GC skew denotes the relative excess of G nucleotides over C nucleotides on the leading versus the lagging replication strand of eubacteria. While the effect is small, typically around 2.5%, it is robust and pervasive. GC skew and the analogous TA skew are a localized deviation from Chargaff's second parity rule, which states that G and C, and T and A occur with (mostly) equal frequency even within a strand.

Most bacteria also show the analogous TA skew. Different phyla show different kinds of skew and differing relations between TA and GC skew.
This article introduces an open access database (https://skewdb.org) of GC and 10 other skews for over 28,000 chromosomes and plasmids. Further details like codon bias, strand bias, strand lengths and taxonomic data are also included.

The SkewDB database can be used to generate or verify hypotheses. Since the origins of both the second parity rule, as well as GC skew itself, are not yet satisfactorily explained, such a database may enhance our understanding of microbial DNA.

This repository contains raw data files and base codes to analyze them.A. The 'powerx_y.xlsx' files are the data files with the one dimensional trajectory of optically trapped probes modulated by an Ornstein-Uhlenbeck noise of given 'x' amplitude. For the corresponding diffusion amplitude A=0.1X(0.6X10-6)2 m2/s, x is labelled as '1'B. The codes are of three types. The skewness codes are used to calculate the skewness of the trajectory. The error_in_fit codes are used to calculate deviations from arcsine behavior. The sigma_exp codes point to the deviation of the mean from 0.5. All the codes are written three times to look ar T+, Tlast and Tmax.C. More information can be found in the manuscript.

"NewEngland_pkflows.PRT" is a text file that contains results of flood-frequency analysis of annual peak flows from 186 selected streamflow gaging stations (streamgages) operated by the U.S. Geological Survey (USGS) in the New England region (Maine, Connecticut, Massachusetts, Rhode Island, New York, New Hampshire, and Vermont). Only streamgages in the region that were also in the USGS "GAGES II" database (https://water.usgs.gov/GIS/metadata/usgswrd/XML/gagesII_Sept2011.xml) were considered for use in the study. The file was generated by combining PeakFQ output (.PRT) files created using version 7.0 of USGS software PeakFQ (https://water.usgs.gov/software/PeakFQ/; Veilleux and others, 2014) to conduct flood-frequency analyses using the Expected Moments Algorithm (England and others, 2018). The peak-flow files used as input to PeakFQ were obtained from the USGS National Water Information System (NWIS) database (https://nwis.waterdata.usgs.gov/usa/nwis/peak) and contained annual peak flows ending in water year 2011. Results of the flood-frequency analyses were used to estimate skewness of annual peak flows in the New England region using Bayesian Weighted Least Squares / Bayesian Generalized Least Squares (B-WLS / B-GLS) regression (Veilleux and others, 2019).

A single regression model is unlikely to hold throughout a large and complex spatial domain. A finite mixture of regression models can address this issue by clustering the data and assigning a regression model to explain each homogenous group. However, a typical finite mixture of regressions does not account for spatial dependencies. Furthermore, the number of components selected can be too high in the presence of skewed data and/or heavy tails. Here, we propose a mixture of regression models on a Markov random field with skewed distributions. The proposed model identifies the locations wherein the relationship between the predictors and the response is similar and estimates the model within each group as well as the number of groups. Overfitting is addressed by using skewed distributions, such as the skew-t or normal inverse Gaussian, in the error term of each regression model. Model estimation is carried out using an EM algorithm, and the performance of the estimators and model selection are illustrated through an extensive simulation study and two case studies.

To improve flood-frequency estimates at rural streams in Mississippi, annual exceedance probability (AEP) flows at gaged streams in Mississippi and regional-regression equations, used to estimate annual exceedance probability flows for ungaged streams in Mississippi, were developed by using current geospatial data, additional statistical methods, and annual peak-flow data through the 2013 water year. The regional-regression equations were derived from statistical analyses of peak-flow data, basin characteristics associated with 281 streamgages, the generalized skew from Bulletin 17B (Interagency Advisory Committee on Water Data, 1982), and a newly developed study-specific skew for select four-digit hydrologic unit code (HUC4) watersheds in Mississippi. Four flood regions were identified based on residuals from the regional-regression analyses. No analysis was conducted for streams in the Mississippi Alluvial Plain flood region because of a lack of long-term streamflow data and poorly defined basin characteristics. Flood regions containing sites with similar basin and climatic characteristics yielded better regional-regression equations with lower error percentages. The generalized least squares method was used to develop the final regression models for each flood region for annual exceedance probability flows. The peak-flow statistics were estimated by fitting a log-Pearson type III distribution to records of annual peak flows and then applying two additional statistical methods: (1) the expected moments algorithm to help describe uncertainty in annual peak flows and to better represent missing and historical record; and (2) the generalized multiple Grubbs-Beck test to screen out potentially influential low outliers and to better fit the upper end of the peak-flow distribution. Standard errors of prediction of the generalized least-squares models ranged from 28 to 46 percent. Pseudo coefficients of determination of the models ranged from 91 to 96 percent. Flood Region A, located in north-central Mississippi, contained 27 streamgages with drainage areas that ranged from 1.41 to 612 square miles. The 1% annual exceedance probability had a standard error of prediction of 31 percent which was lower than the prediction errors in Flood Regions B and C.

In the fixed-effects stochastic frontier model an efficiency measure relative to the best firm in the sample is universally employed. This paper considers a new measure relative to the worst firm in the sample. We find that estimates of this measure have smaller bias than those of the traditional measure when the sample consists of many firms near the efficient frontier. Moreover, a two-sided measure relative to both the best and the worst firms is proposed. Simulations suggest that the new measures may be preferred depending on the skewness of the inefficiency distribution and the scale of efficiency differences.

While classical measurement error in the dependent variable in a linear regression framework results only in a loss of precision, nonclassical measurement error can lead to estimates which are biased and inference which lacks power. Here, we consider a particular type of nonclassical measurement error: skewed errors. Unfortunately, skewed measurement error is likely to be a relatively common feature of many out- comes of interest in political science research. This study highlights the bias that can result even from relatively "small" amounts of skewed measurement error, particularly if the measurement error is heteroskedastic. We also assess potential solutions to this problem, focusing on the stochastic frontier model and nonlinear least squares. Simulations and three replications highlight the importance of thinking carefully about skewed measurement error, as well as appropriate solutions.

1. Structured population models are among the most widely used tools in ecology and evolution. Integral projection models (IPMs) use continuous representations of how survival, reproduction, and growth change as functions of state variables such as size, requiring fewer parameters to be estimated than projection matrix models (PPMs). Yet almost all published IPMs make an important assumption: that size-dependent growth transitions are or can be transformed to be normally distributed. In fact, many organisms exhibit highly skewed size transitions. Small individuals can grow more than they can shrink, and large individuals may often shrink more dramatically than they can grow. Yet the implications of such skew for inference from IPMs has not been explored, nor have general methods been developed to incorporate skewed size transitions into IPMs, or deal with other aspects of real growth rates, including bounds on possible growth or shrinkage. 2. Here we develop a flexible approach to modeling skewed growth data using a modified beta regression model. We propose that sizes first be converted to a (0,1) interval by estimating size-dependent minimum and maximum sizes through quantile regression. Transformed data can then be modeled using beta regression with widely available statistical tools. We demonstrate the utility of this approach using demographic data for a long-lived plant, gorgonians, and an epiphytic lichen. Specifically, we compare inferences of population parameters from discrete PPMs to those from IPMs that either assume normality or incorporate skew using beta regression or, alternatively, a skewed normal model. 3. The beta and skewed normal distributions accurately capture the mean, variance, and skew of real growth distributions. Incorporating skewed growth into IPMs decreases population growth and estimated lifespan relative to IPMs that assume normally-distributed growth, and more closely approximate the parameters of PPMs that do not assume a particular growth distribution. A bounded distribution, such as the beta, also avoids the eviction problem caused by predicting some growth outside the modeled size range. 4. Incorporating biologically relevant skew in growth data has important consequences for inference from IPMs. The approaches we outline here are flexible and easy to implement with existing statistical tools.

We propose a multivariate normality test against skew normal distributions using higher-order log-likelihood derivatives, which is asymptotically equivalent to the likelihood ratio but only requires estimation under the null. Numerically, it is the supremum of the univariate skewness coefficient test over all linear combinations of the variables. We can simulate its exact finite sample distribution for any multivariate dimension and sample size. Our Monte Carlo exercises confirm its power advantages over alternative approaches. Finally, we apply it to the joint distribution of US city sizes in two consecutive censuses finding that non-normality is very clearly seen in their growth rates.

This project examines whether people have an intrinsic preference for negatively skewed or positively skewed information structures and how these preferences relate to intrinsic preferences for informativeness. It reports results from 5 studies (3 lab experiments, 2 online studies).

This dataset contains upper air Skew-T Log-P charts taken at Boise, Idaho during the ICE-L project. The imagery are in GIF format. The imagery cover the time span from 2007-11-08 12:00:00 to 2008-01-03 12:00:00.

We are submitting the updated data and codes for replicating the analysis in the revised manuscript, "Does Average Skewness Matter? Evidence from the Taiwanese Stock Market".

This dataset contains annual peak-flow data, PeakFQ specifications, and results of flood-frequency analyses of annual peak flows for 368 selected streamflow gaging stations (streamgages) operated by the U.S. Geological Survey (USGS) in the Great Lakes and Ohio River basins. "PeakFQinput_all.txt" contains annual peak-flow data, ending in water year 2013, for all 368 streamgages in the study area. Annual peak-flow data were obtained from the USGS National Water Information System (NWIS) database (https://nwis.waterdata.usgs.gov/usa/nwis/peak). "PeakFQspec_all.psf" contains PeakFQ specifications for all 368 streamgages in the study area. The specifications were developed by hydrologists in the various USGS Water Science Centers that participated in the study. "PeakFQoutput_all.PRT" contains the results of flood-frequency analyses of annual peak-flow data, for each of the 368 streamgages in the study area, that were conducted using the Expected Moments Algorithm (England and others, 2018). Using the annual peak-flow data in "PeakFQinput_all.txt" and the specifications in "PeakFQspec_all.psf", "PeakFQoutput_all.PRT" was generated in version 7.2 of USGS flood-frequency analysis software PeakFQ (https://water.usgs.gov/software/PeakFQ/; Veilleux and others, 2014). Results of the flood-frequency analyses were used to estimate regional skew for the study area using Bayesian Weighted Least Squares / Bayesian Generalized Least Squares (B-WLS / B-GLS) regression.

Customs records of Netherlands Antilles are available for SKEW. Learn about its Importer, supply capabilities and the countries to which it supplies goods

These data set reports the data and script used to produce the paper :

" Incremental Nonlinear Dynamic Inversion controller for a Variable Skew Quad Plane ".

The data refers to the the novel Variable Skew Quad plane that has been tested in the Open Jet Facility wind tunnel of TuDelft. The objective of the experiments is to characterize the control capabilities of VSQP. The data collected are the forces and moments exerted by the drone at different state combinations. The data has been aquired through OJF externam moment balance.

This dataset accompanies the paper titled "Unified Actuator Nonlinear Dynamic Inversion controller for the Variable Skew Quad Plane." It includes flight test data, simulation data, post-processing scripts, and derivations. The presented controller is demonstrated to be superior in tracking position and attitude trajectories compared to an Incremental Nonlinear Dynamic Inversion controller. The dataset contains simulation and real indoor testing data comparing the trajectory tracking performance of the two controllers.

This dataset contains upper air Skew-T Log-P data collected at Denver during the HIPPO-4 project. The imagery are in GIF format. The imagery cover the time span from 2011-06-08 12:00:00 to 2011-07-13 12:00:00.

Supplementary Material 2: A supplementary file with examples of STATA script for all models that have been fitted in this paper.

Remarkable variation exists in the distribution of reproduction (skew) among members of cooperatively breeding groups, both within and between species. Reproductive skew theory has provided an important framework for understanding this variation. In the primitively eusocial Hymenoptera, two models have been routinely tested: concessions models, which assume complete control of reproduction by a dominant individual, and tug-of-war models, which assume on-going competition among group members over reproduction. Current data provide little support for either model, but uncertainty about the ability of individuals to detect genetic relatedness and difficulties in identifying traits conferring competitive ability mean that the relative importance of concessions versus tug-of-war remains unresolved. Here, we suggest that the use of social parasitism to generate meaningful variation in key social variables represents a valuable opportunity to explore the mechanisms underpinning reproductive skew within the social Hymenoptera. We present a direct test of concessions and tug-of-war models in the paper wasp Polistes dominulus by exploiting pronounced changes in relatedness and power structures that occur following replacement of the dominant by a congeneric social parasite. Comparisons of skew in parasitized and unparasitized colonies are consistent with a tug-of-war over reproduction within P. dominulus groups, but provide no evidence for reproductive concessions.

Quantile regression provides a powerful tool to study the effects of covariates on key quantiles of conditional distribution. Yet we often still lack a general picture about how covariates affect the overall shape of conditional distribution. Using quantile regression estimation and quantile-based measures of spread, skewness and kurtosis, we propose spread regression, skewness regression and kurtosis regression as empirical tools to quantify the effects of covariates on the spread, skewness and kurtosis of conditional distribution. This methodology is then applied to the U.S. wage data during 1980-2019 with substantive findings, and a comparison is made with a moment-based robust approach. In addition, we decompose changes in the spread into composition effects and structural effects as an effort to understand rising inequality. We also provide Stata commands spreadreg, skewreg and kurtosisreg available from SSC for easy implementation of spread, skewness and kurtosis regressions.