Skewed Model Research Articles

Robust and Flexible Maritime ISAR Tracking Algorithm for Multiple Maneuvering Extended Vessels in Heavy-Tailed Clutter Using Skewed Multiple Model MB-Sub-RMM-TBD Filter

Robust and Flexible Maritime ISAR Tracking Algorithm for Multiple Maneuvering Extended Vessels in Heavy-Tailed Clutter Using Skewed Multiple Model MB-Sub-RMM-TBD Filter

Morphological Evidence for the eROSITA Bubbles Being Giant and Distant Structures

There are two contradictory views of the eROSITA bubbles: either a 104 pc scale pair of giant bubbles blown by the Galactic center (GC), or a 102 pc scale local structure coincidentally located in the direction of GC. A key element of this controversy is the distance to the bubbles. Based on the 3D dust distribution in the Galactic plane, we found three isolated, distant (500–800 pc) clouds at intermediate Galactic latitudes. Their projected morphologies perfectly match the X-ray shadows on the defining features of the north eROSITA bubble, i.e., the North Polar Spur (NPS) and the Lotus Petal Cloud (LPC), indicating that both the NPS and LPC are distant, with a distance lower limit of nearly 1 kpc. In the X-ray-dark region between the NPS and LPC, we found a few polarized radio arcs and attributed them to the bubble’s shock front. These arcs match up perfectly with the outer border of the NPS and LPC and provide a way to define the bubble’s border. The border defined in this way can be well described by the line-of-sight tangent of a 3D skewed cup model rooted in the GC. We conclude that, instead of being two independent, distant features, the NPS and LPC compose a single, giant bubble, which therefore is most plausibly a 10 kpc scale bubble rooted at the GC.

Generating realistic data through modeling and parametric probability for the numerical evaluation of data processing algorithms in two-dimensional chromatography

BackgroundComprehensive two-dimensional chromatography generates complex data sets, and numerous baseline correction and noise removal algorithms have been proposed in the past decade to address this challenge. However, evaluating their performance objectively is currently not possible due to a lack of objective data. ResultTo tackle this issue, we introduce a versatile platform that models and reconstructs single-trace two-dimensional chromatography data, preserving peak parameters. This approach balances real experimental data with synthetic data for precise comparisons. We achieve this by employing a Skewed Lorentz-Normal model to represent each peak and creating probability distributions for relevant parameter sampling. The model's performance has been showcased through its application to two-dimensional gas chromatography data where it has created a data set with 458 peaks with an RMSE of 0.0048 or lower and minimal residuals compared to the original data. Additionally, the same process has been shown in liquid chromatography data. SignificanceData analysis is an integral component of any analytical method. The development of new data processing strategies is of paramount importance to tackle the complex signals generated by state-of-the-art separation technology. Through the use of probability distributions, quantitative assessment of algorithm performance of new algorithms is now possible. Therefore, creating new opportunities for faster, more accurate, and simpler data analysis development.

Non-uniform inlet flow definition for highly skewed model propeller by geometric partitioning

Ship design involves many calculations to provide a propulsion system with the appropriate speed. Prioritizing speed-resistance calculations affects main engine, gearbox, propeller, and other auxiliary system selection. Here, the propeller is most intriguing. Since the propeller operates in a fluid domain, internal and external factors affect it. Despite being designed for service speed, water flow can affect the efficiency of a propeller at the ship's stern. Under open-water circumstances, a ship's hull affects a propeller's hydrodynamic performance. The effect usually decreases propeller efficiency, so it's important to know how ship hull and propeller design interact before production. To overcome this issue, researchers test towing tanks extensively. Computational Fluid Dynamics, which is efficient and cost-effective, is also commonly used. This study examines the hydrodynamics of a fishing vessel's highly skewed model propeller. The objective of this study was to assess the propeller's efficiency under uniform and non-uniform flow conditions and investigate an alternative method for dividing the inlet area, which has not been studied in the literature. The study has effectively met its goals, producing satisfactory error margins for both operating conditions. This research significantly contributes to understanding the relationship between hull configuration and propeller design, particularly in non-uniform flow scenarios.

Investigating accuracy of biomarker involving a parametric approach of proportional hazard skewed normal model

Investigating accuracy of biomarker involving a parametric approach of proportional hazard skewed normal model

An Exploratory Analysis Examining the Relationship Between Protective and Risk Factors of Bullying and Hate Crime Victimization Within Schools

ABSTRACT Over the years, scholars have argued that there are similarities and differences between bullying and hate crimes within Americas’ schools. However, no known study has tested the claims that bullying and hate-related victimization are more similar (or different) and whether they share similar protective and/or risk factors. Utilizing the 2019 NCVS School Crime Supplement, the present study draws on several theoretical traditions to examine the protective and risk factors associated with bullying and hate-related victimization in schools. Skewed logistic regression models find that while there are some similarities between the protective and risk factors among bullying and hate-related victimization, there are also some noticeable and distinct differences. The findings fill important theoretical and empirical gaps in the bullying and hate crime literature. Further, the findings offer important policy implications as it relates to detection and prevention of bullying and hate-related victimization within schools.

Analysis of age-specific fertility in India: Deterministic and non-deterministic modeling approaches

The main objective of this study is to investigate the pattern of age-specific fertility rates (ASFRs) in India using deterministic and non-deterministic approaches. Toward this end, we proposed statistical polynomial regression models to study the distributional pattern of ASFRs for total, rural, and urban women in India. Further, a comparative study considering selected skewed regression models was undertaken. For this study, secondary data on ASFR were collected from Sample Registration System, Statistical Report-2020, and from National Family Health Survey 5 (NFHS-5; 2019 – 2021). It was found that all three subcategories of ASFRs, namely, the total, rural, and urban ASFRs, followed the reciprocal biquadratic polynomial model. On the other hand, all three subcategories of ASFR follow the skew-normal type 2 distribution. Similar findings were also obtained and validated based on NFHS-5 data. Further, the chosen statistical models’ validity and stability were tested using various model validation techniques and model selection criteria.

A Cashless Society: Consumer Perception towards the Use of Digital Transactions

With the advancement of technology, the acceptance the digital electronic transaction systems among the Bhutanese consumers have increased over the years. This paper examines the level of awareness among the Bhutanese consumers about digital transactions and investigates the factors affecting Bhutanese households’ perception of digital transactions. Primary data were collected by using multi-stage sampling technique from 100 Bhutanese household across twenty districts in the country for the year 2021-2022. Data were analyzed using mean, standard deviation, skewness, kurtosis and regression model to draw the results. The results showed that the respondents are more aware in terms of using ATMs and mobile apps mode of payments as compared to the latest modes of digital payments.

Multi-stage skewed grey cloud clustering model and its application

PurposeThe possibility function-based grey clustering model has evolved into a complete approach for dealing with uncertainty evaluation problems. Existing models still have problems with the choice dilemma of the maximum criteria and instances when the possibility function may not accurately capture the data's randomness. This study aims to propose a multi-stage skewed grey cloud clustering model that blends grey and randomness to overcome these problems.Design/methodology/approachFirst, the skewed grey cloud possibility (SGCP) function is defined, and its digital characteristics demonstrate that a normal cloud is a particular instance of a skewed cloud. Second, the border of the decision paradox of the maximum criterion is established. Third, using the skewed grey cloud kernel weight (SGCKW) transformation as a tool, the multi-stage skewed grey cloud clustering coefficient (SGCCC) vector is calculated and research items are clustered according to this multi-stage SGCCC vector with overall features. Finally, the multi-stage skewed grey cloud clustering model's solution steps are then provided.FindingsThe results of applying the model to the assessment of college students' capacity for innovation and entrepreneurship revealed that, in comparison to the traditional grey clustering model and the two-stage grey cloud clustering evaluation model, the proposed model's clustering results have higher identification and stability, which partially resolves the decision paradox of the maximum criterion.Originality/valueCompared with current models, the proposed model in this study can dynamically depict the clustering process through multi-stage clustering, ensuring the stability and integrity of the clustering results and advancing grey system theory.

Imaging the Inner Astronomical Unit of the Herbig Be Star HD 190073

The inner regions of protoplanetary disks host many complex physical processes such as star–disk interactions, magnetic fields, planet formation, and the migration of new planets. To study directly this region requires milliarcsecond angular resolution, beyond the diffraction limit of the world's largest optical telescopes and even too small for the millimeter-wave interferometer Atacama Large Millimeter/submillimeter Array (ALMA). However, we can use infrared interferometers to image the inner astronomical unit. Here, we present new results from the CHARA and VLTI arrays for the young and luminous Herbig Be star HD 190073. We detect a sub-astronomical unit (sub-AU) cavity surrounded by a ring-like structure that we interpret as the dust destruction front. We model the shape with six radial profiles, three symmetric and three asymmetric, and present a model-free image reconstruction. All the models are consistent with a near face-on disk with an inclination ≲20°, and we measure an average ring radius of 1.4 ± 0.2 mas (1.14 au). Around 48% of the total flux comes from the disk with 15% of that emission appearing to emerge from inside the inner rim. The cause of emission is still unclear, perhaps due to different dust grain compositions or gas emission. The skewed models and the imaging point to an off-center star, possibly due to binarity. Our image shows sub-AU structure, which seems to move between the two epochs inconsistently with Keplerian motion and we discuss possible explanations for this apparent change.

Exploring the Dynamics of COVID-19 with a Novel Family of Models

Much effort has recently been expended in developing efficient models that can depict the true picture for COVID-19 mortality data and help scientists choose the best-fit models. As a result, this research intends to provide a new G family for both theoretical and practical scientists that solves the concerns typically encountered in both normal and non-normal random events. The new-G distribution family is able to generate efficient continuous univariate and skewed models that may outperform the baseline model. The analytic properties of the new-G family and its sub-model are investigated and described, as well as a theoretical framework. The parameters were estimated using a classical approach along with an extensive simulation study to assess the behaviour of the parameters. The efficiency of the new-G family is discussed using one of its sub-models on COVID-19 mortality data sets.

A time-varying skewness model for Growth-at-Risk

This paper studies macroeconomic risks in a panel of advanced economies based on a stochastic volatility model in which macro-financial conditions shape the predictive growth distribution. We find sizable time variation in the skewness of these distributions, conditional on the macro-financial environment. Tightening financial conditions signal increasing downside risk in the short term, but this link reverses at longer horizons. When forecasting downside risk, the proposed model, on average, outperforms existing approaches based on quantile regression and a GARCH model, especially at short horizons. In forecasting upside risk, it improves the average accuracy for several horizons up to four quarters ahead. The suggested approach can inform policymakers’ assessment of macro-financial vulnerabilities by providing a timely signal of shifting risks and a quantification of their magnitude.

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.

A New Skew-Symmetric Gudermannian-Laplace Distribution with Properties and Application to Wind Speed Data

We introduce a novel probability distribution that may be used to model both skewed and symmetric data. This new distribution, called the skew-symmetric Gudermanian-Laplace (SSGL) distribution, includes a shape parameter that allows it to change the asymmetry. Some fundamental statistical properties of the new distribution have been given explicit analytical expressions. The study also includes parameter estimations and simulation sections. We considered two datasets in the real-world data application. The first dataset is the "heights of 100 Australian athletes" data, which is discussed in many studies examining alternative skewed models. The second dataset contains the average wind speeds recorded by the İstanbul Çatalca meteorological observatory in January 2020. We showed that the SSGL distribution outperforms its well-known alternative, the Skew-Normal distribution, in both datasets. As a result of the study, it was concluded that the SSGL distribution is a suitable alternative for modeling skewed data.

The analysis of time-varying high-order moment of wind power time series

The efficient wind power forecasting is of great significance for the safety and stability of power grid. Analysis on the high order moment of wind power time series play an important role in improving the forecasting accuracy of wind power. With the rolling subsample generalized auto-regressive conditional heteroskedasticity with skewness and kurtosis (GARCHSK) model sequence, the analysis method for conditional skewness and conditional kurtosis of wind power time series is proposed. The dynamic curves of parameter are presented by estimating the characteristic parameters in the subsample models. Moreover, the time-varying structure of high order moment of the wind power time series is analyzed. A Generalized News Impact Curve (GNIC) is proposed, and the impact of news on the conditional skewness and conditional kurtosis are measured and evaluated by the third order GNIC and the fourth order GNIC. The result of case based on the wind power data of Jiangsu power grid demonstrates that the time-varying structure of high-order moments of wind power time series is steady relatively, and the time-varying characteristics of high-order moments are further verified by GNIC.

An element-wise machine learning strategy to predict glass-forming range of ternary alloys enabled by comprehensive data

Many works have reported machine learning (ML) to predict the glass-forming range (GFR) of metallic glasses. However, the datasets used to train the model were mostly imbalanced and clustered around only a small portion of the composition space that made the model hard to extrapolate. In this work, the generalization deficiency of the ML model was addressed by combining combinatorial materials chip (CMC) high-throughput (HiTp) experimentation and ML modelling, and the importance of comprehensive data was highlighted. By training the models with the HiTp dataset and the published stacked dataset, it is found that 1) the data imbalance can easily lead to a skewed model, and 2) the comprehensive and balanced data distribution over the composition space dominates the model's performance. An element-wise ML strategy that the number of predictable ternary systems could be the combination of the dominant elements was demonstrated, which could dramatically accelerate the identification of GFR.

Practical considerations on data patterns in Bayesian Maximum Entropy Estimation: A systematic and critical review

Objective: It is well known that some data features (sample size, skewness, among others) may determine method performance. The choice of those features depends on the researcher’s level of awareness on the statistical method. In this study, the level of awareness on the influence of spatial data key characteristics (sample size, skewness, spatial dependency and variogram model) in Bayesian Maximum Entropy (BME) was analyzed. Methodology: A systematic review was conducted that covers the period from 1990 (year of BME introduction) to 2019. Two main keywords “Bayesian Maximum Entropy” and “BME” were used for literature search. Publications which only mentioned the keywords without applying BME were excluded while those with application and/or BME theory discussion were considered. Six of the world’s leading Open Access sources of scientific literature were considered, namely: Science Direct, African Journals Online, Springer, Google Scholar, MPDI and Academic Journals. A total of 118 research articles from 62 journals were identified. The sample sizes screened shows that 25.4% of the published articles used few samples (less than 100), which implies the variogram might not yield accurate results. The analysis of the use of skewness showed that most researchers do not apply transformation on skewed data (82.2%) nor consider skewness in their descriptive statistics (90.7%). Even though 11% of theoretical papers have mentioned about spatial dependency level, 92.4% of them failed to consider it. Most researchers (68.64%) do not specify the variogram models but when they do, they mostly use exponential model (12.7%). It clearly appears in this review that most researchers do not consider the effect of sample size, skewness, and spatial dependency level when applying BME. Yet very few research works have focused on these aspects. This therefore calls for more in-depth studies on the effect of data characteristics on BME’s performance. Keywords: Bayesian Maximum Entropy, sample size, skewness, spatial dependency.

Stock Selection Using Skewness to Construct a Portfolio and the Effects of Variables on Portfolio Return

This study aims to investigate the effects of stock selection while constructing a portfolio using Skewness as well as the factors affecting portfolio return. This study was carried out in three stages: stock selection based on skewness, asset allocation based on Quadratic Programming, and portfolio return calculation based on market return and external factors. To assess and select portfolios, this study employs a novel methodology that combines key financial and non-financial characteristics with a skewness model. The research’s findings are as follows. First, skewness could be used to select the stocks that are added to a portfolio. Second, the market capitalization weighted portfolio generated the best return compared to the other two portfolios. Third, market return and the pandemic era have a significant impact on portfolio returns that are equally weighted, market capitalization weighted, and Markowitz weighted. Fourth, investors do not require fund management expertise to manage investor funds.

An analysis of East African tea crop prices using the MCMC approach to estimate volatility and forecast the in-sample value-at-risk

In Eastern African countries, agriculture contributes significantly to the national economy. However, the prices of the essential crops fluctuate considerably due to climate change, the economic crisis, and surging food and fuel costs in the region. This paper aims to capture the dynamic volatility of tea crop prices, one of the region's most important cash crops. We applied the Markov-switching GARCH (MS-GARCH)-type specifications with different scedastic functions and error distributions to estimate volatility and forecast the in-sample value-at-risk (VaR) of tea price returns. This paper analyses monthly tea auction prices (i.e., Mombasa auction) in USD from January 1980 to June 2022. The parameters of the MS-GARCH model are estimated in a Bayesian framework via the MCMC approach. The findings evidenced that the EGARCH skewed Student-t model with three regimes was superior in estimating volatility. In terms of VaR evaluation, there is no superior method, but considering scedastic functions, fat tails, asymmetry, and regime switching is more effective. To manage volatility and risk in the East African tea sector, investors should consider regime switching GARCH models.

On skewed Gaussian graphical models

We introduce a skewed Gaussian graphical model as an extension to the Gaussian graphical model. One of the appealing properties of the Gaussian distribution is that conditional independence can be fully characterized by the sparseness in the precision matrix. The skewed Gaussian distribution adds a shape parameter to the Gaussian distribution to take into account possible skewness in the data; thus it is more flexible than the Gaussian model. Nevertheless, the appealing property of the Gaussian distribution is retained to a large degree: the conditional independence is still characterized by the sparseness in the parameters, which now include a shape parameter in addition to the precision matrix. As a result, the skewed Gaussian graphical model can be efficiently estimated through a penalized likelihood method just as the Gaussian graphical model. We develop an algorithm to maximize the penalized likelihood based on the alternating direction method of multipliers, and establish the asymptotic normality and variable selection consistency for the new estimator. Through simulations, we demonstrate that our method performs better than the Gaussian and Gaussian copula methods when these distributional assumptions are not satisfied. The method is applied to a breast cancer MicroRNA dataset to construct a gene network, which shows better interpretability than the Gaussian graphical model.