Skew Estimation Research Articles

To pool or not to pool: Pooled metabarcoding does not affect estimates of prey diversity in spider gut content analysis

Abstract Gut content metabarcoding has provided important insights into the food web ecology of spiders, the most dominant terrestrial arthropod predators. In small invertebrates, like spiders, gut content analysis is often performed on whole body DNA extracts of individual predators, from which prey sequences are selectively enriched and sequenced. Since many spider species are generalist predators, large numbers of samples comprising individual spider specimens must be analysed to recover an exhaustive image of a spider species' prey spectrum, which is costly and time‐consuming. Pooled processing of bulk samples of multiple specimens has been suggested to reduce the necessary workload and cost while still recovering a representative estimate of the prey diversity. However, it is still unclear if pooling approaches lead to bias in recovering the prey spectrum and if the results are comparable with data from individually processed spiders. Here, we test the effects of metabarcoding pooled spider gut content on the recovered taxonomic diversity and composition of prey. Using a newly adapted primer pair, which efficiently enriches COI barcode sequences of diverse arthropod prey groups while suppressing spider amplification, we test if pooling leads to reduced taxonomic diversity or skewed estimates of prey composition. Our results show that pooling and individual processing recover highly correlated taxonomic diversity and composition of prey. The only exception are very rare prey items which were less well recovered by pooling. Our results support pooling as a cost‐effective and time‐efficient approach to recover the diet of generalist predators for population‐level studies of spider trophic interactions.

Revisiting heterogenous social desirability bias in consumer willingness to pay for food carbon label: Social norms and environmental concerns

Carbon labeling facilitates the evaluation of carbon emissions throughout the entire food production process. Consumer interpretation of food labels is subject to context. We conduct an online discrete choice experiment in four Chinese cities to investigate the potential how social desirability bias (SDB) may affect consumer willingness-to-pay (WTP) for milk carbon labels. Our findings indicate that failing to account for SDB leads to skewed WTP estimates. We observe a significant presence of SDB in WTP for carbon labels, particularly among respondents under subject to injunctive social-norm treatment. Additionally, our research shows that SDB can manifest differently among consumers with varying levels of environmental commitment. This research provides crucial insights for both policymakers and marketers in food carbon labeling. It highlights the importance of considering SDB and emphasizes the necessity of developing tailored strategies across different consumer segments.

Investigation on the sampling distributions of non-Gaussian wind pressure skewness and kurtosis

Accurate estimation of skewness and kurtosis is crucial for addressing issues related to non-Gaussian wind pressure distribution fitting, signal simulation, and extreme estimation. The skewness and kurtosis determined from finite-length signals are inherent random variables characterized by obvious value fluctuations. Very limited research has been conducted on the sampling distributions of skewness and kurtosis for non-Gaussian wind pressure, motivating the present study. Firstly, the investigation focuses on the non-Gaussian white noise. The expressions of main statistical indicators such as the variance, covariance, correlation coefficient, skewness, and kurtosis of the two sampling distributions are derived and then verified using the Hermite polynomial model. The primary factors influencing the sampling distribution variance (SDV) of white noise are analyzed, including the signal length and marginal moments of the parent distribution. Secondly, the concentration shifts to colored non-Gaussian processes. The composition of SDV is analyzed through theoretical derivation. On this basis, a method for estimating SDV values using the Gaussian process regression model is proposed, with accuracy and feasibility verified based on the long-duration wind pressure data measured in wind tunnel. Furthermore, the relationship between the SDVs of wind pressure processes, signal length, higher-order correlation functions, and marginal moments of the parent process is discussed.

Fission source convergence diagnosis in Monte Carlo eigenvalue calculations by skewness and kurtosis estimation methods

In this study, a skewness estimation method (SEM) and kurtosis estimation method (KEM) are introduced to determine the number of inactive cycles in Monte Carlo eigenvalue calculations. The SEM and KEM can determine the number of inactive cycles on the basis that fully converged fission source distributions may follow normal distributions without asymmetry or outliers. Two convergence criteria values and a minimum cycle length for the SEM and KEM were determined from skewness and kurtosis analyses of the AGN-201K benchmark and 1D slab problems. The SEM and KEM were then applied to two OECD/NEA slow convergence benchmark problems to evaluate the performance and reliability of the developed methods. Results confirmed that the SEM and KEM provide appropriate and effective convergence cycles when compared to other methods and fission source density fraction trends. Also, the determined criterion value of 0.5 for both ε1 and ε2 was concluded to be reasonable. The SEM and KEM can be utilized as a new approach for determining the number of inactive cycles and judging whether Monte Carlo tally values are fully converged. In the near future, the methods will be applied to various practical problems to further examine their performance and reliability, and optimization will be performed for the convergence criteria and other parameters as well as for improvement of the methodology for practical usage.

Joint Clock Offset and Skew Estimation Based on Correlated Propagation Delays in Internet of Bio-Nano Things

Joint Clock Offset and Skew Estimation Based on Correlated Propagation Delays in Internet of Bio-Nano Things

Multivariate probit linear mixed models for multivariate longitudinal binary data.

When analyzing multivariate longitudinal binary data, we estimate the effects on the responses of the covariates while accounting for three types of complex correlations present in the data. These include the correlations within separate responses over time, cross-correlations between different responses at different times, and correlations between different responses at each time point. The number of parameters thus increases quadratically with the dimension of the correlation matrix, making parameter estimation difficult; the estimated correlation matrix must also meet the positive definiteness constraint. The correlation matrix may additionally be heteroscedastic; however, the matrix structure is commonly considered to be homoscedastic and constrained, such as exchangeable or autoregressive with order one. These assumptions are overly strong, resulting in skewed estimates of the covariate effects on the responses. Hence, we propose probit linear mixed models for multivariate longitudinal binary data, where the correlation matrix is estimated using hypersphere decomposition instead of the strong assumptions noted above. Simulations and real examples are used to demonstrate the proposed methods. An open source R package, BayesMGLM, is made available on GitHub at https://github.com/kuojunglee/BayesMGLM/ with full documentation to produce the results.

Linear extrapolation and model-free option implied moments

This study proposes an approach for assessing the effectiveness of linear extrapolation (LE) for the implied moment estimators even in cases in which the true values of implied moments are unknown. To this end, we develop truncation sensitivity functions for simulation and empirical analyses. LE proves effective for implied volatility, skewness, and kurtosis estimators. However, higher moment (i.e., implied skewness and kurtosis) estimators exhibit sensitivity to truncation, that is, the absence of option prices in the outermost region of the strike price domain, regardless of the use of LE to address truncation.

Training Sequences Design for Simultaneously Transceiver IQ Skew Estimation in Coherent Systems

Training Sequences Design for Simultaneously Transceiver IQ Skew Estimation in Coherent Systems

Resource-Limited Skew Estimation and Correction (RLSEC) for Edge Devices in Delay Non-Tolerant Networks

Resource-Limited Skew Estimation and Correction (RLSEC) for Edge Devices in Delay Non-Tolerant Networks

On approximation of linear regression disturbance distribution

Inference on the parameters of a linear regression usually requires the assumption that the disturbances are normally distributed. But sometimes this assumption does not correspond to the residual distribution. Thus, there is a need to infer the disturbances of the regression model from its residuals. In this article, the analysis is mainly focused on estimation of moments of the disturbances based on residuals. The main goal of this article is derivation of the unbiased estimators of the third and forth central moments of the linear regression disturbances. They are applied to estimation parameters of the random component distribution based on empirical data. In addition to the normal distribution of the random component, the gamma distribution and the class of Pearson curves are considered. An example of skewness and kurtosis coefficient estimation is shown. An example of estimating the dominant of the gamma distribution is shown. Finally, different types of point and interval forecasts of the examined variable are presented.

Cosmic web dissection in fuzzy dark matter cosmologies

ABSTRACT On large cosmological scales, anisotropic gravitational collapse is manifest in the dark cosmic web. Its statistical properties are little known for alternative dark matter (DM) models such as fuzzy dark matter (FDM). In this work, we assess for the first time the relative importance of cosmic nodes, filaments, walls, and voids in a cosmology with primordial small-scale suppression of power. We post-process N-body simulations of FDM-like cosmologies with varying axion mass m at redshifts z ∼ 1.0−5.6 using the NEXUS+ Multiscale Morphology Filter technique at smoothing scale Δx = 0.04 h−1 Mpc. The formation of wall and void halos is more suppressed than naively expected from the half-mode mass M1/2. Also, we quantify the mass- and volume-filling fractions of cosmic environments and find that 2D cosmic sheets host a larger share of the matter content of the Universe as m is reduced, with an ∼8−12 per cent increase for the m = 7 × 10−22 eV model compared to cold dark matter (CDM). We show that in FDM-like cosmologies, filaments, walls, and voids are cleaner and more pronounced structures than in CDM, revealed by a strong mid-range peak in the conditioned overdensity PDFs P(δ). At high redshift, low-density regions are more suppressed than high-density regions. Furthermore, skewness estimates S3 of the total overdensity PDF in FDM-like cosmologies are consistently higher than in CDM, especially at high redshift z ∼ 5.6 where the m = 10−22 eV model differs from CDM by ∼6σ. Accordingly, we advocate for the usage of P(δ) as a testbed for constraining FDM and other alternative DM models.

Modeling Dependence in Survival Times using Log-Skew Normal Shared Frailties

In survival studies, event times under a common influence are often grouped together in clusters. The association between and within these clusters can be studied using frailty models where randomness in the data or heterogeneity arising due to unknown covariates is described using a frailty variable. In shared frailty models, frailty value is common or shared for all observations within a cluster while it is conditionally independent for different clusters. In this article, we consider a model whose baseline distribution is Weibull and shared frailties follow log skew-normal distribution. This distribution increases flexibility of the model as it allows the frailty term to be positively or negatively skewed and estimation of skewness parameter enables us to comment on dependence structure of the random component. A simulation study is performed and Bayesian estimates of treatment effects, variance and skewness of frailty term are obtained using Metropolis-Hastings algorithm. It is shown that while bias and expected loss for estimates of all parameters reduce as dataset size increases, frailty parameters are more efficiently estimated when the random component is considered to be skewed. The model is also applied to two real-life datasets where positive and negative skewness is observed in the frailty term.

Modeling skewness in portfolio choice

AbstractWe seek the best skewness models for portfolio choice decisions. To this end, we compare the predictive ability and portfolio performance of several prominent skewness models in a sample of 10 international equity market indices. Overall, models that employ information from the option markets outperform models that only rely on stock returns. We propose an option‐based skewness estimator that accounts for the skewness risk premium. This estimator offers the most informative forecasts of future skewness, the lowest prediction errors, and the best portfolio performance in most of our tests.

Inter-comparison of wave skewness and asymmetry estimation using wavelet, Fourier and statistical methods

The traditional definition of wavelet bispectrum does not give the quantification of the bispectral content of an area of frequency–frequency space and bicoherence for signals exhibiting rapidly changing amplitudes and limited length can be misleading. To overcome these drawbacks, this research applies a newly defined quantitative wavelet bispectrum to the analysis of wave skewness and asymmetry over typical coastal topographies. Comparing with statistical methods and the Fourier-based bispectrum, the quantitative wavelet bispectrum with a lognormal mother wavelet is validated and can be used to calculate wave skewness and asymmetry. Parameters that can influence the wavelet transform is analyzed. For laboratory cases, number of voices nv=8 is adequate to discretize the interested frequency band with sufficient resolution. The influence of wavelet peak frequency is analyzed. The magnitude of wavelet derived skewness (Sk) and asymmetry (As) increases with an increasing f0. Based on the analysis results, procedures to carry out wavelet transform to analyze wave records is suggested.

The Pricing of Skewness Over Different Return Horizons

While recent theoretical and empirical work suggests that the physical skewness of a stock’s future discrete return distribution prices stocks, it does not tell us over which return horizon(s) that physical skewness is priced. Developing a novel block bootstrap estimator that allows us to calculate realized return skewness over arbitrary horizons, we aim to identify those return horizons. In doing so, we first show that our block bootstrap estimator produces more accurate realized skewness estimates than other recent estimators do. Next, we report that the existing skewness proxies used in the empirical asset pricing literature differ in how well they predict skewness over short or long return horizons. Finally, we reveal that the skewness pricing evidence documented in the empirical asset pricing literature is mostly driven by skewness over short (and not long) return horizons.

A Novel Adaptive Deskewing Algorithm for Document Images.

Document scanning often suffers from skewing, which may seriously influence the efficiency of Optical Character Recognition (OCR). Therefore, it is necessary to correct the skewed document before document image information analysis. In this article, we propose a novel adaptive deskewing algorithm for document images, which mainly includes Skeleton Line Detection (SKLD), Piecewise Projection Profile (PPP), Morphological Clustering (MC), and the image classification method. The image type is determined firstly based on the image’s layout feature. Thus, adaptive correcting is applied to deskew the image according to its type. Our method maintains high accuracy on the Document Image Skew Estimation Contest (DISEC’2013) and PubLayNet datasets, which achieved 97.6% and 80.1% accuracy, respectively. Meanwhile, extensive experiments show the superiority of the proposed algorithm.

Can average skewness really predict financial returns? The euro area case

Jondeau et al. (2020) find evidence that average stock return skewness predicts stock market returns. Although this evidence is consistent with asset pricing theory, we are not able to replicate this result on a broad sample of stock returns taken from the well-developed euro area stock markets. Nor does accounting for potential slower information dissemination or using two alternative skewness estimators lead to finding predictability.

Researcher degrees of freedom in statistical software contribute to unreliable results: A comparison of nonparametric analyses conducted in SPSS, SAS, Stata, and R.

Researcher degrees of freedom can affect the results of hypothesis tests and consequently, the conclusions drawn from the data. Previous research has documented variability in accuracy, speed, and documentation of output across various statistical software packages. In the current investigation, we conducted Pearson's chi-square test of independence, Spearman's rank-ordered correlation, Kruskal-Wallis one-way analysis of variance, Wilcoxon Mann-Whitney U rank-sum tests, and Wilcoxon signed-rank tests, along with estimates of skewness and kurtosis, on large, medium, and small samples of real and simulated data in SPSS, SAS, Stata, and R and compared the results with those obtained through hand calculation using the raw computational formulas. Multiple inconsistencies were found in the results produced between statistical packages due to algorithmic variation, computational error, and statistical output. The most notable inconsistencies were due to algorithmic variations in the computation of Pearson's chi-square test conducted on 2 × 2 tables, where differences in p-values reported by different software packages ranged from .005 to .162, largely as a function of sample size. We discuss how such inconsistencies may influence the conclusions drawn from the results of statistical analyses depending on the statistical software used, and we urge researchers to analyze their data across multiple packages to check for inconsistencies and report details regarding the statistical procedure used for data analysis.

There's a simple fix for skewed pandemic estimates.

Demographers must work together so that officials can produce numbers all can trust. Demographers must work together so that officials can produce numbers all can trust.

Genetic identifications challenge our assumptions of physical development and mother–calf associations and separation times: a case study of the North Atlantic right whale (Eubalaena glacialis)

While photo-identification is an effective tool to monitor individuals in wild populations, it has limitations. Specifically, it cannot be applied to very young animals before their identifying features have stabilized or to dead, decomposed animals. These shortfalls leave gaps in our understanding of survival, parentage, age structure, physical development, and behavioral variability. Here we report on 13 case studies of North Atlantic right whale, Eubalaena glacialis, calves that required genetics to track their life history data. These case studies revealed unexpected variations in mother–calf associations and separation times, as well as calf physical development. Prior to this study, calves were assumed to have died if their mothers were always alone on the feeding ground in the calf’s birth year. Using genetics and photo-identification, four such calves were discovered to be alive; two of the four possibly weaned earlier than expected at 7.5–8.0 months. To put these early separations in context, photo-identification data were queried and revealed that mothers and calves are seen apart from each other on the feeding grounds in 10–40% of all spring/summer sightings; previously, there were no published data on how often pairs are seen apart in the calf’s birth year. Two dead whales initially logged as calves of the year were discovered to be juveniles, thus allowing skewed survival estimates for calves of the year to be corrected. Genetically sampling animals early in their lives before they disperse or separate from their mothers provides an important means of individual identification at a time when photo-identification is not reliable.