction is often uncertain. Random resampling simulations can aid to assess methods, provided synthetic data exists for reliable network construction.
 Objectives: We implemented a new Monte Carlo algorithm to create simulated data for network reconstruction, tested the influence of adjusted parameters and used simulations to select a method for network model estimation based on real-world data. We hypothesized, that reconstructs based on Monte Carlo data are scored at least as good compared to a benchmark.
 Methods: Simulated data was generated in R using the Monte Carlo algorithm of the mcgraph package. Benchmark data was created by the huge package. Networks were reconstructed using six estimator functions and scored by four classification metrics. For compatibility tests of mean score differences, Welch’s t-test was used. Network model estimation based on real-world data was done by stepwise selection.
 Samples: Simulated data was generated based on 640 input graphs of various types and sizes. The real-world dataset consisted of 67 medieval skeletons of females and males from the region of Refshale (Lolland) and Nordby (Jutland) in Denmark.
 Results: Results after t-tests and determining confidence intervals (CI95%) show, that evaluation scores for network reconstructs based on the mcgraph package were at least as good compared to the benchmark huge. The results even indicate slightly better scores on average for the mcgraph package.
 Conclusion: The results confirmed our objective and suggested that Monte Carlo data can keep up with the benchmark in the applied test framework. The algorithm offers the feature to use (weighted) un- and directed graphs and might be useful for assessing methods for network construction.
 
 
 
 
 
 
 
 
 
 
 
 
 

Metrics of News Audience Polarization: Same or Different?

ABSTRACT Although media and communication scholars have suggested various analytical methods for measuring and comparing news audience polarization across countries, we lack a systematic assessment of the metrics produced by these techniques. Using survey data from the 2016 Reuters Institute Digital News Report on news use in 26 countries, we address this gap through a resampling simulation experiment. Our simulation revealed a strong impact of analytical choices, which invited disparate interpretations in terms of how polarized news audiences are, how strongly audience polarization structurally varies between news environments, and how news audience polarization is distributed cross-nationally. Alternative choices led to profound differences in the compatibility, consistency, and validity of the empirical news audience polarization estimates. We conclude from these results that a more precise methodological understanding of news audience polarization metrics informs our capability to draw meaningful inferences from empirical work.

IMPACT OF DIFFERENT LIFE-CYCLE SAVING STRATEGIES AND UNEMPLOYMENT ON INDIVIDUAL SAVINGS IN DEFINED CONTRIBUTION PENSION SCHEME IN SLOVAKIA

Searching for the optimal saving strategy is often tied with the life-cycle strategies where only the age of a saver is considered for setting the allocation profile between equities and bonds. Our article contributes to the debate by looking at the performance and adequacy risks arising from applying age-based saving strategies for savers in funded pension schemes. As many studies have proven the shift of the risk onto savers in defined contribution pension schemes under various saving strategies, we contribute to the debate by providing simulations of expected accumulated savings via funded pension scheme under the various life-cycle income profiles and existence of unemployment risk. Using the resampling simulation technique, we compare the fixed and age-based strategies of three different agents with various life-cycle income paths and different unemployment risk. We compare the expected amount of savings and calculate relative indicators comparing the expected monthly benefits, income replacement rate. We look closely on the impact of unemployment on the value of savings and calculate the unemployment factor explaining the value of savings lost due to the periods of unemployment. By combining life-cycle income functions of individuals with different education level and unemployment risk, we show that decisions of implementing low risk saving strategies are suboptimal and lead to a substantial decrease in replacement ratios not only for higher income cohorts but especially for the lowest ones. At the same time, we prove that employing low risk saving strategy leads to the increase of adequacy risk especially driven by the unemployment risk that is higher for lower education individuals. We conclude that age-based life-cycle saving strategies, where the remaining saving horizon is the only factor defining the allocation profile is not the optimal saving strategy and other factors should be considered as well when searching for optimal saving strategy.

Cefazolin prophylaxis in minimally invasive gynecologic surgery – are dosage and timing appropriate? Prospective study using resampling simulation

ObjectiveCefazolin is a widely used antimicrobial prophylactic agent, however the appropriate dosage, timing, pharmacology and microbial coverage have not been well-established for gynecologic procedures. We aimed to describe serum concentrations and pharmacokinetics of Intravenous cefazolin given to women prior to scheduled minimally invasive gynecologic surgeries, and to determine whether appropriate antimicrobial coverage had been achieved in short time from prophylactic administration to surgical start time. MethodsA prospective cohort analysis study, using a resampled dataset, of women undergoing scheduled gynecological surgeries in a university affiliated tertiary medical center. IV cefazolin (1 or 2 gr) was administered prior to incision to women weighing <80 kg (Group A) and ≥80 kg (Group B), respectively. Cefazolin serum levels were obtained at the time of skin incision (Time 0) and 30 min later (Time 30), measured by high-pressure liquid chromatography (HPLC). Appropriate antimicrobial coverage was defined when cefazolin serum levels were above minimal inhibitory concentrations (MIC) for Enterobacteriaceae. ResultsOverall, 21 women were included. The mean time interval between drug administration and incision did not differ between the two groups (18 ± 10 min vs. 11 ± 10 min, respectively, p = .0.25). A hierarchical mixed linear regression model, using a simulation of multiple random bootstrap resampling (n = 1,000), revealed that cefazolin serum levels exceeded MIC, regardless of the timing of administration in the sampling intervals. Mean cefazolin serum levels in time 0 and time 30 min were not affected by BMI in patients receiving 1 gr. ConclusionA single dose of IV cefazolin given shortly prior to skin incision provides serum concentrations above minimal inhibitory concentrations for susceptible pathogens in most women undergoing scheduled minimally invasive gynecologic surgery.

Projecting a Life-cycle Income – a Simulation Model for the Slovak Pension Benefit Statement

The introduction of a regulation requiring pension asset managers to provide savers with an estimation of pension benefits opened a wide range of scientific questions on the projection methods and estimation of input parameters. One of them is the estimation of life-cycle income for calculating expected contributions and the estimation of the benefit ratio at the moment of retirement. We present an estimation of life-cycle income functions for various age and educational cohorts influenced by temporary labor market shocks. By employing the resampling simulation method for incorporating macroeconomic shocks, we have shown that using longitudinal data on the income process from a large closed economy could bring valid results for a country with a small open economy as well where the longitudinal data on income processes of individuals are unavailable. Our findings could serve a practical use when pension or other social benefits tied to individual income should be modelled.

A novel gamma GLM approach to MRI relaxometry comparisons.

PurposeTo demonstrate that constant coefficient of variation (CV), but nonconstant absolute variance in MRI relaxometry (T 1, T 2, R 1, R 2) data leads to erroneous conclusions based on standard linear models such as ordinary least squares (OLS). We propose a gamma generalized linear model identity link (GGLM‐ID) framework that factors the inherent CV into parameter estimates. We first examined the effects on calculations of contrast agent relaxivity before broadening to other applications such as analysis of variance (ANOVA) and liver iron content (LIC).MethodsEight models including OLS and GGLM‐ID were initially fit to data obtained on sulfated dextran iron oxide (SDIO) nanoparticles. Both a resampling simulation on the data as well as two separate Monte Carlo simulations (with and without concentration error) were performed to determine mean square error (MSE) and type I error rate. We then evaluated the performance of OLS/GGLM‐ID on R 1 repeatability and LIC data sets.ResultsOLS had an MSE of 4–5× that of GGLM‐ID as well as a type I error rate of 20–30%, whereas GGLM‐ID was near the nominal 5% level in the relaxivity study. Only OLS found statistically significant effects of MRI facility on relaxivity in an R 1 repeatability study, but no significant differences were found in a resampling, whereas GGLM was more consistent. GGLM‐ID was also superior to OLS for modeling LIC.ConclusionsOLS leads to erroneous conclusions when analyzing MRI relaxometry data. GGLM‐ID factors in the inherent CV of an MRI experiment, leading to more reproducible conclusions.

How Do Filtering Choices Impact the Structures of Audience Networks? A Simulation Study Using Data from 26 Countries

ABSTRACT Although communication scholars have increasingly studied audience fragmentation by analyzing audience networks, recent cross-national research has spurred a methodological debate about how to construct these audience networks from media use data. One point of contention lies in the choice of filtering procedures that only keep meaningful ties, where the traditional deviation-from-random-duplication filter has been replaced by alternatives such as backbone extraction. Using survey data from the 2016 Reuters Institute Digital News Report on news usage in 26 countries, we examined how filtering choices impact the structures of audience networks through a resampling simulation experiment. The simulation revealed a strong divergence between the filtering approaches, which invited disparate interpretations regarding the fragmentation of modern news audiences. The backbone extraction approach produced the most distinct results, while dealing most effectively with randomness in the media use data. As the filtering approaches mapped the structural features of audiences to different values of the same network metrics, the findings imply that studies diverging in these analytical decisions do not produce compatible results.

Satellite-Based Water Quality Mapping from Sequential Simulation with Parameter Outlier Removal

The satellite-based regression model provides the data model that identifies water quality for inland and coastal waters. However, the satellite regression usually depends on the selection of observation, satellite data, and model type. A resampling simulation technique, such as sequential simulation using geographically weighted regression (GWR simulation), can be applied in generating multiple realizations for water quality estimation to reduce the sampling effect and consider spatial heterogeneity. Traditional models often result in considerable underestimation in extreme observations. The GWR simulation provides the best goodness of fit and spatial varying relationship between observed water quality and remote sensing considering parameter outlier and noise removal for parameter stability. This simulation model can increase the sampling diversity from various observations and reduce the neighboring effects of observations using outlier and noise removal. The model that handles spatial uncertainty and heterogeneity is a novel tool for inferring the characteristics of water quality from a series of sample subsets.

Comparison between two generalized process capability indices for Burr XII distribution using bootstrap confidence intervals

Process capability index is an effective indicator for gauging the capability of a process potential and performance. It is used to quantify the relation between the actual performance of the process and the preset specifications of the product. In this article, we utilize bootstrap re-sampling simulation method to construct bootstrap confidence intervals, namely, standard bootstrap, percentile bootstrap, Student’s t bootstrap (STB) and bias-corrected percentile bootstrap to study the difference between two generalized process capability indices $$C_{\text {pTk1}}$$ and $$C_{\text {pTk2}}$$ ($$\delta =C_{\text {pTk1}}-C_{\text {pTk2}}$$) and to select the better of the two processes or manufacturer’s (or supplier’s) through simulation when the underlying distribution is Burr XII distribution. The model parameters are estimated by maximum likelihood method. The proposed four bootstrap confidence intervals can be effectively employed to determine which one of the two processes or manufacturers (or suppliers) has a better process capability. Monte Carlo simulations are performed to compare the performances of the proposed bootstrap confidence intervals for $$\delta $$ in terms of their estimated average widths, coverage probabilities and relative coverages. Simulation results showed that the estimated average width and relative coverages of the STB confidence interval perform better than their counterparts. Finally, real data are presented to illustrate the bootstrap confidence intervals of the difference between two process capability indices.

Bootstrap confidence intervals of the difference between two generalized process capability indices for inverse Lindley distribution

A process capability index (PCI) meant for assessing the capability of the concerned manufacturing process to manufacturer’s products as per specifications pre-set by the product designers or customers. In this article, we utilize bootstrap re-sampling simulation method to construct bootstrap confidence intervals, namely, standard bootstrap (s-boot), percentile bootstrap (p-boot), and bias-corrected percentile bootstrap (BCp-boot) for the difference between two indices ( $$C_\mathrm{pyk1}-C_\mathrm{pyk2}$$ ) through simulation when the underlying distribution is inverse Lindley distribution. Maximum-likelihood method is used to estimate the parameter of the model. The proposed bootstrap confidence intervals can be effectively employed to determine which one of the two processes or manufacturer’s (or supplier’s) has a better process capability. A Monte Carlo simulation has been used to investigate the estimated coverage probabilities and average widths of the bootstrap confidence intervals of ( $$C_\mathrm{pyk1}-C_\mathrm{pyk2}$$ ). Simulation results showed that the estimated coverage probabilities of the standard bootstrap confidence interval perform better than their counterparts. Finally, a simulated data and a real data are presented to illustrate the bootstrap confidence intervals of the difference between two PCIs.

Trends in UAH Zonal Mean Lower Troposphere Temperatures

Full span OLS trends in zonal means for land and ocean of UAH satellite based measurements of lower troposphere temperatures 12/1978 to 4/2017 are tested for reliability and their information content in the context of OLS assumptions. Resampling and Gaussian simulations do not indicate instability of full span linear OLS trends in any of the sixteen zonal regions studied. However, comparison of split half trends and trends in a moving window show evidence of nonlinear behavior in nine of the sixteen zonal regions. It is proposed that in such cases trend profiles provide greater insight and more useful trend information than full span linear OLS trends.

Optimal plot size in the evaluation of papaya scions: proposal and comparison of methods

ABSTRACT Evaluating the quality of scions is extremely important and it can be done by characteristics of shoots and roots. This experiment evaluated height of the aerial part, stem diameter, number of leaves, petiole length and length of roots of papaya seedlings. Analyses were performed from a blank trial with 240 seedlings of "Golden Pecíolo Curto". The determination of the optimum plot size was done by applying the methods of maximum curvature, maximum curvature of coefficient of variation and a new proposed method, which incorporates the bootstrap resampling simulation to the maximum curvature method. According to the results obtained, five is the optimal number of seedlings of papaya "Golden Pecíolo Curto" per plot. The proposed method of bootstrap simulation with replacement provides optimal plot sizes equal or higher than the maximum curvature method and provides same plot size than maximum curvature method of the coefficient of variation.

Proceedings: human leukocyte antigen haplo-homozygous induced pluripotent stem cell haplobank modeled after the california population: evaluating matching in a multiethnic and admixed population.

The development of a California-based induced pluripotent stem cell (iPSC) bank based on human leukocyte antigen (HLA) haplotype matching represents a significant challenge and a valuable opportunity for the advancement of regenerative medicine. However, previously published models of iPSC banks have neither addressed the admixed nature of populations like that of California nor evaluated the benefit to the population as a whole. We developed a new model for evaluating an iPSC haplobank based on demographic and immunogenetic characteristics reflecting California. The model evaluates haplolines or cell lines from donors homozygous for a single HLA-A, HLA-B, HLA-DRB1 haplotype. We generated estimates of the percentage of the population matched under various combinations of haplolines derived from six ancestries (black/African American, American Indian, Asian/Pacific Islander, Hispanic, and white/not Hispanic) and data available from the U.S. Census Bureau, the California Institute for Regenerative Medicine, and the National Marrow Donor Program. The model included both cis (haplotype-level) and trans (genotype-level) matching between a modeled iPSC haplobank and the recipient population following resampling simulations. We showed that serving a majority (>50%) of a simulated California population through cis matching would require the creation, redundant storage, and maintenance of almost 207 different haplolines representing the top 60 most frequent haplotypes from each ancestry group. Allowances for trans matching reduced the haplobank to fewer than 141 haplolines found among the top 40 most frequent haplotypes. Finally, we showed that a model optimized, custom haplobank was able to serve a majority of the California population with fewer than 80 haplolines.

A rapid indicator of cultural ecosystem service usage at a fine spatial scale: Content analysis of social media photographs

Cultural ecosystem services (CES) are an important benefit that habitats provide, particularly in the fragments of natural ecosystems that remain in urban areas. To manage CES we need to understand what people use habitats for, and where different activities take place. It is challenging to assess CES provision, as surveys and interviews are time consuming and can be expensive. Social media data, particularly geo-tagged photographs, are spatially explicit and contain visual information that can be used to infer cultural use. Indicators of CES derived from social media make use of existing data so may contribute useful information for rapid, cost-effective assessments of CES. In this study we develop an indicator of CES usage that is derived from photographs from an image-sharing website, at two different scales. First, we compare four small (<150ha) urban mangrove sites in Singapore, using photograph content to classify sites according to predominant cultural use. Second, the spatial distribution of different CES was modelled within one site using MaxEnt. A resampling simulation was conducted to identify the sensitivity of the photograph classification to the number of photographs classified. Photographs of social recreation, organisms and landscapes occurred most commonly. The proportional occurrence of photograph types differed between sites depending on their characteristics. Within one site, the probability of occurrence of social recreation photographs was highest around built focal points, while photographs of organisms were more likely in the mangrove and terrestrial habitats. Classifying more than 50–70 photographs (which would take approximately 30minutes) gave only small increases in categorisation accuracy. This tool for CES assessment rapidly provided information that would be useful for managing Singapore's mangroves. The approach could be widely applied to assess CES provision across a range of habitats and settings, helping CES to become more commonly considered in ecosystem service evaluations.

The Power of Phase I Studies to Detect Clinical Relevant QTc Prolongation: A Resampling Simulation Study

Concentration-effect (CE) models applied to early clinical QT data from healthy subjects are described in the latest E14 Q&A document as promising analysis to characterise QTc prolongation. The challenges faced if one attempts to replace a TQT study by thorough ECG assessments in Phase I based on CE models are the assurance to obtain sufficient power and the establishment of a substitute for the positive control to show assay sensitivity providing protection against false negatives. To demonstrate that CE models in small studies can reliably predict the absence of an effect on QTc, we investigated the role of some key design features in the power of the analysis. Specifically, the form of the CE model, inclusion of subjects on placebo, and sparse sampling on the performance and power of this analysis were investigated. In this study, the simulations conducted by subsampling subjects from 3 different TQT studies showed that CE model with a treatment effect can be used to exclude small QTc effects. The number of placebo subjects was also shown to increase the power to detect an inactive drug preventing false positives while an effect can be underestimated if time points around t max are missed.

Reliability analysis of a gravity-based foundation for wind turbines: a code-based design assessment

Deterministic code-based designs proposed for wind turbine foundations are typically biased on the conservative side, and overestimate the probability of failure, which can lead to higher than necessary construction cost. In this study reliability analysis of a gravity-based foundation, concerning its bearing capacity, is used to calibrate a code-based design procedure. A probabilistic finite-element model is developed to analyse the bearing capacity of a surface footing on soil with spatially variable undrained strength. Monte Carlo simulation is combined with a re-sampling simulation technique to perform the reliability analysis. The calibrated code-based design approach leads to savings of up to 20% in the concrete foundation volume, depending on the target annual reliability level. The study can form the basis for future optimisation on deterministic-based designs for wind turbine foundations.

NEW NEUROSCIENCE BASED COGNITIVE PARADIGMS FOR BIOMARKER RESEARCH IN SCHIZOPHRENIA

Many individuals with schizophrenia experience a profound deficit in global cognitive ability, which is related to poor functional outcomes. Historically, the standard of assessing neurocognitive impairments is one of extensive neuropsychological batteries that are labour-intensive. The present study examined whether a brief neurocognitive assessment (BNA) instrument could effectively estimate global neurocognition and further examined its clinical utility.The validity and clinical utility of a BNA that takes approximately 10 min to administer was examined against a full neuropsychological battery that takes approximately 90 min to administer in a large and heterogeneous sample of 1303 patients with schizophrenia.The BNA explained 76% of the variance in global neurocognition in the total sample and remained consistent in subsamples stratified by clinical characteristics (e.g., severity of psychopathology) and in randomized re-sampling simulations. The two items that comprised the BNA were the letter-number sequencing test, a measure of working memory, and the digit-symbol test, a measure of processing speed. Next, perhaps more importantly, the BNA and full neuropsychological battery were related to symptoms and functional status to a similar degree in both univariate and multivariate regression models; moreover, both instruments were sensitive to longitudinal treatment related change to a similar degree.The BNA is able to rapidly, easily, and validly assess global neurocognition in schizophrenia. The BNA was associated with important clinical outcomes to a similar degree as a full cognitive battery. This tool provides clinicians and researchers a means to assess global neurocognitive impairments without requiring extensive neuropsychological testing.