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In the present article, we derive a new Modified-X (NM-X) family of probability distributions by using a popular approach known as the T-X method. For the NM-X family, some of the main distributional (or statistical) properties such as quantile function, identifiability property, order statistics, residual life and reverse residual life, moments, and moments generating function are determined. A well-known method of estimation such as the Maximum Likelihood estimation method is used for estimating the model parameters of the introduced family of distributions. A Monte Carlo simulation method is incorporated to judge the performance of the estimators with different selected values of parameters. To check the tails behaviors of the proposed model, the risk measurements such as value-at-risk (VaR), tailed-value-at-risk (TVaR), tailed-variance (TV), and tailed-variance-premium (TVP) are investigated. For illustrative purposes, a special sub-model of the proposed class is derived and named a New Modified Weibull distribution (abbreviated as the NM-Weib). Furthermore, four applications from the field of medicine are analyzed to demonstrate the application, and effectiveness of the NM-Weib distribution. By adopting different diagnostic criteria, the NM-Weib distribution is compared with Weibull (Weib) distribution, Alpha Power Transformed-Weib (APTra-Weib) distribution, new Exponent-Weib (NExpo-Weib) distribution, Flexible Reduced Logarithmic-Weib (FRLog-Weib) distribution, Marshal Olkin-Weib (MO-Weib) distribution, and Kumaraswamy-Weib (K-Weib) distribution. Finally, based on the diagnostic criteria, it is observed that the NM-Weib distribution provides a suitable and best fit for each considered biomedical data set. [ABSTRACT FROM AUTHOR]

Kvam and Samaniego (J Am Stat Assoc 89: 526–537, 1994) derived an estimator that they billed as the nonparametric maximum likelihood estimator (MLE) of the distribution function based on a ranked-set sample. However, we show here that the likelihood used by Kvam and Samaniego (1994) is different from the probability of seeing the observed sample under perfect rankings. By appealing to results on order statistics from a discrete distribution, we write down a likelihood that matches the probability of seeing the observed sample. We maximize this likelihood by using the EM algorithm, and we show that the resulting MLE avoids certain unintuitive behavior exhibited by the Kvam and Samaniego (1994) estimator. We find that the new MLE outperforms both the Kvam and Samaniego (1994) estimator and the unbiased estimator due to Stokes and Sager (J Am Stat Assoc 83: 374– 381, 1988) in terms of integrated mean squared error under perfect rankings. [ABSTRACT FROM AUTHOR]

Due to advancements in Bayesian modeling, the likelihood-free posterior estimate is now feasible. These estimation methods are crucial for a deeper understanding of simulation-based systems since it might be difficult, if not impossible, to estimate probability values. This compares the effectiveness of such updated estimate approaches to earlier maximum likelihood forecasting models and offers some adjustments to maximum likelihood estimation for estimating the parameters of the Bayesian analysis in this work. Recent improvements in Bayesian modeling have made it feasible to obtain the likelihood-free posterior estimate. These estimate methods are essential for evaluating simulation-based theories since it might be difficult, if not impossible, to determine probability values. Simulation-based concepts such as the Leaky Competing Accumulator (LCA) theory and Feed-Forward Inhibition (FFI) theory have not yet benefited from Bayesian techniques. As assessment criteria, total relative deviation (TRD), total mean square error (TMSE), and Stein Loss Function (SLF) are used. Maximum likelihood (ML) estimates are modified because the original statistic's Cumulative Distribution Function (CDF) is better than traditional ML estimations and other modified estimators emphasize average and coefficient variability. Prior estimator performance was unaffected by response rate or real parameter settings. These systems have not been formally evaluated by the Bayesian factor. [ABSTRACT FROM AUTHOR]

This paper introduces a new three-parameter distribution called the modified Kies Gompertz (MKGO) distribution to identify an effective statistical distribution in a real-life modeling data set. The distribution's probability density function may take numerous forms and graphs, and it could be negatively and positively skewed and uni-modal. We established several statistical properties of the MKGO distribution, such as moments, skewness, kurtosis, moment generating function, distribution of order statistic, quantile function, stress strength reliability and two cases of entropy measures. Further, we proposed six methods for estimating the unknown parameters of the suggested model. A brief Monter Carlo (MC) simulation study is illustrated in terms of average estimate (AE) and mean square error (MSE). to show the efficiency of these estimators. Finally, we analyzed four real data sets in the engineering sector to evaluate the potential of the proposed model with that of various existing distributions. [ABSTRACT FROM AUTHOR]

Copyright of Tecnología y Ciencias del Agua is the property of Instituto Mexicano de Tecnologia del Agua (IMTA) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Nueva Revista del Pacífico is the property of Universidad de Playa Ancha de Ciencias de la Educacion and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• Prediction of the emotional states in children with ASD using ECG signals. • Customized elicitation protocol using audio and video clips for data acquisition. • Analysis of geometrical and time domain features of positive and negative emotions. • KNN and Ensemble classifier were used for the classifying the two emotional states. • Geometrical features resulted in good accuracy of 84.8% and 74.7% in both states. Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that is characterized by various social impairments. Children with ASD have major difficulties in expressing themselves, resulting in stress and meltdowns. Understanding their hidden feelings and needs may help in tackling and avoiding such strenuous behaviors. This research aims to aid the parents and caretakers of children with ASD to understand the hidden and unexpressed emotional state by using physiological signals obtained from wearable devices. Here, electrocardiogram (ECG) signals pertaining to two valence states ('like' and 'dislike') were recorded from twenty children (10 Control and 10 children with ASD). The heart rate variability (HRV) signals were then obtained from the ECG signals using the Pan-Tompkins's algorithm. The statistical, higher order statistics (HOS) and geometrical features which were statistically significant were trained using the K Nearest Neighbor (KNN) and Ensemble Classifier algorithms. The findings of our analysis indicate that the integration of major statistical features resulted in an overall average accuracy of 84.8% and 75.3% using HRV data for the control and test population, respectively. Similarly, geometrical features resulted in a maximum average accuracy of 84.8% and 74.2% for control and test population respectively. The decreased HRV in the test population indicates the presence of autonomic dysregulation in children with ASD when compared to their control peers. [ABSTRACT FROM AUTHOR]

Moments of generalized order statistics appear in several areas of science and engineering. These moments are useful in studying properties of the random variables which are arranged in increasing order of importance, for example, time to failure of a computer system. The computation of these moments is sometimes very tedious and hence some algorithms are required. One algorithm is to use a recursive method of computation of these moments and is very useful as it provides the basis to compute higher moments of generalized order statistics from the corresponding lower-order moments. Generalized order statistics provides several models of ordered data as a special case. The moments of generalized order statistics also provide moments of order statistics and record values as a special case. In this research, the recurrence relations for single, product, inverse and ratio moments of generalized order statistics will be obtained for Lindley-Weibull distribution. These relations will be helpful for obtained moments of generalized order statistics from Lindley-Weibull distribution recursively. Special cases of the recurrence relations will also be obtained. Some characterizations of the distribution will also be obtained by using moments of generalized order statistics. These relations for moments and characterizations can be used in different areas of computer sciences where data is arranged in increasing order. [ABSTRACT FROM AUTHOR]

Efficient medium access protocols are crucial in leveraging the advancement in the physical layer techniques that have been proposed for the next-generation wireless networks. However, collisions among data packets in random access protocols reduce channel efficiency and hence the throughput. In this paper, we propose a MAC protocol based on both the signal transmission diversity and Successive interference cancellation (SIC) to improve the throughput. In the proposed Medium Access Control (MAC) protocol, the receiver receives two sets of signals from the transmitters over two uncorrelated time slots. Inter slot SIC is first carried out at the receiver over two independent sets of received interfered signals. Signals are decoded by canceling those signals that have already been decoded in one set from the other set. Closed-form expressions for the packet decoding probability, system throughput, average packet transmission delay, and energy efficiency have been derived by considering order statistics of received signal power. Analytical results are validated against simulation. Maximum achievable throughput of the proposed scheme is shown to be four times that of slotted Aloha and twice that of a slotted Aloha with SIC. [ABSTRACT FROM AUTHOR]

Land use and land cover classification from a remote sensing image is a long standing research problem. It ranges from simple classifications like mapping water bodies to complex classifications like crop and forest strands. Crop image classification is complex because of various stages of growth of the same crop, same spectral values for various crops, an other multitude of problems. Crop image classification is very essential for agriculture monitoring, crop yield production, global food security, etc. A new unsupervised algorithm, Spherical Contact Distribution Classification Algorithm (SCDCA) is proposed in this paper which uses mathematical morphology, spherical contact distributions, and first order statistics. Later SCDCA is compared with linear contact distribution classification algorithm (LCDCA). Quantitative analyses prove the efficiency of the algorithm and present that the complexity of SCDCA is very much less when compared to that of LCDCA. [ABSTRACT FROM AUTHOR]

In this paper, we provide some general results for the usual stochastic ordering of the extreme order statistics arising from two sets of random variables with different marginal distributions and different underlying Archimedean copulas structure. Application to some general models as marginal distributions which are including the well-known semiparametric family of distributions in statistics, is discussed. [ABSTRACT FROM AUTHOR]

In this paper, the Rama distribution (RD) is considered, and a new model called extended Rama distribution (ERD) is suggested. The new model involves the sum of two independent Rama distributed random variables. The probability density function (pdf) and cumulative distribution function (cdf) are obtained and analyzed. It is found that the new model is skewed to the right. Several mathematical and statistical properties are derived and proved. The properties studied include moments, coefficient of variation, coefficient of skewness, coefficient of kurtosis and moment generating function. Some simulations are undertaken to illustrate the behavior of these properties. In addition, the reliability analysis of the distribution is investigated through the hazard rate function, reversed hazard rate function and odds function. The parameter of the distribution is estimated based on the maximum likelihood method. The distributions of order statistics for ERD are also presented. The performance of the suggested model is compared with several other lifetime distributions based on some goodness of fit tests on a real dataset. It turns out that the suggested model is more flexible than its competitors considered in this study, for modeling real lifetime data. [ABSTRACT FROM AUTHOR]

This paper provides new evidence on the early career earnings of Canadians who obtain a trade certificate. The analysis uses comprehensive national level administrative data on individuals who received a trade certificate between 2008 and 2016 linked to their tax information at Statistics Canada in order to link earnings to their individual characteristics and details related to their certification. In this paper we track their yearly employment earnings from their first full year following certification onward, to a maximum of 9 years. Overall, journeypersons in nationally accredited Red Seal trades earn more than those in non-Red Seal trades, those in Mechanical and Electrical trades earn the most, and journeywomen earn only 47% of what journeymen do overall, largely driven by their being concentrated in a relatively narrow set of low-paying trades. [ABSTRACT FROM AUTHOR]

A robust myoelectric control system (MCS) is essential for the design of electromyography (EMG) based human–machine interface (HMI) designs such as prosthetics, exoskeleton, wheelchair and humanoid robots. The functionality of the current pattern recognition (PR) technique in MCS is limited by factors such as variation in the user's limb position. To overcome the effect of this dynamic variation, an invariant higher order statistics – frequency domain feature set (HOS-FD) is proposed in this paper. EMG data from eight hand movements in five limb positions are considered. When trained with three limb position data, the HOS-FD with three hidden layers deep neural network (DNN) achieved a significantly high classification accuracy of 97.84%±0.22 compared to other classifiers viz., single layer artificial neural network (ANN), linear discriminant analysis (LDA), support vector machine (SVM), k nearest neighbor (kNN), decision tree (DT) and Naïve Bayes (NB) classifiers with accuracies of 94.11%±1.63, 95.02%±1.89, 94.63%±2.33, 90.05 ± 4.11, 86.66 ± 4.72 and 78.78%±5.02 respectively. Further, when trained with data from all five limb positions, the proposed feature set with DNN had an accuracy of 99.16%±0.14. The statistical significance of the high classification accuracy obtained using the proposed feature set is also proven using multiple analysis of variance tests (p < 0.001). These results indicate that the proposed method is a promising technique for HMI. [ABSTRACT FROM AUTHOR]

Copyright of Vigo International Journal of Applied Linguistics is the property of Universidade de Vigo and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A signal can be represented by sparse representation with fewer coefficients. Due to this ability, sparse representation is used in research fields such as signal compression, noise elimination, and classification. In this study, sparse coefficients of the signals were obtained by using dictionary learning and sparse representation algorithms. The obtained coefficients were used in the weight update process of three different classifiers, which were created by using AdaBoost, SVM, and LDA algorithms. So, Dictionary learning based AdaBoost classifiers were obtained. The proposed Dictionary Learning (DL) based AdaBoost classifiers classified the ECG (Electrocardiography) signals. Before classification, the feature selection process was applied to ECG signals and six different feature subsets were obtained by Discrete Wavelet Transform (DWT), First Order Statistics (FOS), T-test, Bhattacharyya, Entropy, and Wilcoxon test methods. The feature subsets were used as the new dataset. The classification process was done by the proposed method and satisfying results were obtained. The best classification accuracy was obtained as 99.75% by the proposed dictionary learning based method called as DL-AdaBoost-SVM on feature subsets obtained by DWT and Wilcoxon test methods. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a new extension of the Rayleigh distribution with two parameter called type I half logistic Rayleigh distribution. Several important mathematical and statistical properties of this new distribution are discussed. Simulation studies and real data applications are also considered for performance of the new distribution. [ABSTRACT FROM AUTHOR]

• EMD-DWT two stage analysis of voice samples. • Temporal energy to select robust IMF. • Extraction of HOSs features from robust IMF. • Classification accuracy reached 100% for the classification of "Funktionelle Dysphonia vs. Rekurrensparse". The voice is a prominent tool allowing people to communicate and to change information in their daily activities. However, any slight alteration in the voice production system may affect the voice quality. Over the last years, researchers in biomedical engineering field worked to develop a robust automatic system that may help clinicians to perform a preventive diagnosis in order to detect the voice pathologies in an early stage. In this context, pathological voice detection and classification method based on EMD-DWT analysis and Higher Order Statistics (HOS) features, is proposed. Also DWT coefficients features are extracted and tested. To carry out our experiments a wide subset of voice signal from normal subjects and subjects which suffer from the five most frequent pathologies in the Saarbrücken Voice Database (SVD), is selected. In The first step, we applied the Empirical Mode Decomposition (EMD) to the voice signal. Afterwards, among the obtained candidates of Intrinsic Mode Functions (IMFs), we choose the robust one based on temporal energy criterion. In the second step, the selected IMF was decomposed via the Discrete Wavelet Transform (DWT). As a result, two features vector includes six HOSs parameters, and a features vector includes six DWT features were formed from both approximation and detail coefficients. In order to classify the obtained data a support vector machine (SVM) is employed. After having trained the proposed system using the SVD database, the system was evaluated using voice signals of volunteer's subjects from the Neurological department of RABTA Hospital of Tunis. The proposed method gives promising results in pathological voices detection. The accuracies reached 99.26% using HOS features and 93.1% using DWT features for SVD database. In the classification, an accuracy of 100% was reached for "Funktionelle Dysphonia vs. Rekrrensparese" based on HOS features. Nevertheless, using DWT features the accuracy achieved was 90.32% for "Hyperfunktionelle Dysphonia vs. Rekurrensparse". Furthermore, in the validation the accuracies reached were 94.82%, 91.37% for HOS and DWT features, respectively. In the classification the highest accuracies reached were for classifying "Parkinson versus Paralysis" 94.44% and 88.87% based on HOS and DWT features, respectively. HOS features show promising results in the automatic voice pathology detection and classification compared to DWT features. Thus, it can reliably be used as noninvasive tool to assist clinical evaluation for pathological voices identification. [ABSTRACT FROM AUTHOR]

Arrhythmia is one of the major cause of deaths across the globe. Almost 17.9 million deaths are caused due to cardiovascular diseases. In order to reduce this much mortality rate, the cardiovascular disease should be properly identified and the proper treatment for the same should be immediately provided to the patients. In this study, a new ensemble based support vector machine (SVM) classifier was proposed to classify heartbeat into four classes from MIT-BIH arrhythmia database. The results were compared with other classifiers that are SVM, Random Forest (RF), K-Nearest Neighbours (KNN), and Long Short Term Memory network. The four features were extracted from the ECG signals that were used by the classifiers are Wavelets, high order statistics, R-R intervals and morphological features. An ensemble of SVMs obtained the best result with an overall accuracy of 94.4%. [ABSTRACT FROM AUTHOR]

Cardiovascular Arrhythmias (irregular beat) are related to the sudden death, can be characterized into two kinds, life-threatening (dangerous) and non-life-threatening. In clinical research and diagnosis analysis electrocardiography (ECG) plays an important role. The Advancement of Medical Instrumentation (AAMI) suggests, all the heartbeat of MIT-BIH dataset into four classes, namely, normal or bundle branch block (N), supraventricular ectopic (S), ventricular ectopic (V) and fusion of ventricular and normal (F). The objective of this paper is the classification of arrhythmias on the ECG as per AAMI standards. First beat detection and then on a given window size beat segmentation is performed. Then, features computed from ECG signals are RR (separation between two successive heartbeat pulses), local binary pattern(LBP), morphological changes, wavelet, high order statistics, and several amplitude values; Pre-RR, Post-RR, Local RR and Global RR. The main reason is to computing many morphological features to comparison analysis with the model when trained and tested on single features vs combining all features together. Model gives highest performance which is 90.8% accuracy using LBP as single feature compared with other features. Combined all important features together and optimal features (total 104) are given as an input to Single SVM classifier as well as ensembles of SVM which maps the component feature vectors to the particular class name. Principal Component Analysis (PCA) technique is also applied to the model which reduces the total number of features from 104 to 30 features. The Single SVM system can accurately classify four beat types and has an overall classification rate of 94.05% without PCA and using PCA 92.84% while the Ensemble SVM classifies and with an accuracy of 92.96% [ABSTRACT FROM AUTHOR]

The conveying stability of the powder propellant is significant for the feeding-system design and particle-combustion performance of powder engines. In this study, a nozzle structure was employed to increase the conveying stability in a pipeline. The gas-solid flow through the nozzle was visualized, and the pressure signals were analyzed using multiscale methods: the standard deviation, wavelet transform, and higher order statistics. The nozzle structure helped to reorganize the downstream gas-solid by accelerating the gas-solid two-phase flow. The results for the standard deviation indicated that the upstream was more stable and less affected by the downstream at higher fluidized pressures. Through wavelet analysis, the energy fraction of the frequency band was used to represent the gas-solid characteristics, and the particle collision and nonlinear drag of the gas-solid interaction represented by the low-frequency band were determined to be the main factors affecting the downstream stability. Additionally, a high fluidized pressure (>2 MPa) yielded a relatively stable downstream flow. The higher order statistics method provided a better result than the standard deviation because of its high resolution and strong noise suppression. The analysis results indicate that increasing the particle size enhances the downstream stability. [ABSTRACT FROM AUTHOR]

Hyperspectral Image reconstruction from RGB images is a low-cost and convenient alternative to acquiring hyperspectral images directly. The challenge in estimating the spectral response function and using it for generating the hyperspectral image data is addressed effectively by the use of convolutional neural networks for the task. The accuracy of the reconstruction techniques is improving and effective architectures are being adapted for the purpose. Specifically, in a recent work the authors propose the use of Function Mixture Network, to model the mapping from RGB to hyperspectral. This technique benefits from the use of an adaptive spatial receptive field, however, given that the goal is spectral superresolution additional use of adaptive spectral field is prudent. Accordingly, in this paper we propose an improved Function Mixture Network based reconstruction technique by additionally incorporating a variable spectral receptive field. The proposed updates have resulted in significant gains in performance as is demonstrated by the experiments conducted over multiple datasets. An additional update to the architecture is the use of non-convolutional branches in the function mixture network. The experiments of three standard datasets, clearly demonstrate the superiority of the proposed technique for hyperspectral image reconstruction based on RGB data. • While Pixel-wise receptive field is an effective approach, one cannot ignore correlation that exists across spectral bands. Spectral Receptive field is dynamically adapted (via the use of mixing function) to correctly represent the amount of correlation between pixels along the spectral domain. In this study we explore the effect of adaptive spectral receptive field on the quality of hyperspectral image reconstruction, within a dataset and across different datasets. • Spectral composition is not necessarily a linear operation. To effectively model such non-linearity, we propose the use of Rank order statistics (ROS) filter branches along with convolution branches in the FMB blocks. Such strategy results in clear performance gains as is evidenced by the experimental results obtained. [ABSTRACT FROM AUTHOR]

Ramp-based twin support vector clustering (RampTWSVC) is a powerful clustering method, which measures the within-cluster and between-cluster scatter by the bounded ramp functions. However, the ramp loss is a symmetric function and it does not consider the data distribution, which makes the RampTWSVC not robust enough to deal with noisy datasets. In this paper, we construct an asymmetric distribution loss function and propose a novel plane-based clustering with asymmetric distribution loss (ADPC). The asymmetric distribution loss function combined the ramp loss with first-order and second-order statistics, it inherits the advantages of ramp loss function and captures the data distribution precisely, which makes ADPC more robust to the samples far from the cluster center plane. Moreover, two adjusting parameters t 1 and t 2 are introduced to flexibly control the form of the asymmetric distribution loss function and improve the performance of the algorithm. In addition, its nonlinear clustering formation is also provided by using the kernel trick. Some experiments on noise-corrupted benchmark UCI and artificial datasets have been provided, and the detailed comparison results show that the proposed ADPC has better performance on most datasets. Further experiments and analysis on the face clustering have been done to demonstrate the effectiveness and feasibility of the proposed ADPC. • A novel asymmetric distribution loss is constructed. • A novel plane-based clustering with asymmetric distribution loss is proposed, termed as ADPC. • Sufficient experiments are performed on several benchmark UCI datasets and artificial datasets. • We also demonstrate the effectiveness of ADPC in real-world applications. [ABSTRACT FROM AUTHOR]

Objective To develop reference data for young men/women on the variability of parameters used for the evaluation of masticatory function with an artificial test food. Methods Subjects included were 200 18-25-year olds with complete dentition and "normal" occlusion. An artificial test food was chewed in two tests (20 cycles and swallowing threshold), during which sequences/cycles were counted and timed. Medium-particle-size (MPS) and broadness of particle distribution were calculated evaluating the chewed material. Reference data was based on order statistics. Sex-specific 95% reference limits with 90% confidence intervals were calculated with RefVal-v2.1-software. Coefficients of variation were also obtained. Results Tables with reference data for young men/women chewing an artificial test food were constructed with the data collected displaying ample variability: MPS after 20 cycles anywhere between 0.7-3.5 mm or 14-84 cycles to deem the test food ready to be swallowed (C.V. 43% males/34% females). Conclusion There is much variability in masticatory parameters for young adults with good oral health. [ABSTRACT FROM AUTHOR]

In this study, the exponentiated generalized exponential Dagum distribution has been proposed and studied. This family of distribution consists of a number of sub-models such as the exponentiated generalized Dagum distribution, Dagum distribution, Fisk distribution, Burr III distribution and exponentiated generalized exponential Burr III distribution among others. Statistical properties of the new family were also derived. Maximum likelihood estimators of the parameters of the distribution were developed and simulation studies performed to assess the properties of the estimators. Applications of the model was demonstrated to show its usefulness. [ABSTRACT FROM AUTHOR]

Parry Romberg syndrome (PRS) is a condition characterized by progressive hemifacial atrophy, predominantly affecting the soft tissues. Associated bone retraction is a common clinical feature of PRS but has never been assessed. Here we used 3D imaging and Bayesian statistics in order to demonstrate and quantify bone atrophy in PRS. Ten non-operated patients with PRS (4/10 males) and 12 age-matched controls (7/12 males) were included into the study. The average age at CT-scan was 9.67 ± 4.13 years for PRS patients and 12.5 ± 4.37 years for controls. Soft and hard tissue atrophy levels were quantified using computed tomography scans, based on the distances between surfaces of the affected side and the non-affected contralateral side, both for the skin and the bone. We used a hierarchical Bayesian model with clinical priors in order to assess the relationship between hard and soft tissue atrophies. PRS patients had significant hard tissue atrophy, and atrophy extents were similar for soft and hard tissues. There was a trend for a correlation between the extent of hard tissue retraction and the extent of soft tissue retraction, and we could not demonstrate that the relationship between hard and soft tissue retractions was different in PRS and controls. Our results indicated that bone atrophy was most probably a primary process rather than a phenomenon secondary to soft tissue retraction. We have provided the first assessment of bone atrophy in PRS patients using Bayesian statistics. [ABSTRACT FROM AUTHOR]

Spectrum sensing plays an important role in spectrum sharing. Energy detection is generally used because it does not require a priori knowledge of primary user, (PU) signals; however, it is sensitive to noise uncertainty. An order statistics (OS) detector provides inherent protection against nonhomogeneous background signals. However, no analysis has been conducted yet to apply OS detection to spectrum sensing in a wireless channel to solve noise uncertainty. In this paper, we propose a robust spectrum sensing scheme based on generalized order statistics (GOS) and analyze the exact false alarm and detection probabilities under noise uncertainty. From the equation of the exact false alarm probability, the threshold value is calculated to maintain a constant false alarm rate. The detection probability is obtained from the calculated threshold under noise uncertainty. As a fusion rule for cooperative spectrum sensing, we adopt an OR rule, that is, a 1‐out‐of‐N rule, and we call the proposed scheme GOS‐OR. The analytical results show that the GOS‐OR scheme can achieve optimum performance and maintain the desired false alarm rates if the coefficients of the GOS‐OR detector can be correctly selected. [ABSTRACT FROM AUTHOR]

As the youngest branch of information hiding, network covert timing channels conceal the existence of secret messages by manipulating the timing information of the overt traffic. The popular model-based framework for constructing covert timing channels always utilizes cumulative distribution function (CDF) of the inter-packet delays (IPDs) to modulate secret messages, whereas discards high-order statistics of the IPDs completely. The consequence is the vulnerability to high-order statistical tests, e.g., entropy test. In this study, a rich security model of covert timing channels is established based on IPD chains, which can be used to measure the distortion of multi-order timing statistics of a covert timing channel. To achieve rich security, we propose two types of covert timing channels based on nested lattices. The CDF of the IPDs is used to construct dot-lattice and interval-lattice for quantization, which can ensure the cell density of the lattice consistent with the joint distribution of the IPDs. Furthermore, compensative quantization and guard band strategy are employed to eliminate the regularity and enhance the robustness, respectively. Experimental results on real traffic show that the proposed schemes are rich-secure, and robust to channel interference, whereas some state-of-the-art covert timing channels cannot evade detection under the rich security model. [ABSTRACT FROM AUTHOR]

• The statistical nature of h-index of a network node is revealed by order statistics. • New measures can be derived by generalizing the h-index based on order statistics. • Experiments on 53 networks validate the effectiveness and flexibility of our method. Evaluating the importance of a network node is a crucial task in network science and graph data mining. H -index is a popular centrality measure for this task, however, there is still a lack of its interpretation from a rigorous statistical aspect. Here we rewrite h -index in a new way based on order statistics, which allows us to obtain a new family of centrality indices by generalizing the h -index along this direction. The theoretical and empirical evidence shows that such a statistical interpretation enables us to obtain a general and versatile framework for quantifying the importance of a network node. Under this framework, many new centrality indices can be derived and some of which can be more accurate and robust than h -index. We believe that this research opens up new avenues for developing more effective indices for node importance quantification from a viewpoint that still remains unexplored. [ABSTRACT FROM AUTHOR]

This paper investigates the order statistics based on the moment-generating function for the Generalized Power Function Distribution for the two different forms of this distribution. In this paper, we continue our investigation on the distribution of order statistics assuming that the original sample of size n is taken from a population that follows the Generalized Power Function Distribution. Two different forms of the Generalized Power Function Distribution with its special cases are presented and some order statistics related to these different forms are discussed. The main technique is the consideration of the moment-generating function. [ABSTRACT FROM AUTHOR]

Adding parameters to a known distribution is a useful way of constructing flexible families of distributions. Marshall and Olkin (1997) introduced a general method of adding a shape parameter to a family of distributions. In this paper, based on the Marshall–Olkin extension of a specified distribution, we introduce two new models, referred to as modified proportional hazard rates (MPHR) and modified proportional reversed hazard rates (MPRHR) models, which include as special cases the well-known proportional hazard rates and proportional reversed hazard rates models, respectively. Next, when two sets of random variables follow either the MPHR or the MPRHR model, we establish some stochastic comparisons between the corresponding order statistics based on majorization theory. The results established here extend some well-known results in the literature. [ABSTRACT FROM AUTHOR]

The Bayesian sampling plans for exponential distributions are studied based on type-II hybrid censored samples. The optimal Bayesian sampling plan is derived under a general loss function which includes the sampling cost, time-consuming cost, salvage value, and decision loss. It is employed to determine the Bayes risk and the corresponding optimal sampling plan. An explicit expression of the Bayes risk is derived. Furthermore, for the conjugate prior distribution, the closed-form formula of the Bayes decision rule can be obtained under either the linear or quadratic decision loss. [ABSTRACT FROM AUTHOR]

This paper presents a random utility maximization model for individuals selecting discrete quantities from a set of n alternatives. Multiple alternatives with positive quantities may be selected. Diminishing marginal utility to quantity of each alternative is modeled via order statistics of independent Gumbel random variables. The model is parsimonious and tractable, admitting closed-form expressions for choice probabilities. As such, the model is amenable to maximum likelihood estimation of structural parameters from observed choices. Probability functions recover binary logit probabilities under binary choice and a maximum quantity of one unit, and probability is monotonic in the quantity of each alternative. The monotonic property likely restricts the application of the model to a narrow range of settings. The property is a manifestation of a recursive relationship among Gumbel order statistic probabilities. This relationship and related properties may lead to new models for capturing important complexities in a tractable manner. • The paper presents a random utility maximization model for individuals selecting discrete quantities among multiple alternatives. • Decreasing marginal utility to consumption is modeled as Gumbel order statistics. • Properties of Gumbel order statistics and choice probabilities are derived and presented. [ABSTRACT FROM AUTHOR]

We consider an ad-hoc network of wireless sensors that harvest energy from the environment and broadcasts measurements independently, at random, provided sufficient energy is available. Clients arriving at the network are interested in retrieving measurements from an arbitrary set of sensors of some fixed size s . We show that the sensors broadcast measurements according to a phase-type distribution. We determine the probability distribution of the time needed for a client to retrieve s sensor measurements. We provide a closed-form expression for the retrieval time of s sensor measurements for an asymptotically large capacity of the sensor battery or the rate at which energy is harvested. We also analyze numerically the retrieval time of s sensor measurements under various assumptions regarding the battery capacity of the sensors, the energy harvesting and consumption processes. The results provide a lower bound for the energy storage capacity of the sensors for which the retrieval time of measurements is below a targeted level. It is also shown that the ratio between the energy harvesting rate and the broadcasting rate significantly influences the retrieval time of measurements, whereas deploying sensors with large batteries does not significantly reduce the retrieval time of measurements. Numerical experiments also indicate that our theoretical results generalize to non-identical energy harvesting rates, various amount of energy consumed upon a broadcast and non-exponential distributions of the energy harvesting and broadcasting processes. [ABSTRACT FROM AUTHOR]

As a result of densification, the performance of the wireless networks has become highly interference-limited and energy inefficient. To overcome this problem, interference mitigation techniques such as Successive Interference Cancellation (SIC) can be used to decode multiple packets simultaneously at the receiver. In this context, we analyze a SIC-based Slotted Aloha (SIC-SA) Medium Access Control (MAC) protocol for wireless networks. We derive expressions for packets decoding probability and optimal transmission probability of the nodes of the SIC-SA MAC protocol. Our derivation is based on the order statistics of Independent and Identical/non-Identical exponentially distributed received-signal-powers from the nodes under the Rayleigh channel condition. Throughput, delay, and energy efficiency of the SIC-SA MAC protocol have been derived and validated against simulation. The effect of path loss exponent, SINR threshold, and the number of nodes on the performance of SIC-SA have been studied. The performance of SIC-SA in a network of nodes distributed randomly according to the Poisson Point Process has been analyzed. Extension of our analysis to Power Domain Non-Orthogonal Multiple Access (NOMA) has been demonstrated. We also analyzed the impact of imperfect estimation of channel state information and imperfect SIC at the receiver. Results show an improvement in performance metrics of SIC-SA over the traditional Slotted Aloha. [ABSTRACT FROM AUTHOR]

A hierarchical approach for fast bi-dimensional empirical mode decomposition (B-EMD) is proposed. The presented approach utilizes an efficient window size determination scheme that enables the multi-level computation of the order statistics filter (OSF). Our detailed experiments show that the proposed OSF computation approach allows a significantly faster computation of an EMD without degrading the decomposition accuracy. [ABSTRACT FROM AUTHOR]

Prediction of the duration of a repair and maintenance project of a gas transport system is an important part of planning activities. There exist numerous sources of uncertainties that may result in time overruns possibly leading to multiple negative consequences. Our experience in planning this work suggests that accepting the stochastic nature of the project duration is a constructive step towards the preparedness to contingencies and defining penalties for repair companies. To support this approach, one needs to construct probability distributions of the durations of the projects. To address the issue of the scarcity of observed data, we suggest using a bootstrap resampling procedure. Gram-Charlier functions and order statistics are employed to approximate the distributions. It is demonstrated how to derive them for a separate repair project and a larger project consisting of a number of concurrently running subprojects. Following this, guidance is provided on how to decide about what duration should define the deadline for completion of the whole work. A simple example is provided. [ABSTRACT FROM AUTHOR]

We propose a new image representation for texture categorization and facial analysis, relying on the use of higher-order local differential statistics as features. It has been recently shown that small local pixel pattern distributions can be highly discriminative while being extremely efficient to compute, which is in contrast to the models based on the global structure of images. Motivated by such works, we propose to use higher-order statistics of local non-binarized pixel patterns for the image description. The proposed model does not require either (i) user specified quantization of the space (of pixel patterns) or (ii) any heuristics for discarding low occupancy volumes of the space. We propose to use a data driven soft quantization of the space, with parametric mixture models, combined with higher-order statistics, based on Fisher scores. We demonstrate that this leads to a more expressive representation which, when combined with discriminatively learned classifiers and metrics, achieves state-of-the-art performance on challenging texture and facial analysis datasets, in low complexity setup. Further, it is complementary to higher complexity features and when combined with them improves performance. [ABSTRACT FROM AUTHOR]

A new generalization of the Lindley distribution is recently proposed by Ghitany et al. [1] , called as the power Lindley distribution. Another generalization of the Lindley distribution was introduced by Nadarajah et al. [2] , named as the generalized Lindley distribution. This paper proposes a more generalization of the Lindley distribution which generalizes the two. We refer to this new generalization as the exponentiated power Lindley distribution. The new distribution is important since it contains as special sub-models some widely well-known distributions in addition to the above two models, such as the Lindley distribution among many others. It also provides more flexibility to analyze complex real data sets. We study some statistical properties for the new distribution. We discuss maximum likelihood estimation of the distribution parameters. Least square estimation is used to evaluate the parameters. Three algorithms are proposed for generating random data from the proposed distribution. An application of the model to a real data set is analyzed using the new distribution, which shows that the exponentiated power Lindley distribution can be used quite effectively in analyzing real lifetime data. [ABSTRACT FROM AUTHOR]

In this paper we derive explicit algebraic expressions and some recurrence relations for both single and product moments of dual generalized order statistics from a family of J -shaped distribution. These relations generalize the results given by Zghoul (2010, 2011). Further, a characterization of this distribution through conditional expectation of dual generalized order statistics is given and some computational works are also carried out. [ABSTRACT FROM AUTHOR]

In this note we give some recurrence relations satisfied by single and product moments of k -th upper record values from the exponential-Weibull lifetime distribution. Using a recurrence relation for single moments we obtain a characterization of exponential-Weibull, Weibull, exponential, Rayleigh and two parameter linear failure rate distributions. [ABSTRACT FROM AUTHOR]

Rapid diagnosis of the Covid-19 disease is the best way to prevent infection. In this paper, it is proposed to use machine learning methods to aid diagnoses quickly Covid-19 and focused on effect of several features on classification accuracy. In the proposed method 746 axial computed tomography (CT) images of the lung; 349 Covid-19 (positives) and 397 non-Covid-19 (negative) are used. Gray-level texture, shape and first order statistical features were extracted from the images. The feature vector for model training is constructed with one feature group or combination of more than one group. We then classified with Support Vector Machine, Random Forest, k-nearest neighbor and XGBoost classifier models. The hyperparameter of the models were controlled by the tuning test. Experimental results obtained with 10-fold cross-validation. The results of cross-validation verified with the additionally independent test. The best overall accuracy was 98.65% with first order statistics features classified with XGBoost. In the gray level features, the best individual results given by GLSZM as 81.25%, and the best combination result is with GLDM, GLRLM and GLSZM features as 85.52%. An important finding of this paper is that, for Covid-19 classification, the shape and first order statistics features are more valuable than gray level features. The proposed results compared with the literature studies under some Covid-19 dataset for accuracy, precision, sensitivity and F1-score metrics. Also, the literature studies which used the different Covid-19 dataset were compared with the proposed study. Our results have the significant superiority when compared with the literature studies. [ABSTRACT FROM AUTHOR]

Weibull distribution is a very flexible family of distributions which has been applied in a vast number of disciplines. In this work, we investigate stochastic properties of the smallest order statistics from two independent heterogeneous Weibull random variables with different scale and shape parameters. Furthermore, we study the hazard rate order of the smallest order statistics from lower-truncated Weibull distributions due to, in general, Weibull random variables are not ordered according to this ordering in the shape parameter. [ABSTRACT FROM AUTHOR]

Probability plotting positions are popular and used as the basis for distribution fitting and for inspecting the quality of the fit because of its simplicity. The plotting positions that lead to excellent approximation to the mean of the order statistics should be used if the objective of the fitting is to estimate quantiles. Since the mean depends on the sample size and is not amenable for simple to use closed form solution, many plotting positions have been presented in the literature, including a new plotting position that is derived based on the weighted least-squares method. In this study, the accuracy of using the new plotting position to fit the Gumbel distribution for estimating quantiles is assessed. Also, plotting positions derived by fitting the mean of the order statistics for all ranks is proposed, and an approximation to the covariance of the order statistics for the Gumbel (and Weibull) variate is given. Relative bias and root-mean-square-error of the estimated quantiles by using the proposed plotting position are shown. The use of the proposed plotting position to estimate the quantiles of annual maximum wind speed is illustrated. [ABSTRACT FROM AUTHOR]

Abstract: In this study a general form of recurrence relations of continuous function for doubly truncated modified Makeham distribution is obtained. Recurrence relations between single and product moments of order statistics from doubly truncated modified Makeham distribution are given. Also, a characterization of modified Makeham distribution from the right and the left is discussed through the properties of order statistics. [Copyright &y& Elsevier]

A pre-processing system for adaptive noise removal is proposed based on the principle of identifying and filtering object regions and background regions. Human perception of images depends on bright, well-focused object regions; these regions can be treated with the best filters, while simpler filters can be applied to other regions to reduce overall computational complexity. In the proposed method, bright region segmentation is performed, followed by segmentation of object and background regions. Noise in dark, background, and object regions is then removed by the median, fast bilateral, and bilateral filters, respectively. Simulations show that the proposed algorithm is much faster than and performs nearly as well as the bilateral filter (which is considered a powerful noise removal algorithm); it reduces computation time by 19.4 % while reducing PSNR by only 1.57 % relative to bilateral filtering. Thus, the proposed algorithm remarkably reduces computation while maintaining accuracy. [ABSTRACT FROM AUTHOR]