Parameters Of Statistical Model Research Articles

A Reinforcement Learning Based Routing in Cognitive Radio Networks for Primary Users with Multi-stage Periodicity

Designing an efficient routing protocol for cognitive radio networks is critical due to the dynamic behavior of the primary users. Based on empirical studies, the primary users activity on the licensed channels has periodicity comprised of several stages, and that the model of primary users activity may change during different stages. This paper has identified two main challenges facing designers: how to transmit packets via a stable route, and how to ensure imposing of minimal interference on the primary users. To address these, we propose a routing protocol which is based on a generalized version of Q-learning and which exploits the said model of primary users behavior. We divide the infinite time horizon into cycles (corresponding to periods), then break each cycle into several sub-cycles (corresponding to stages), making an assumption that the statistical model parameters of the primary users activity will not change during a sub-cycle. The extensive simulations confirm that our proposed routing approach outperforms existing schemes in terms of throughput and minimal interference. The paper also verifies that imposing of significant interference on the primary users and degradation of QoS of secondary users stem from lack of attention to the multi-stage periodic behavior of primary users.

The Modification of LMS Quipperschool to Improve Senior High School Students’ Concept Mastery of Biology Subject, Topic: Human Reproduction Health

This research aims to: (1) develop a biology Learning Management System (LMS) in Human Reproduction System Health with Quipper School platform, (2) develop an instrument of biology to improve student’s concept mastery at SMA Muhammadiyah I Klaten. This developmental research referred to the design and development research (DDR) model suggested by Richey & Klein (2007) grouped into six developmental procedures, and simplified into five procedures including (a) identifying the problem, (b) describing the objectives, (c) designing and developing product, (d) testing and evaluating product, (e) communicating the testing results. The data were analyzed using the parametric statistical model of ANCOVA test with the significance level of 0.05. The research finding reveals that the implementation of the LMS is in the “very good” category. There is a significant difference in the concept mastery (p=0.013) between the experimental and control class after the use of Human Health Reproduction LMS with p < 0.05. The conclusion of this research is that the use of LMS of Human Health Reproduction is able to improve the concept mastery of students

A statistical model for helices with applications.

Motivated by a cutting edge problem related to the shape of -helices in proteins, we formulate a parametric statistical model, which incorporates the cylindrical nature of the helix. Our focus is to detect a "kink," which is a drastic change in the axial direction of the helix. We propose a statistical model for the straight -helix and derive the maximum likelihood estimation procedure. The cylinder is an accepted geometric model for -helices, but our statistical formulation, for the first time, quantifies the uncertainty in atom positions around the cylinder. We propose a change point technique "Kink-Detector" to detect a kink location along the helix. Unlike classical change point problems, the change in direction of a helix depends on a simultaneous shift of multiple data points rather than a single data point, and is less straightforward. Our biological building block is crowdsourced data on straight and kinked helices; which has set a gold standard. We use this data to identify salient features to construct Kink-detector, test its performance and gain some insights. We find the performance of Kink-detector comparable to its computational competitor called "Kink-Finder." We highlight that identification of kinks by visual assessment can have limitations and Kink-detector may help in such cases. Further, an analysis of crowdsourced curved -helices finds that Kink-detector is also effective in detecting moderate changes in axial directions.

A Novel Method for Assessment of the Velocity of Detonation for Primary Explosives

AbstractPrimary explosives are hazardous energetic materials with considerable sensitivities to external stimuli such as impact, friction, flame, heat, electric spark, and shock. A novel correlation has been introduced for prediction of detonation velocities of different hazardous primary explosives. The needed data for the new model are the loading density, the heat of detonation, the sum of the moles of gaseous products per gram of explosive and the average molecular weight of gaseous products. Decomposition products were specified based on the oxygen balance of primary explosives. Cross‐validation of the model and the statistical parameters were used for evaluating the goodness‐of‐fit, goodness‐of‐prediction, accuracy, and precision of the new model. The statistical parameters of the new model, as compared to the outputs of one of the best available thermochemical computer codes, confirm the higher reliability of the new correlation.

The BOSS Emission-line Lens Survey. V. Morphology and Substructure of Lensed Lyα Emitters at Redshift Z ≈ 2.5 in the BELLS GALLERY

Abstract We present a morphological study of the 17 lensed Lyα emitter (LAE) galaxies of the Baryon Oscillation Spectroscopic Survey Emission-Line Lens Survey (BELLS) for the GALaxy-Lyα EmitteR sYstems (BELLS GALLERY) sample. This analysis combines the magnification effect of strong galaxy–galaxy lensing with the high resolution of the Hubble Space Telescope to achieve a physical resolution of ∼80 pc for this 2 < z < 3 LAE sample, allowing a detailed characterization of the LAE rest-frame ultraviolet continuum surface brightness profiles and substructure. We use lens-model reconstructions of the LAEs to identify and model individual clumps, which we subsequently use to constrain the parameters of a generative statistical model of the LAE population. Since the BELLS GALLERY sample is selected primarily on the basis of Lyα emission, the LAEs that we study here are likely to be directly comparable to those selected in wide-field, narrowband LAE surveys, in contrast with the lensed LAEs identified in cluster-lensing fields. We find an LAE clumpiness fraction of approximately 88%, which is significantly higher than that found in previous (non-lensing) studies. We find a well-resolved characteristic clump half-light radii of ∼350 pc, a scale comparable to the largest H ii regions seen in the local universe. This statistical characterization of LAE surface-brightness profiles will be incorporated into future lensing analyses using the BELLS GALLERY sample to constrain the incidence of dark-matter substructure in the foreground lensing galaxies.

Building damage assessment from PolSAR data using texture parameters of statistical model

Accurate building damage assessment is essential in providing decision support for disaster relief and reconstruction. Polarimetric synthetic aperture radar (PolSAR) has become one of the most effective means of building damage assessment, due to its all-day/all-weather ability and richer backscatter information of targets. However, intact buildings that are not parallel to the SAR flight pass (termed oriented buildings) and collapsed buildings share similar scattering mechanisms, both of which are dominated by volume scattering. This characteristic always leads to misjudgments between assessments of collapsed buildings and oriented buildings from PolSAR data. Because the collapsed buildings and the intact buildings (whether oriented or parallel buildings) have different textures, a novel building damage assessment method is proposed in this study to address this problem by introducing texture parameters of statistical models. First, the logarithms of the estimated texture parameters of different statistical models are taken as a new texture feature to describe the collapse of the buildings. Second, the collapsed buildings and intact buildings are distinguished using an appropriate threshold. Then, the building blocks are classified into three levels based on the building block collapse rate. Moreover, this paper also discusses the capability for performing damage assessment using texture parameters from different statistical models or using different estimators. The RADARSAT-2 and ALOS-1 PolSAR images are used to present and analyze the performance of the proposed method. The results show that using the texture parameters avoids the problem of confusing collapsed and oriented buildings and improves the assessment accuracy. The results assessed by using the K/G0 distribution texture parameters estimated based on the second moment obtain the highest extraction accuracies. For the RADARSAT-2 and ALOS-1 data, the overall accuracy (OA) for these three types of buildings is 73.39% and 68.45%, respectively.

Optimal transformations leading to normal distributions of positron emission tomography standardized uptake values

The statistical analysis of positron emission tomography (PET) standardized uptake value (SUV) measurements is challenging due to the skewed nature of SUV distributions. This limits utilization of powerful parametric statistical models for analyzing SUV measurements. An ad-hoc approach, which is frequently used in practice, is to blindly use a log transformation, which may or may not result in normal SUV distributions. This study sought to identify optimal transformations leading to normally distributed PET SUVs extracted from tumors and assess the effects of therapy on the optimal transformations. Methods. The optimal transformation for producing normal distributions of tumor SUVs was identified by iterating the Box–Cox transformation parameter (λ) and selecting the parameter that maximized the Shapiro–Wilk P-value. Optimal transformations were identified for tumor SUVmax distributions at both pre and post treatment. This study included 57 patients that underwent 18F-fluorodeoxyglucose (18F-FDG) PET scans (publically available dataset). In addition, to test the generality of our transformation methodology, we included analysis of 27 patients that underwent 18F-Fluorothymidine (18F-FLT) PET scans at our institution. Results. After applying the optimal Box–Cox transformations, neither the pre nor the post treatment 18F-FDG SUV distributions deviated significantly from normality (P > 0.10). Similar results were found for 18F-FLT PET SUV distributions (P > 0.10). For both 18F-FDG and 18F-FLT SUV distributions, the skewness and kurtosis increased from pre to post treatment, leading to a decrease in the optimal Box–Cox transformation parameter from pre to post treatment. There were types of distributions encountered for both 18F-FDG and 18F-FLT where a log transformation was not optimal for providing normal SUV distributions. Conclusion. Optimization of the Box–Cox transformation, offers a solution for identifying normal SUV transformations for when the log transformation is insufficient. The log transformation is not always the appropriate transformation for producing normally distributed PET SUVs.

Project Management Risks in the Sphere of Housing and Communal Services

This paper shows a project risk management system model allowing enterprises to better identify risks in the sphere of housing and communal services and to manage them throughout the life cycle of the project. It shows the most popular methods of risk probability assessment and tries to indicate the advantages of the robust approach over the traditional methods. Modern development of project management as well as the accumulated knowledge and experience in this field made it possible to integrate project management knowledge into a single system model. Within the framework of this model, standard and robust approaches are applied and expanded for the tasks of project data analysis. The suggested algorithms used to assess the parameters in statistical models allow to obtain reliable estimates. In this study, the classification of risks was determined by the degree of relevance. I conducted an analysis of statistical data, such as requests for maintenance of housing stock of different service lives. The frequency of failures in the work of housing organizations was determined and the probability of accidents was calculated. Taking into account these calculations, the housing stock was graded according to the degree of admissibility of the risk of its maintenance.

Project risk management in the construction of high-rise buildings

This paper shows the project risk management methods, which allow to better identify risks in the construction of high-rise buildings and to manage them throughout the life cycle of the project. One of the project risk management processes is a quantitative analysis of risks. The quantitative analysis usually includes the assessment of the potential impact of project risks and their probabilities. This paper shows the most popular methods of risk probability assessment and tries to indicate the advantages of the robust approach over the traditional methods. Within the framework of the project risk management model a robust approach of P. Huber is applied and expanded for the tasks of regression analysis of project data. The suggested algorithms used to assess the parameters in statistical models allow to obtain reliable estimates. A review of the theoretical problems of the development of robust models built on the methodology of the minimax estimates was done and the algorithm for the situation of asymmetric “contamination” was developed.

Remote estimation of crop canopy parameters by statistical regression algorithms for winter rapeseed using Sentinel-2 multispectral images

Еstimation of crop canopy parameters is important task for remote sensing monitoring of agriculture and constructing strategies for within-field management. The main objective of this study is to evaluate the retrieval from Sentinel-2 images by parametric and non-parametric statistical models several crop canopy parameters for monitoring before winter and after winter rapeseed crop in real farming conditions of North East Bulgaria. For the calibration of the models in-situ data from three field campaigns is used. For most of the studied parameters models with good accuracy were identified, except for aboveground fresh biomass. The best identified model for vegetation fraction (RMSEcv=0.14%) and plant density (RMSEcv=9 nb/m2) were parametric models with three band vegetation index (3BSI-Tian) and linear fitting function for the first, three band vegetation index (3BSI-Verreslt) and polynomial for the second parameter. For aboveground dry biomass (RMSEcv=52 g/m²), mean plant height (RMSEcv=4cm) and nitrogen concentration in fresh biomass (RMSEcv=2%) the best models were non-parametric, Gaussian Processes Regression for the first parameter and Variational Heteroscedastic variant of the Gaussian Processes Regression for the other two.

Baseline map of organic carbon stock in farmland topsoil in East China

Soil organic carbon (SOC) is important to soil fertility and the global carbon cycle. Accurate estimates of SOC stock and its dynamics are critical for managing agricultural ecosystems and carbon accounting under climate change, especially for highly cultivated regions. We extensively surveyed the SOC levels in 29,927 sites in Zhejiang province, an intensively cultivated region of East China, from year 2007 to 2008. We then estimated the spatial distribution of topsoil (0–30cm) organic carbon stock using a random forest (RF) model, which is a powerful machine learning algorithm with superior predictive performance over parametric statistical models. The final RF model contained 23 predictor variables, covering soil properties, vegetation, climate, topography, land cover, farming practices, and locations. The RF model showed high performance in predicting the SOC stock, with a coefficient of determination (R2) of 0.76 and a root mean square error (RMSE) of 10.63tCha−1. This performance was superior to the General Linear Model (GLM) (R2=0.35, RMSE=19.93tCha−1) and the ordinary kriging (OK) method (R2=0.57, RMSE=14.44tCha−1), and was equivalent to Boosted Regressing Trees (BRT) (R2=0.73, RMSE=11.26tCha−1). According to the variable importance evaluation, soil properties were the most important predictor variables, followed by climate and location, with relative importance values of 61%, 17%, and 14%, respectively. The predicted SOC stock ranged from 14.8 to 125.5tCha−1, with an average±standard deviation of 50.1±12.3tCha−1. The mean SOC level obtained from this survey was considerably lower than the value of 60.5tCha−1 reported for the same region in the Harmonized World Soil Database (HWSD), which is the most commonly used soil database worldwide. A large spatial discrepancy of SOC stock was observed between this survey and HWSD in regional and sub-regional levels. This study provided an updated regional baseline map of SOC levels for improving farmland management and refining carbon accounting under climate change.

Kernel density estimation with bounded data

The uncertainties of input variables are quantified as probabilistic distribution functions using parametric or nonparametric statistical modeling methods for reliability analysis or reliability-based design optimization. However, parametric statistical modeling methods such as the goodness-of-fit test and the model selection method are inaccurate when the number of data is very small or the input variables do not have parametric distributions. To deal with this problem, kernel density estimation with bounded data (KDE-bd) and KDE with estimated bounded data (KDE-ebd), which randomly generates bounded data within given input variable intervals for given data and applies them to generate density functions, are proposed in this study. Since the KDE-bd and KDE-ebd use input variable intervals, they attain better convergence to the population distribution than the original KDE does, especially for a small number of given data. The KDE-bd can even deal with a problem that has one data with input variable bounds. To verify the proposed method, statistical simulation tests were carried out for various numbers of data using multiple distribution types and then the KDE-bd and KDE-ebd were compared with the KDE. The results showed the KDE-bd and KDE-ebd to be more accurate than the original KDE, especially when the number of data is less than 10. It is also more robust than the original KDE regardless of the quality of given data, and is therefore more useful even if there is insufficient data for input variables.

Discrete Model for the Structure and Strength of Cementitious Materials

Cementitious materials are characterized by brittle behavior in direct tension and by transverse dilatation (due to microcracking) under compression. Microcracking causes increasingly larger transverse strains and a phenomenological Poisson’s ratio that gradually increases to about $$\nu =0.5$$ and beyond, at the limit point in compression. This behavior is due to the underlying structure of cementitious pastes which is simulated here with a discrete physical model. The computational model is generic, assembled from a statistically generated, continuous network of flaky dendrites consisting of cement hydrates that emanate from partially hydrated cement grains. In the actual amorphous material, the dendrites constitute the solid phase of the cement gel and interconnect to provide the strength and stiffness against load. The idealized dendrite solid is loaded in compression and tension to compute values for strength and Poisson’s effects. Parametric studies are conducted, to calibrate the statistical parameters of the discrete model with the physical and mechanical characteristics of the material, so that the familiar experimental trends may be reproduced. The model provides a framework for the study of the mechanical behavior of the material under various states of stress and strain and can be used to model the effects of additives (e.g., fibers) that may be explicitly simulated in the discrete structure.

Target Recognition in Radar Images Using Weighted Statistical Dictionary-Based Sparse Representation

In this letter, we present a novel generic approach for radar automatic target recognition in either inverse synthetic aperture radar (ISAR) or synthetic aperture radar (SAR) images. For this purpose, the radar image is described by a statistical modeling in the complex wavelet domain. Thus, the radar image is transformed into a complex wavelet domain using the dual-tree complex wavelet transform. Afterward, the magnitudes of the complex sub-bands are modeled by Weibull or Gamma distributions. The estimated parameters of these models are stacked together to create a statistical dictionary in training step. For the recognition task, we use the weighted sparse representation-based classification method that captures the linearity and locality information of image features. In this context, we propose to use the Kullback–Leibler divergence between the parametric statistical models of training and test sets in order to assign a weight for each training sample. Experiments conducted on both ISAR and SAR images’ databases demonstrate that the proposed approach leads to an improvement in the recognition rate.

A gamma process modeling approach for the comparison of dissolution profiles

In this paper, we propose a stochastic gamma process model for assessing the similarity of two dissolution profiles. Based on the proposed stochastic model, we utilize the difference factor and similarity factor to test the similarity of two dissolution profiles based on bootstrap confidence intervals. The performances of the proposed methods are compared with a multivariate test procedure via Monte Carlo simulation studies. The proposed testing methods are shown to be powerful and effectively controlling the error rate. The proposed model provides a simple yet useful alternative parametric statistical model for assessing the similarity of two dissolution profiles. All the methods are illustrated with a numerical example.

D4L: Decentralized Dynamic Discriminative Dictionary Learning

We consider discriminative dictionary learning in a distributed online setting, where a network of agents aims to learn, from sequential observations, statistical model parameters jointly with data-driven signal representations. We formulate this problem as a distributed stochastic program with a nonconvex objective that quantifies the merit of the choice of model parameters and dictionary. We consider the use of a block variant of the Arrow–Hurwicz saddle point algorithm to solve this problem, which exploits factorization properties of the Lagrangian to yield a protocol in that only requires exchange of model information among neighboring nodes. We show that decisions made with this saddle point algorithm asymptotically achieve a first-order stationarity condition on average. The learning rate depends on the signal source, network structure, and discriminative task. We illustrate the algorithm performance for solving a large-scale image classification task on a network of interconnected servers and observe that practical performance is comparable to a centralized approach. We further apply this method to the problem of a robotic team seeking to autonomously navigate in an unknown environment by predicting unexpected maneuvers, demonstrating the proposed algorithm's utility in a field setting.

Mixture Statistical Distribution Based Multiple Component Model for Target Detection in High Resolution SAR Imagery

This paper proposes an innovative Mixture Statistical Distribution Based Multiple Component (MSDMC) model for target detection in high spatial resolution Synthetic Aperture Radar (SAR) images. Traditional detection algorithms usually ignore the spatial relationship among the target’s components. In the presented method, however, both the structural information and the statistical distribution are considered to better recognize the target. Firstly, the method based on compressed sensing reconstruction is used to recover the SAR image. Then, the multiple component model composed of a root filter and some corresponding part filters is applied to describe the structural information of the target. In the following step, mixture statistical distributions are utilised to discriminate the target from the background, and the Method of Logarithmic Cumulants (MoLC) based Expectation Maximization (EM) approach is adopted to estimate the parameters of the mixture statistical distribution model, which will be finally merged into the proposed MSDMC framework together with the multiple component model. In the experiment, the aeroplanes and the electrical power towers in TerraSAR-X SAR images are detected at three spatial resolutions. The results indicate that the presented MSDMC Model has potential for improving the detection performance compared with the state-of-the-art SAR target detection methods.

Estimation of The Health Benefit Associated With A Potential Denosumab-Induced Extension of Progression Free Survival In Multiple Myeloma Patients

The efficacy of Denosumab in the prevention of skeletal-related events in multiple myeloma (MM) patients, and its non-inferiority to Zoledronic Acid, was demonstrated in a randomized, double blind phase 3 trial (IMW 2017 and ASCO 2017). Preliminary analyses of trial survival data suggested that patients treated with Denosumab may have a longer progression free survival (PFS) compared to patients treated with Zoledronic Acid (IMW 2017 and ASCO 2017). The objective of this study was to quantify the potential incremental health benefit associated with a Denosumab-induced extension of PFS, in MM patients. Parametric statistical models were fitted on individual failure time data from the primary data analysis of the trial to extrapolate long-term trends for PFS. Multiple scenarios, based on the 5 best fitting parametric models (unrestricted exponential, Weibull, log-normal, log-logistic, and generalized gamma) were generated. A partitioned survival cohort model was used to calculate the quality-adjusted life gains associated with the extended PFS on top of anti-myeloma therapy. Model results showed that the delay in disease progression observed in the trial may translate into a lifetime health benefit equivalent to living between 1.5 and 2.3 extra months in perfect health, 1.9 and 2.8 extra months with the same quality of life as in the MM pre-progression state, and between 2.3 and 3.5 extra months with the same quality of life as in the MM post-progression state. In all MM patients, Denosumab use is potentially associated with a significant incremental health benefit, compared to Zoledronic Acid.

An Image Segmentation Method Based on Renyi Relative Entropy and Gaussian Distribution

Background: Image segmentation is a necessary prerequisite for many higher level computer vision tasks, such as object recognition, image understanding, and image retrieval. However, the segmentation problem is inherently ill-posed due to the large number of possible partitionings for any single image. So, image segmentation remains one of the major challenges in image analysis. There are also many patents on these problems. This paper introduces a new image segmentation method to readers, and the effectiveness of the new method is demonstrated by experiments. Keywords: Image segmentation, histogram thresholding, Renyi relative entropy, Gaussian distribution, parametric statistical model.

A Copula-Based Trend-Renewal Process Model for Analysis of Repairable Systems With Multitype Failures

Reliability analysis of multicomponent repairable systems with dependent component failures is challenging for two reasons. First, the failure mechanism of one component may depend on other components when considering component failure dependence. Second, imperfect repair actions can have accumulated effects on the repaired components and these accumulated effects are difficult to measure. In this paper, we propose a parametric statistical model to capture the failure dependence information with general component repair actions. We apply the maximum likelihood method to estimate the model parameters by utilizing the historical failure data. Statistical hypothesis tests are developed to determine the dependence structure of the component failures based on the proposed reliability model. The proposed methodology is demonstrated by a simulation study and case studies of a car body assembly process and a forklift vehicle system.