Asymmetric Data Research Articles

Likelihood-Based Inference for the Asymmetric Exponentiated Bimodal Normal Model

Asymmetric probability distributions have been widely studied by various authors in recent decades, who have introduced new families of flexible distributions in terms of skewness and kurtosis than the classical distributions known in statistical theory. Most of the new distributions fit unimodal data, others fit bimodal data, however, in the bimodal, singularity problems have been found in their information matrices in most of the proposals presented. In contrast, in this paper an extension of the family of alpha-power distributions was developed, which has a non-singular information matrix, based on the bimodal-normal and bimodal elliptic-skew-normal probability distributions. These new extensions model asymmetric bimodal data commonly found in various areas of scientific interest. The properties of these new probabilistic distributions were also studied in detail and the respective statistical inference process was carried out to estimate the parameters of these new models. The stochastic convergence for the vector of maximum likelihood estimators could be found due to the non-singularity of the expected information matrix in the corresponding support.

BAYESIAN ESTIMATION FOR THE STABLE DISTRIBUTIONS IN THE PRESENCE OF COVARIATES WITH APPLICATIONS IN CLINICAL ISSUES

In this paper we explore a Bayesian approach for stable distributions in presence of covariates. This class of distribution has great flexibility for fitting asymmetric and heavy-tailed empirical data. These models are commonly used for data sets in finance and insurance. In this paper we show that these distributions can also be used to fit clinical data. Since there is not an analytical form for the density probability function which implies in serious difficulties to obtain the maximum likelihood estimators for the parameters, we use Bayesian methods with data augmentation techniques to get the inferences of interest. In this study we also discuss the choice of different prior distributions for the parameters considering regression models for the location and scale parameters of the stable distribution. We use MCMC (Markov Chain Monte Carlo) algorithms to generate samples from the posterior distributions in order to evaluate the point and interval estimators. A great simplification is obtained using the OpenBugs software. Two real data examples illustrate the applicability of the proposed modeling approach.

Analysis of Data-centric Financial Governance System

In the era of digital economy, data is a new production factor, and it’s economic and value attributes are constantly being discovered and valued. The financial industry has a large amount of high-value data information, but there are still problems such as unclear data ownership, inconsistent data quality, and asymmetric data information, which lead to fragmentation of financial data governance and seriously hinder the full play of the value of data. Thus, how to establish a financial governance system with data factor is particularly urgent.

Modeling asymmetrically dependent multivariate ocean data using truncated copulas

Characterizing multivariate ocean parameters is quite important for offshore engineering reliability design and risk assessment. To fully understand ocean conditions, a robust and accurate multivariate model is essential for the analysis and estimation of the ocean state. Therefore, advanced simulation of the ocean parameters helps to improve practices in offshore engineering. In this work, the principle of a new type of copula, namely truncated copula, is developed and adopted for modeling the multivariate ocean data. Unlike previous studies on modeling asymmetric ocean data by purely mathematical fitting techniques, this study proposes a truncated method based on physical limits to study asymmetrically dependent ocean data. The truncated copula method is contrasted with the conventional symmetric and existing asymmetric copula from the literature using real environmental observations for the demonstration. Various commonly used traditional copula models are modified by the proposed truncation technique and applied to fit multivariate ocean data collected in buoys off the US coast. Based on the fitting of ocean data, this paper compares the advantages and disadvantages of different copula models. The properties of different copula models for data simulation and extreme value prediction are also discussed.

Resource Allocation Algorithm Based on Power Control and Dynamic Transmission Protocol Configuration for HAPS-IMT Integrated System

The high altitude platform station (HAPS) system is an essential component of the air-based network. It can shorten transmission delay and make a better user experience compared with satellite networks, and it can also be easily deployed and cover a larger area compared with international mobile telecommunications (IMT). In order to meet the needs of users with asymmetric and random data flow, the spectrum sharing and dynamic time division duplexing (TDD) mode are used in HAPS-IMT heterogeneous network. However, the cross-link interference brought by TDD mode will lead to the degradation of system performance. In this paper, a resource allocation algorithm based on power control and dynamic transmission protocol configuration is proposed. Firstly, a specific timeslot, “low power almost-bank subframe (LP-ABS)”, is introduced into the frame structure of the HAPS physical layer. The transmission protocol designing could mitigate inter-layer interference efficiently by power control in “LP-ABS”. Secondly, the utilization function is adopted for assessing the system performance, which gives attention to both diversified requirements on the quality of services (QoS) and the throughput of the HAPS-IMT system. Simulation results show that power control and resource allocation technologies proposed in this paper can effectively improve system performance and user satisfaction.

Coverage and Spectral Efficiency of Network Assisted Full Duplex in a Millimeter Wave System

To cope with the growing trend of asymmetric data traffic, we introduce a novel network assisted full duplex (NAFD) for a millimeter wave system. NAFD can dynamically allocate the number of remote radio heads in the uplink mode or in the downlink mode, which can facilitate simultaneous uplink and downlink communications. In this manuscript, we use stochastic geometry to analyze the distribution of the signal-to-interference-plus-noise ratio and the data rate in a NAFD system. The numerical results verify the analysis and show that the NAFD outperforms the dynamic time division duplex system and the traditional flexible duplex system in terms of spectral efficiency.

Asymmetric Density for Risk Claim-Size Data: Prediction and Bimodal Data Applications

A new, flexible claim-size Chen density is derived for modeling asymmetric data (negative and positive) with different types of kurtosis (leptokurtic, mesokurtic and platykurtic). The new function is used for modeling bimodal asymmetric medical data, water resource bimodal asymmetric data and asymmetric negatively skewed insurance-claims payment triangle data. The new density accommodates the “symmetric”, “unimodal right skewed”, “unimodal left skewed”, “bimodal right skewed” and “bimodal left skewed” densities. The new hazard function can be “decreasing–constant–increasing (bathtub)”, “monotonically increasing”, “upside down constant–increasing”, “monotonically decreasing”, “J shape” and “upside down”. Four risk indicators are analyzed under insurance-claims payment triangle data using the proposed distribution. Since the insurance-claims data are a quarterly time series, we analyzed them using the autoregressive regression model AR(1). Future insurance-claims forecasting is very important for insurance companies to avoid uncertainty about big losses that may be produced from future claims.

Asymmetric Data Hiding for Compressed Images with High Payload and Reversibility

Hiding secret data in digital images is an attractive topic in the information security research area. Because the data-embedded stego image looks exactly the same as a regular image, transmitting secret data with stego images does not draw the attention of eavesdroppers, thus fulfilling the goal of information security. Many reversible data hiding (RDH) methods for absolute moment block truncation coding (AMBTC) compressed images have been proposed. These methods hide secret data in an AMBTC-compressed image to produce a stego image and transmit it to the recipient. Upon receiving the stego image, the recipient can extract the secret data and recover the AMBTC-compressed image. In this paper, we propose an RDH scheme for AMBTC-compressed images with an asymmetric embedding rule. Using the AMBTC-compressed version as the basis, the proposed embedding scheme always modifies a pixel value toward its original value with a step size (bitrate) proportional to the gap width. Therefore, the visual quality of the stego image is better than the referred AMBTC version. Additionally, as a result of the adaptive bitrate strategy, the data embedding capacity of the proposed scheme outperforms that of state-of-the-art methods. The security of the resulting stego images was also tested by RS-steganalysis. Experimental results show that the overall performance of the proposed scheme is satisfactory. We revised it, please confirm.

A Novel Generator of Continuous Probability Distributions for the Asymmetric Left-skewed Bimodal Real-life Data with Properties and Copulas

This paper presents a novel two-parameter G family of distributions. Relevant statistical properties such as the ordinary moments, incomplete moments and moment generating function are derived. Using common copulas, some new bivariate type G families are derived. Special attention is devoted to the standard exponential base line model. The density of the new exponential extension can be “asymmetric and right skewed shape” with no peak, “asymmetric right skewed shape” with one peak, “symmetric shape” and “asymmetric left skewed shape” with one peak. The hazard rate of the new exponential distribution can be “increasing”, “U-shape”, “decreasing” and “J-shape”. The usefulness and flexibility of the new family is illustrated by means of two applications to real data sets. The new family is compared with many common G families in modeling relief times and survival times data sets.

The Extended Birnbaum\u2013Saunders Distribution Based on the Scale Shape Mixture of Skew Normal Distributions

In this article, a large class of univriate Birnbaum–Saunders distributions based on the scale shape mixture of skew normal distributions is introduced which generates suitable subclasses for modeling asymmetric data in a variety of settings. The moments and maximum likelihood estimation procedures are disscused via an ECM-algorithm. The observed information matrix to approximate the asymptotic covariance matrix of the parameter estimates is then derived in some subclasses. A simulation study is also performed to evaluate the finite sample properties of ML estimators and finally, a real data set is analyzed for illustrative purposes.

PQMLE of a Partially Linear Varying Coefficient Spatial Autoregressive Panel Model with Random Effects

This article deals with asymmetrical spatial data which can be modeled by a partially linear varying coefficient spatial autoregressive panel model (PLVCSARPM) with random effects. We constructed its profile quasi-maximum likelihood estimators (PQMLE). The consistency and asymptotic normality of the estimators were proved under some regular conditions. Monte Carlo simulations implied our estimators have good finite sample performance. Finally, a set of asymmetric real data applications was analyzed for illustrating the performance of the provided method.

Tackling Small Eigen-Gaps: Fine-Grained Eigenvector Estimation and Inference Under Heteroscedastic Noise

This paper aims to address two fundamental challenges arising in eigenvector estimation and inference for a low-rank matrix from noisy observations: 1) how to estimate an unknown eigenvector when the eigen-gap (i.e. the spacing between the associated eigenvalue and the rest of the spectrum) is particularly small; 2) how to perform estimation and inference on linear functionals of an eigenvector—a sort of “fine-grained” statistical reasoning that goes far beyond the usual <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{2}$ </tex-math></inline-formula> analysis. We investigate how to address these challenges in a setting where the unknown <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n\times n$ </tex-math></inline-formula> matrix is symmetric and the additive noise matrix contains independent (and non-symmetric) entries. Based on eigen-decomposition of the asymmetric data matrix, we propose estimation and uncertainty quantification procedures for an unknown eigenvector, which further allow us to reason about linear functionals of an unknown eigenvector. The proposed procedures and the accompanying theory enjoy several important features: 1) distribution-free (i.e. prior knowledge about the noise distributions is not needed); 2) adaptive to heteroscedastic noise; 3) minimax optimal under Gaussian noise. Along the way, we establish valid procedures to construct confidence intervals for the unknown eigenvalues. All this is guaranteed even in the presence of a small eigen-gap (up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(\sqrt {n/\mathrm {poly}\log (n)}\,)$ </tex-math></inline-formula> times smaller than the requirement in prior theory), which goes significantly beyond what generic matrix perturbation theory has to offer.

Möbius Transformation-Induced Distributions Provide Better Modelling for Protein Architecture

Proteins are found in all living organisms and constitute a large group of macromolecules with many functions. Proteins achieve their operations by adopting distinct three-dimensional structures encoded within the sequence of the constituent amino acids in one or more polypeptides. New, more flexible distributions are proposed for the MCMC sampling method for predicting protein 3D structures by applying a Möbius transformation to the bivariate von Mises distribution. In addition to this, sine-skewed versions of the proposed models are introduced to meet the increasing demand for modelling asymmetric toroidal data. Interestingly, the marginals of the new models lead to new multimodal circular distributions. We analysed three big datasets consisting of bivariate information about protein domains to illustrate the efficiency and behaviour of the proposed models. These newly proposed models outperformed mixtures of well-known models for modelling toroidal data. A simulation study was carried out to find the best method for generating samples from the proposed models. Our results shed new light on proposal distributions in the MCMC sampling method for predicting the protein structure environment.

Towards Potential Content-Based Features Evaluation to Tackle Meaningful Citations

The scientific community has presented various citation classification models to refute the concept of pure quantitative citation analysis systems wherein all citations are treated equally. However, a small number of benchmark datasets exist, which makes the asymmetric citation data-driven modeling quite complex. These models classify citations for varying reasons, mostly harnessing metadata and content-based features derived from research papers. Presently, researchers are more inclined toward binary citation classification with the belief that exploiting the datasets of incomplete nature in the best possible way is adequate to address the issue. We argue that contemporary ML citation classification models overlook essential aspects while selecting the appropriate features that hinder elutriating the asymmetric citation data. This study presents a novel binary citation classification model exploiting a list of potential natural language processing (NLP) based features. Machine learning classifiers, including SVM, KLR, and RF, are harnessed to classify citations into important and non-important classes. The evaluation is performed using two benchmark data sets containing a corpus of around 953 paper-citation pairs annotated by the citing authors and domain experts. The study outcomes exhibit that the proposed model outperformed the contemporary approaches by attaining a precision of 0.88.

Exponentiated Generalized Inverted Gompertz Distribution: Properties and Estimation Methods with Applications to Symmetric and Asymmetric Data

In this article, a new four-parameter lifetime model called the exponentiated generalized inverted Gompertz distribution is studied and proposed. The newly proposed distribution is able to model the lifetimes with upside-down bathtub-shaped hazard rates and is suitable for describing the negative and positive skewness. A detailed description of some various properties of this model, including the reliability function, hazard rate function, quantile function, and median, mode, moments, moment generating function, entropies, kurtosis, and skewness, mean waiting lifetime, and others are presented. The parameters of the studied model are appreciated using four various estimation methods, the maximum likelihood, least squares, weighted least squares, and Cramér-von Mises methods. A simulation study is carried out to examine the performance of the new model estimators based on the four estimation methods using the mean squared errors (MSEs) and the bias estimates. The flexibility of the proposed model is clarified by studying four different engineering applications to symmetric and asymmetric data, and it is found that this model is more flexible and works quite well for modeling these data.

Forecasting Grain Production and Static Capacity of Warehouses Using the Natural Neighbor and Multiquadric Equations

The strategic logistics of agricultural production and storage aggregates information related to production and storage. In this sense, time, location, and distance from producer and consumer markets are considered, emphasizing the importance of grain storage and production logistics. The Natural Neighbor and multiquadric equation are spatial interpolation methods used to predict these variables value at non-sampled locations, for asymmetric and categorical data, respectively. This study investigated the spatial prediction of grain production (tons) (soybean, first crop corn, second-crop corn, and wheat) in the 2016/2017 growing season and qualitative data on the static capacity of warehouses in the 2017/2018 growing season. The result obtained through the spatial interpolation using the natural neighbor method was coherent, as it showed the high variability of grain production relative to the different meso-regions. Therefore, the method was appropriate because it allowed predicting the behavior of grain production in the 2016/2017 growing season in the state of Paraná-Brazil, making it possible to identify regions of higher or lower production. The result of the spatial interpolation using the multiquadric equations allowed identifying a higher predominance of storage units with a low static capacity of warehouses, but also enabled the detection of regions with a static capacity of warehouses that varied from the medium to the high category in the state of Paraná, Brazil.

Feature Selection and Ensemble-Based Intrusion Detection System: An Efficient and Comprehensive Approach

The emergence of ground-breaking technologies such as artificial intelligence, cloud computing, big data powered by the Internet, and its highly valued real-world applications consisting of symmetric and asymmetric data distributions, has significantly changed our lives in many positive aspects. However, it equally comes with the current catastrophic daily escalating cyberattacks. Thus, raising the need for researchers to harness the innovative strengths of machine learning to design and implement intrusion detection systems (IDSs) to help mitigate these unfortunate cyber threats. Nevertheless, trustworthy and effective IDSs is a challenge due to low accuracy engendered by vast, irrelevant, and redundant features; inept detection of all types of novel attacks by individual machine learning classifiers; costly and faulty use of labeled training datasets cum significant false alarm rates (FAR) and the excessive model building and testing time. Therefore, this paper proposed a promising hybrid feature selection (HFS) with an ensemble classifier, which efficiently selects relevant features and provides consistent attack classification. Initially, we harness the various strengths of CfsSubsetEval, genetic search, and a rule-based engine to effectively select subsets of features with high correlation, which considerably reduced the model complexity and enhanced the generalization of learning algorithms, both of which are symmetry learning attributes. Moreover, using a voting method and average of probabilities, we present an ensemble classifier that used K-means, One-Class SVM, DBSCAN, and Expectation-Maximization, abbreviated (KODE) as an enhanced classifier that consistently classifies the asymmetric probability distributions between malicious and normal instances. HFS-KODE achieves remarkable results using 10-fold cross-validation, CIC-IDS2017, NSL-KDD, and UNSW-NB15 datasets and various metrics. For example, it outclassed all the selected individual classification methods, cutting-edge feature selection, and some current IDSs techniques with an excellent performance accuracy of 99.99%, 99.73%, and 99.997%, and a detection rate of 99.75%, 96.64%, and 99.93% for CIC-IDS2017, NSL-KDD, and UNSW-NB15, respectively based on only 11, 8, 13 selected relevant features from the above datasets. Finally, considering the drastically reduced FAR and time, coupled with no need for labeled datasets, it is self-evident that HFS-KODE proves to have a remarkable performance compared to many current approaches.

Modeling Extreme Values Utilizing an Asymmetric Probability Function

In this article, a new flexible probability density function with three parameters is proposed for modeling asymmetric data (positive and negative) with different types of kurtosis (mesokurtic, leptokurtic and platykurtic). Some of its statistical and reliability properties, including hazard rate function, moments, moment generating function, incomplete moments, mean deviations, moment of the residual life, moment of the reversed residual life, and order statistics are derived. Its hazard rate function can be either constant, increasing-constant, decreasing-constant, U shape, upside down shape or upside down-U shape. Seven classical estimation methods are considered to estimate the unknown model parameters. Monte Carlo simulation experiments are performed to compare the performance of the seven different estimation methods. Finally, a distinctive asymmetric real data application is analyzed for illustrating the flexibility of the new model.

Deep Learning-Based Residual Control Chart for Binary Response

A residual (r) control chart of asymmetrical and non-normal binary response variable with highly correlated explanatory variables is proposed in this research. To avoid multicollinearity between multiple explanatory variables, we employ and compare a neural network regression model and deep learning regression model using Bayesian variable selection (BVS), principal component analysis (PCA), nonlinear PCA (NLPCA) or whole multiple explanatory variables. The advantage of our r control chart is able to process both non-normal and correlated multivariate explanatory variables by employing a neural network model and deep learning model. We prove that the deep learning r control chart is relatively efficient to monitor the simulated and real binary response asymmetric data compared with r control chart of the generalized linear model (GLM) with probit and logit link functions and neural network r control chart.

A Compound Class of the Inverse Gamma and Power Series Distributions

In this paper, the inverse gamma power series (IGPS) class of distributions asymmetric is introduced. This family is obtained by compounding inverse gamma and power series distributions. We present the density, survival and hazard functions, moments and the order statistics of the IGPS. Estimation is first discussed by means of the quantile method. Then, an EM algorithm is implemented to compute the maximum likelihood estimates of the parameters. Moreover, a simulation study is carried out to examine the effectiveness of these estimates. Finally, the performance of the new class is analyzed by means of two asymmetric real data sets.