Lottery Numbers and Ordered Statistics

The lottery has long captivated the imagination of players worldwide, offering the tantalizing possibility of life-changing wins. While winning the lottery is largely a matter of chance, as lottery drawings are typically random and unpredictable. Some people use the lottery terminal randomly generates numbers for them, some players choose numbers that hold personal significance to them, such as birthdays, anniversaries, or other important dates, some enthusiasts have turned to statistical analysis as a means to analyze past winning numbers identify patterns or frequencies. In this paper, we use order statistics to estimate the probability of specific order of numbers or number combinations being drawn in future drawings.

留学生Medical Statistics线上课程建设与远程教学的实践与思考

受政治、经济和全球健康等因素影响,远程教育成为高等教育的一个发展趋势。新冠疫情防控期间,受出入境限制,未能返华的留学生一直以远程教学推进学业,成为其间持续进行远程教学最久的群体。本研究总结临床专业本科留学生主干课程Medical Statistics的校级一流线上课程建设与教学实践,并比较了疫情前后,线下教学与远程教学的留学生期末考试成绩,发现远程教学成绩经历波动后逐渐稳定并接近传统线下教学。本文同时对效果影响因素进行了调研和分析,发现“充分学习和使用远程课程中丰富的资源”和“上网是否容易”是留学生学习效果的主要影响因素。提出建设丰富教学资源对促进医学统计学远程学习效果的重要影响,同时提出对策建议,以期进一步提高远程教学水平,服务高校现代化课程体系建设。

Removal of Stripes in Remote Sensing Images Based on Statistics Combined with Image Enhancement

A method to remove stripes from remote sensing images is proposed based on statistics and a new image enhancement method.The overall processing steps for improving the quality of remote sensing images are introduced to provide a general baseline.Due to the differences in satellite sensors when producing images,subtle but inherent stripes can appear at the stitching positions between the sensors.These stitchingstripes cannot be eliminated by conventional relative radiometric calibration.The inherent stitching stripes cause difficulties in downstream tasks such as the segmentation,classification and interpretation of remote sensing images.Therefore,a method to remove the stripes based on statistics and a new image enhancement approach are proposed in this paper.First,the inconsistency in grayscales around stripes is eliminated with the statistical method.Second,the pixels within stripes are weighted and averaged based on updated pixel values to enhance the uniformity of the overall image radiation quality.Finally,the details of the images are highlighted by a new image enhancement method,which makes the whole image clearer.Comprehensive experiments are performed,and the results indicate that the proposed method outperforms the baseline approach in terms of visual quality and radiation correction accuracy.

The asymptotic relation between the first crossing point and the last exit time of Gaussian order statistics sequences

In this paper,we study the asymptotic relation between the first crossing point and the last exit time for Gaussian order statistics which are generated by stationary weakly and strongly dependent Gaussian sequences.It is shown that the first crossing point and the last exit time are asymptotically independent and dependent for weakly and strongly dependent cases,respectively.The asymptotic relations between the first crossing point and the last exit time for stationary weakly and strongly dependent Gaussian sequences are also obtained.

Seismic safety assessment with non-Gaussian random processes for train-bridge coupled systems

Extensive high-speed railway(HSR)network resembled the intricate vascular system of the human body,crisscrossing mainlands.Seismic events,known for their unpredictability,pose a significant threat to both trains and bridges,given the HSR’s extended operational duration.Therefore,ensuring the running safety of train-bridge coupled(TBC)system,primarily composed of simply supported beam bridges,is paramount.Traditional methods like the Monte Carlo method fall short in analyzing this intricate system efficiently.Instead,efficient algorithm like the new point estimate method combined with moment expansion approximation(NPEM-MEA)is applied to study random responses of numerical simulation TBC systems.Validation of the NPEM-MEA’s feasibility is conducted using the Monte Carlo method.Comparative analysis confirms the accuracy and efficiency of the method,with a recommended truncation order of four to six for the NPEM-MEA.Additionally,the influences of seismic magnitude and epicentral distance are discussed based on the random dynamic responses in the TBC system.This methodology not only facilitates seismic safety assessments for TBC systems but also contributes to standard-setting for these systems under earthquake conditions.

Smart Healthcare Activity Recognition Using Statistical Regression and Intelligent Learning

In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.

Nonparametric Statistical Feature Scaling Based Quadratic Regressive Convolution Deep Neural Network for Software Fault Prediction

The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods.

Statistical Approach to Basketball Players’Skill Level

In basketball, each player’s skill level is the key to a team’s success or failure, the skill level is affected by many personal and environmental factors. A physics-informed AI statistics has become extremely important. In this article, a complex non-linear process is considered by taking into account the average points per game of each player, playing time, shooting percentage, and others. This physics-informed statistics is to construct a multiple linear regression model with physics-informed neural networks. Based on the official data provided by the American Basketball League, and combined with specific methods of R program analysis, the regression model affecting the player’s average points per game is verified, and the key factors affecting the player’s average points per game are finally elucidated. The paper provides a novel window for coaches to make meaningful in-game adjustments to team members.

Statistical Inversion Based on Nonlinear Weighted Anisotropic Total Variational Model and Its Application in Electrical Impedance Tomography

Electrical impedance tomography (EIT) aims to reconstruct the conductivity distribution using the boundary measured voltage potential. Traditional regularization based method would suffer from error propagation due to the iteration process. The statistical inverse problem method uses statistical inference to estimate unknown parameters. In this article, we develop a nonlinear weighted anisotropic total variation (NWATV) prior density function based on the recently proposed NWATV regularization method. We calculate the corresponding posterior density function, i.e., the solution of the EIT inverse problem in the statistical sense, via a modified Markov chain Monte Carlo (MCMC) sampling. We do numerical experiment to validate the proposed approach.

Improving Statistical Literacy through Evidence-Based Strategies Among First-Year Education Students in a State University

Statistical literacy is crucial for cultivating well-rounded thinkers.The integration of evidence-based strategies in teaching and learning is pivotal for enhancing students’statistical literacy.This research specifically focuses on the utilization of Share and Model Concepts and Nurturing Metacognition as evidence-based strategies aimed at improving the statistical literacy of learners.The study employed a quasi-experimental design,specifically the nonequivalent control group,wherein students answered pre-test and post-test instruments and researcher-made questionnaires.The study included 50 first-year Bachelor in Secondary Education majors in Mathematics and Science for the academic year 2023-2024.The results of the study revealed a significant difference in the scores of student respondents,indicating that the use of evidence-based strategies helped students enhance their statistical literacy.This signifies a noteworthy increase in their performance,ranging from very low to very high proficiency in understanding statistical concepts,insights into the application of statistical concepts,numeracy,graph skills,interpretation capabilities,and visualization and communication skills.Furthermore,the study showed a significant difference in the post-test scores’performance of the two groups in understanding statistical concepts and visualization and communication skills.However,no significant difference was found in the post-test scores of the two groups concerning insights into the application of statistical concepts,numeracy and graph skills,and interpretation capabilities.Additionally,students acknowledged that the implementation of evidence-based strategies significantly contributed to the improvement of their statistical literacy.

基于statistics的316L不锈钢柠檬酸钝化工艺正交实验数据分析

从statistics的数据处理能力出发,运用统计学知识对316L不锈钢柠檬酸钝化工艺参数正交实验数据进行分析,找出钝化速率的主要影响因素;柠檬酸含量5wt%、双氧水含量5wt%、钝化时间60 min为最佳的实验方案;双氧水含量为钝化速率的主要影响因素。

Using Monte Carlo Simulation Technology to Improve Intuitive Effect of Teaching Probability and Mathematical Statistics Course

With the illustration of a specific problem, this paper demonstrates that using Monte Carlo Simulation technology will improve intuitive effect of teaching Probability and Mathematical Statistics course, and save instructors' effort as well.And it is estimated that Monte Carlo Simulation technology will be one of the major teaching methods for Probability and Mathematical Statistics course in the future.

Target detection for low angle radar based on multi-frequency order-statistics

For radar targets flying at low altitude, multiple pathways produce fade or enhancement relative to the level that would be expected in a free-space environment. In this paper, a new detection method based on a wide-ranging multi-frequency radar for low angle targets is proposed. Sequential transmitting multiple pulses with different frequencies are first applied to decorrelate the coherence of the direct and reflected echoes. After receiving all echoes,the multi-frequency samples are arranged in a sort descending according to the amplitude. Some high amplitude echoes in the same range cell are accumulated to improve the signal-to-noise ratio and the optimal number of high amplitude echoes is analyzed and given by experiments. Finally, simulation results are presented to verify the effectiveness of the method.

The use of hydrogeochemical analyses and multivariate statistics for the characterization of thermal springs in the Constantine area, Northeastern Algeria

This paper deals with the results of a hydrogeochemistry study on the thermal waters of the Constantine area, Northeastern Algeria, using geochemical and statistical tools. The samples were collected in December2016 from twelve hot springs and were analyzed for physicochemical parameters(electric conductivity, p H,total dissolved solids, temperature, Ca, Mg, Na, K, HCO_3,Cl, SO_4, and SiO_2). The waters of the thermal springs have temperatures varying from 28 to 51 °C and electric conductivity values ranging from 853 to 5630 l S/cm. Q-mode Cluster analysis resulted in the determination of two major water types: a Ca–HCO_3–SO_4 type with a moderate salinity and a Na–K–Cl type with high salinity. The plot of the major ions versus the saturation indices suggested that the hydrogeochemistry of thermal groundwater is mainly controlled by dissolution/precipitation of carbonate minerals, dissolution of evaporite minerals(halite and gypsum), and ion exchange of Ca(and/or Mg) by Na. The Gibbs diagram shows that evaporation is another factor playing a minor role. Principal Component Analysis produced three significant factors which have 88.2% of totalvariance that illustrate the main processes controlling the chemistry of groundwaters, which are respectively: the dissolution of evaporite minerals(halite and gypsum), ion exchange, and dissolution/precipitation of carbonate minerals. The subsurface reservoir temperatures were calculated using different cation and silica geothermometers and gave temperatures ranging between 17 and 279 °C. The Na–K and Na–K-Ca geothermometers provided high temperatures(up to 279 °C), whereas, estimated geotemperatures from K/Mg geothermometers were the lowest(17–53 °C). Silica geothermometers gave the most reasonable temperature estimate of the subsurface waters overlap between 20 and 58 °C, which indicate possible mixing with cooler Mg groundwaters indicated by the Na–K–Mg plot in the immature water field and in silica and chloride mixing models. The results of stable isotope analyses(δ^(18) O and δ~2 H) suggest that the origin of thermal water recharge is precipitation, which recharged from a higher altitude(600–1200 m) and infiltrated through deep faults and fractures in carbonate formations. They circulate at an estimated depth that does not exceed 2 km and are heated by a high conductive heat flow before rising to the surface through faults that acted as hydrothermal conduits.During their ascent to the surface, they are subjected to various physical and chemical changes such as cooling by conduction and change in their chemical constituents due to the mixing with cold groundwaters.

DOA Estimation Based on Sparse Representation of the Fractional Lower Order Statistics in Impulsive Noise

This paper is mainly to deal with the problem of direction of arrival(DOA) estimations of multiple narrow-band sources impinging on a uniform linear array under impulsive noise environments. By modeling the impulsive noise as α-stable distribution, new methods which combine the sparse signal representation technique and fractional lower order statistics theory are proposed. In the new algorithms, the fractional lower order statistics vectors of the array output signal are sparsely represented on an overcomplete basis and the DOAs can be effectively estimated by searching the sparsest coefficients. To enhance the robustness performance of the proposed algorithms,the improved algorithms are advanced by eliminating the fractional lower order statistics of the noise from the fractional lower order statistics vector of the array output through a linear transformation. Simulation results have shown the effectiveness of the proposed methods for a wide range of highly impulsive environments.

Nearly best linear estimates of logistic parameters based on complete ordered statistics

Deals with the determination of the nearly best linear estimates of location and scale parameters of a logistic population, when both parameters are unknown, by introducing Blom’s semi empirical ’α,β correction’ into the asymptotic mean and covariance formulae with complete and ordered samples taken into consideration and various nearly best linear estimates established and points out the high efficiency of these estimators relative to the best linear unbiased estimators (BLUEs) and other linear estimators makes them useful in practice.

EVALUATION OF CONVECTIVE-STRATIFORM RAINFALL SEPARATION SCHEMES BY PRECIPITATION AND CLOUD STATISTICS

In this study,two convective-stratiform rainfall partitioning schemes are evaluated using precipitation and cloud statistics for different rainfall types categorized by applying surface rainfall equation on grid-scale data from a two-dimensional cloud-resolving model simulation.One scheme is based on surface rainfall intensity whereas the other is based on cloud content information.The model is largely forced by the large-scale vertical velocity derived from the Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment(TOGA COARE).The results reveal that over 40% of convective rainfall is associated with water vapor divergence,which primarily comes from the rainfall type with local atmospheric drying and water hydrometeor loss/convergence,caused by precipitation and evaporation of rain.More than 40% of stratiform rainfall is related to water vapor convergence,which largely comes from the rainfall type with local atmospheric moistening and hydrometeor loss/convergence attributable to water clouds through precipitation and the evaporation of rain and ice clouds through the conversion from ice hydrometeor to water hydrometeor.This implies that the separation methods based on surface rainfall and cloud content may not clearly separate convective and stratiform rainfall.

Current Statistics System Fails to Reflect New Changes in International Trade Activities

Today developed countries mainly participate in international trade via their foreign affiliates and intermediate goods are more frequently traded as a result of increasingly deepening international divisions of labour.Against this background,the current trade statistical system that is characterized by the statistical principle of cross-border trade and production,and the custom’s registration data collection method,greatly exaggerate the trade imbalance between China and America. Consequently,it is necessary for the Chinese nation to overcome the shortcomings of the current statistical system so as to evaluate more fairly the trade imbalance between China and America by referring to the Ownership-Based Framework of the complimentary statistical system under the US Current Account.This article, on the basis of the statistical principle of oumership,attempts to regulate the distorted trade imbalance resulting from the current trade statistical system,and aims for an increased understanding of the trade imbalance between China and America from a theoretical background,as well as from the points of view of practitioners and decision makers.

Prediction Based on Generalized Order Statistics from a Mixture of Rayleigh Distributions Using MCMC Algorithm

This article considers the problem in obtaining the maximum likelihood prediction (point and interval) and Bayesian prediction (point and interval) for a future observation from mixture of two Rayleigh (MTR) distributions based on generalized order statistics (GOS). We consider one-sample and two-sample prediction schemes using the Markov chain Monte Carlo (MCMC) algorithm. The conjugate prior is used to carry out the Bayesian analysis. The results are specialized to upper record values. Numerical example is presented in the methods proposed in this paper.

Lithofacies modeling by multipoint statistics and economic evaluation by NPV volume for the early Cretaceous Wabiskaw Member in Athabasca oilsands area, Canada

The static modeling and dynamic simulation are essential and critical processes in petroleum exploration and development. In this study, lithofacies models for Wabiskaw Member in Athabasca, Canada are generated by multipoint statistics(MPS) and then compared with the models built by sequential indicator simulation(SIS). Three training images(Tls) are selected from modern depositional environments;the Orinoco River Delta estuary, Cobequid bay-Salmon River estuary, and Danube River delta environment. In order to validate lithofacies models, average and variance of similarity in lithofacies are calculated through random and zonal blind-well tests.In random six-blind-well test, similarity average of MPS models is higher than that of SIS model. The Salmon MPS model closely resembles facies pattern of Wabiskaw Member in subsurface. Zonal blind-well tests show that successful lithofacies modeling for transitional depositional setting requires additional or proper zonation information on horizontal variation, vertical proportion, and secondary data.As Wabiskaw Member is frontier oilsands lease, it is impossible to evaluate the economics from production data or dynamic simulation. In this study, a dynamic steam assisted gravity drainage(SAGD)performance indicator(SPIDER) on the basis of reservoir characteristics is calculated to build 3 D reservoir model for the evaluation of the SAGD feasibility in Wabiskaw Member. SPIDER depends on reservoir properties, economic limit of steam-oil ratio, and bitumen price. Reservoir properties like porosity,permeability, and water saturation are measured from 13 cores and calculated from 201 well-logs. Three dimensional volumes of reservoir properties are constructed mostly based on relationships among properties. Finally, net present value(NPV) volume can be built by equation relating NPV and SPIDER. The economic area exceeding criterion of US$ 10,000 is identified, and the ranges of reservoir properties are estimated. NPV-volume-generation workflow from reservoir parameter to static model provides costand time-effective method to evaluate the oilsands SAGD project.