A Non-Parametric Scheme for Identifying Data Characteristic Based on Curve Similarity Matching

For accurately identifying the distribution charac-teristic of Gaussian-like noises in unmanned aerial vehicle(UAV)state estimation,this paper proposes a non-parametric scheme based on curve similarity matching.In the framework of the pro-posed scheme,a Parzen window(kernel density estimation,KDE)method on sliding window technology is applied for roughly esti-mating the sample probability density,a precise data probability density function(PDF)model is constructed with the least square method on K-fold cross validation,and the testing result based on evaluation method is obtained based on some data characteristic analyses of curve shape,abruptness and symmetry.Some com-parison simulations with classical methods and UAV flight exper-iment shows that the proposed scheme has higher recognition accuracy than classical methods for some kinds of Gaussian-like data,which provides better reference for the design of Kalman filter(KF)in complex water environment.

Evaluation of Rainfall Tendency for the Twentieth Century over Indira Sagar Region in Central India

The study investigates long-term changes in annual and seasonal rainfall patterns in the Indira Sagar Region of Madhya Pradesh, India, from 1901 to 2010. Agriculture sustainability, food supply, natural resource development, and hydropower system reliability in the region rely heavily on monsoon rainfall. Monthly rainfall data from three stations (East Nimar, Barwani, and West Nimar) were analyzed. Initially, the pre-whitening method was applied to eliminate serial correlation effects from the rainfall data series. Subsequently, statistical trends in annual and seasonal rainfall were assessed using both parametric (student-t test) and non-parametric tests [Mann-Kendall, Sen’s slope estimator, and Cumulative Sum (CUSUM)]. The magnitude of the rainfall trend was determined using Theil-Sen’s slope estimator. Spatial analysis of the Mann-Kendall test on an annual basis revealed a statistically insignificant decreasing trend for Barwani and East Nimar and an increasing trend for West Nimar. On a seasonal basis, the monsoon season contributes a significant percentage (88.33%) to the total annual rainfall. The CUSUM test results indicated a shift change detection in annual rainfall data for Barwani in 1997, while shifts were observed in West and East Nimar stations in 1929. These findings offer valuable insights into regional rainfall behavior, aiding in the planning and management of water resources and ecological systems.

A Short-Term Traffic Flow Forecasting Method Based on a Three-Layer K-Nearest Neighbor Non-Parametric Regression Algorithm

Short-term traffic flow is one of the core technologies to realize traffic flow guidance. In this article, in view of the characteristics that the traffic flow changes repeatedly, a short-term traffic flow forecasting method based on a three-layer K-nearest neighbor non-parametric regression algorithm is proposed. Specifically, two screening layers based on shape similarity were introduced in K-nearest neighbor non-parametric regression method, and the forecasting results were output using the weighted averaging on the reciprocal values of the shape similarity distances and the most-similar-point distance adjustment method. According to the experimental results, the proposed algorithm has improved the predictive ability of the traditional K-nearest neighbor non-parametric regression method, and greatly enhanced the accuracy and real-time performance of short-term traffic flow forecasting.

An Improved Non-Parametric Method for Multiple Moving Objects Detection in the Markov Random Field

Detecting moving objects in the stationary background is an important problem in visual surveillance systems.However,the traditional background subtraction method fails when the background is not completely stationary and involves certain dynamic changes.In this paper,according to the basic steps of the background subtraction method,a novel non-parametric moving object detection method is proposed based on an improved ant colony algorithm by using the Markov random field.Concretely,the contributions are as follows:1)A new nonparametric strategy is utilized to model the background,based on an improved kernel density estimation;this approach uses an adaptive bandwidth,and the fused features combine the colours,gradients and positions.2)A Markov random field method based on this adaptive background model via the constraint of the spatial context is proposed to extract objects.3)The posterior function is maximized efficiently by using an improved ant colony system algorithm.Extensive experiments show that the proposed method demonstrates a better performance than many existing state-of-the-art methods.

Exponential Continuous Non-Parametric Neural Identifier With Predefined Convergence Velocity

This paper addresses the design of an exponential function-based learning law for artificial neural networks(ANNs)with continuous dynamics.The ANN structure is used to obtain a non-parametric model of systems with uncertainties,which are described by a set of nonlinear ordinary differential equations.Two novel adaptive algorithms with predefined exponential convergence rate adjust the weights of the ANN.The first algorithm includes an adaptive gain depending on the identification error which accelerated the convergence of the weights and promotes a faster convergence between the states of the uncertain system and the trajectories of the neural identifier.The second approach uses a time-dependent sigmoidal gain that forces the convergence of the identification error to an invariant set characterized by an ellipsoid.The generalized volume of this ellipsoid depends on the upper bounds of uncertainties,perturbations and modeling errors.The application of the invariant ellipsoid method yields to obtain an algorithm to reduce the volume of the convergence region for the identification error.Both adaptive algorithms are derived from the application of a non-standard exponential dependent function and an associated controlled Lyapunov function.Numerical examples demonstrate the improvements enforced by the algorithms introduced in this study by comparing the convergence settings concerning classical schemes with non-exponential continuous learning methods.The proposed identifiers overcome the results of the classical identifier achieving a faster convergence to an invariant set of smaller dimensions.

Analysis of Trends in Drought with the Non-Parametric Approach in Vietnam: A Case Study in Ninh Thuan Province

A quantitative study was used in the study of the tendency to change drought indicators in Vietnam through the Ninh Thuan province case study. The research data are temperature and precipitation data of 11 stations from 1986 to 2016 inside and outside Ninh Thuan province. To do the research, the author uses a non-parametric analysis method and the drought index calculation method. Specifically, with the non-parametric method, the author uses the analysis, Mann-Kendall (MK) and Theil-Sen (Sen’s slope), and to analyze drought, the author uses the Standardized Precipitation Index (SPI) and the Moisture Index (MI). Two Softwares calculated in this study are ProUCL 5.1 and MAKENSEN 1.0 by the US Environmental Protection Agency and Finnish Meteorological Institute. The calculation results show that meteorological drought will decrease in the future with areas such as Phan Rang, Song Pha, Quan The, Ba Thap tend to increase very clearly, while Tam My and Nhi Ha tend to increase very clearly short. With the agricultural drought, the average MI results increased 0.013 per year, of which Song Pha station tended to increase the highest with 0.03 per year and lower with Nhi Ha with 0.001 per year. The forecast results also show that by the end of the 21st century, the SPI tends to decrease with SPI 1 being −0.68, SPI 3 being −0.40, SPI 6 being −0.25, SPI 12 is 0.42. Along with that is the forecast that the MI index will increase 0.013 per year to 2035, the MI index is 0.93, in 2050 it is 1.13, in 2075 it will be 1.46, and by 2100 it is 1.79. Research results will be used in policymaking, environmental resources management agencies, and researchers to develop and study solutions to adapt and mitigate drought in the context of variable climate change.

Bayesian Non-Parametric Mixture Model with Application to Modeling Biological Markers

The effect of treatment on patient’s outcome can easily be determined through the impact of the treatment on biological events. Observing the treatment for patients for a certain period of time can help in determining whether there is any change in the biomarker of the patient. It is important to study how the biomarker changes due to treatment and whether for different individuals located in separate centers can be clustered together since they might have different distributions. The study is motivated by a Bayesian non-parametric mixture model, which is more flexible when compared to the Bayesian Parametric models and is capable of borrowing information across different centers allowing them to be grouped together. To this end, this research modeled Biological markers taking into consideration the Surrogate markers. The study employed the nested Dirichlet process prior, which is easily peaceable on different distributions for several centers, with centers from the same Dirichlet process component clustered automatically together. The study sampled from the posterior by use of Markov chain Monte carol algorithm. The model is illustrated using a simulation study to see how it performs on simulated data. Clearly, from the simulation study it was clear that, the model was capable of clustering data into different clusters.

Oxygen uptake response to switching stairs exercise by non-parametric modeling

Oxygen uptake plays a crucial role in the evaluation of endurance performance during exercise and is extensively utilized for metabolic assessment. This study records the oxygen uptake during the exercise phase (i.e., ascending or descending) of the stair exercise, utilizing an experimental dataset that includes ten participants and covers various exercise periods. Based on the designed experiment protocol, a non-parametric modeling method with kernel-based regularization is generally applied to estimate the oxygen uptake changes during the switching stairs exercise, which closely resembles daily life activities. The modeling results indicate the effectiveness of the non-parametric modeling approach when compared to fixed-order models in terms of accuracy, stability, and compatibility. The influence of exercise duration on estimated fitness reveals that the model of the phase-oxygen uptake system is not time-invariant related to respiratory metabolism regulation and muscle fatigue. Consequently, it allows us to study the humans’ conversion mechanism at different metabolic rates and facilitates the standardization and development of exercise prescriptions.

Comparative Analysis of Climatic Change Trend and Change-Point Analysis for Long-Term Daily Rainfall Annual Maximum Time Series Data in Four Gauging Stations in Niger Delta

The aim of this study is to establish the prevailing conditions of changing climatic trends and change point dates in four selected meteorological stations of Uyo, Benin, Port Harcourt, and Warri in the Niger Delta region of Nigeria. Using daily or 24-hourly annual maximum series (AMS) data with the Indian Meteorological Department (IMD) and the modified Chowdury Indian Meteorological Department (MCIMD) models were adopted to downscale the time series data. Mann-Kendall (MK) trend and Sen’s Slope Estimator (SSE) test showed a statistically significant trend for Uyo and Benin, while Port Harcourt and Warri showed mild trends. The Sen’s Slope magnitude and variation rate were 21.6, 10.8, 6.00 and 4.4 mm/decade, respectively. The trend change-point analysis showed the initial rainfall change-point dates as 2002, 2005, 1988, and 2000 for Uyo, Benin, Port Harcourt, and Warri, respectively. These prove positive changing climatic conditions for rainfall in the study area. Erosion and flood control facilities analysis and design in the Niger Delta will require the application of Non-stationary IDF modelling.

AN OPERATIONAL STATISTICAL SCHEME FOR TROPICAL CYCLONE INDUCED RAINFALL FORECAST

A non-parametric method is used in this study to analyze and predict short-term rainfall due to tropical cyclones(TCs) in a coastal meteorological station. All 427 TCs during 1953-2011 which made landfall along the Southeast China coast with a distance less than 700 km to a certain meteorological station- Shenzhen are analyzed and grouped according to their landfalling direction, distance and intensity. The corresponding daily rainfall records at Shenzhen Meteorological Station(SMS) during TCs landfalling period(a couple of days before and after TC landfall) are collected. The maximum daily rainfall(R-24) and maximum 3-day accumulative rainfall(R-72) records at SMS for each TC category are analyzed by a non-parametric statistical method, percentile estimation. The results are plotted by statistical boxplots, expressing in probability of precipitation. The performance of the statistical boxplots is evaluated to forecast the short-term rainfall at SMS during the TC seasons in 2012 and 2013. Results show that the boxplot scheme can be used as a valuable reference to predict the short-term rainfall at SMS due to TCs landfalling along the Southeast China coast.

基于两种转换函数的真实波浪参数的统计分析（英文）

The probability distributions of wave characteristics from three groups of sampled ocean data with different significant wave heights have been analyzed using two transformation functions estimated by non-parametric and parametric methods. The marginal wave characteristic distribution and the joint density of wave properties have been calculated using the two transformations, with the results and accuracy of both transformations presented here. The two transformations deviate slightly between each other for the calculation of the crest and trough height marginal wave distributions, as well as the joint densities of wave amplitude with other wave properties. The transformation methods for the calculation of the wave crest and trough height distributions are shown to provide good agreement with real ocean data. Our work will help in the determination of the most appropriate transformation procedure for the prediction of extreme values.

Application of Statistical Tools for Data Analysis and Interpretation in Rice Plant Pathology

There has been a significant advancement in the application of statistical tools in plant pathology during the past four decades. These tools include multivariate analysis of disease dynamics involving principal component analysis, cluster analysis, factor analysis, pattern analysis, discriminant analysis, multivariate analysis of variance, correspondence analysis, canonical correlation analysis, redundancy analysis, genetic diversity analysis, and stability analysis, which involve in joint regression, additive main effects and multiplicative interactions, and genotype-by-environment interaction biplot analysis. The advanced statistical tools, such as non-parametric analysis of disease association, meta-analysis, Bayesian analysis, and decision theory, take an important place in analysis of disease dynamics. Disease forecasting methods by simulation models for plant diseases have a great potentiality in practical disease control strategies. Common mathematical tools such as monomolecular, exponential, logistic, Gompertz and linked differential equations take an important place in growth curve analysis of disease epidemics. The highly informative means of displaying a range of numerical data through construction of box and whisker plots has been suggested. The probable applications of recent advanced tools of linear and non-linear mixed models like the linear mixed model, generalized linear model, and generalized linear mixed models have been presented. The most recent technologies such as micro-array analysis, though cost effective, provide estimates of gene expressions for thousands of genes simultaneously and need attention by the molecular biologists. Some of these advanced tools can be well applied in different branches of rice research, including crop improvement, crop production, crop protection, social sciences as well as agricultural engineering. The rice research scientists should take advantage of these new opportunities adequately in adoption of the new highly potential advanced technologies while planning experimental designs, data collection, analysis and interpretation of their research data sets.

An Integrated Statistical Model to Measure Academic Teaching Quality

The aim of this paper is to present a new proposal for the classification of Academic institutions in terms of quality of teaching. Our methodological proposal borrows concepts from operational risk, such as scorecard models, employed to assess University performances, on the basis of both the perceived and the actual quality. We propose to summarize opinion data using new non parametric indexes able to exploit efficiently the ordinal nature of the analysed variables and to integrate different sources of data. In particular we show how web survey methods can improve the quality and robustness of collected data, especially when integrated with students career data. Empirical evidence is given on the basis of real data from the University of Pavia.

Accelerated proportional degradation hazards-odds model in accelerated degradation test

An accelerated proportional degradation hazards-odds model is proposed. It is a non-parametric model and thus has path- free and distribution-free properties, avoiding the errors caused by faulty assumptions of degradation paths or distribution of degra- dation measurements. It is established based on a link function which combines the degradation cumulative hazard rate function and the degradation odds function through a transformation pa- rameter, and this makes the accelerated proportional degradation hazards model and the accelerated proportional degradation odds model special cases of it. Hypothesis tests are discussed, and the proposed model is applicable when some model assumptions are satisfied. This model is utilized to estimate the reliability of minia- ture bulbs under low stress levels based on the degradation data obtained under high stress levels to validate the effectiveness of this model.

Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review

In this paper, sixty-eight research articles published between 2000 and 2017 as well as textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF) and Neural Network (NN) as the main statistical tools were reviewed. The aim was to examine and compare these nonparametric classification methods on the following attributes: robustness to training data, sensitivity to changes, data fitting, stability, ability to handle large data sizes, sensitivity to noise, time invested in parameter tuning, and accuracy. The performances, strengths and shortcomings of each of the algorithms were examined, and finally, a conclusion was arrived at on which one has higher performance. It was evident from the literature reviewed that RF is too sensitive to small changes in the training dataset and is occasionally unstable and tends to overfit in the model. KNN is easy to implement and understand but has a major drawback of becoming significantly slow as the size of the data in use grows, while the ideal value of K for the KNN classifier is difficult to set. SVM and RF are insensitive to noise or overtraining, which shows their ability in dealing with unbalanced data. Larger input datasets will lengthen classification times for NN and KNN more than for SVM and RF. Among these nonparametric classification methods, NN has the potential to become a more widely used classification algorithm, but because of their time-consuming parameter tuning procedure, high level of complexity in computational processing, the numerous types of NN architectures to choose from and the high number of algorithms used for training, most researchers recommend SVM and RF as easier and wieldy used methods which repeatedly achieve results with high accuracies and are often faster to implement.

NONPARAMETRIC ESTIMATION OF BIVARIATE SURVIVAL FUNCTION UNDER THE COMPETING RISKS CASE

Estimation of the bivariate survival function under the competing risks caseis considered.We give an explicit formula for the estimator from a decomposition of thebivariate survival function based on competing risks,which is almost sure consistent.

Crustal attenuation characteristics of S-waves beneath the Eastern Tohoku region,Japan

An inversion method was applied to crustal earthquakes dataset to find S-wave attenuation characteristics beneath the Eastern Tohoku region of Japan. Accelerograms from 85 shallow crustal earthquakes up to 25 km depth and magnitude range between 3.5 and 5.5 were analyzed to estimate the seismic quality factor Qs. A homogeneous attenuation model Qs for the wave propagation path was evaluated from spectral amplitudes, at 24 different frequencies between 0.5 and 20 Hz by using generalized inversion technique. To do this, non-parametric attenuation functions were calculated to observe spectral amplitude decay with hypocentral distance. Then, these functions were parameterized to estimate Qs. It was found that in Eastern Tohoku region, the Qs frequency dependence can be approximated with the function 33 f 1.22 within a frequency range between 0.5 and 20 Hz. However, the frequency dependence of Qs in the frequency range between 0.5 and 6 Hz is best approximated by Qs （f） = 36 f 0.94 showing relatively weaker frequency dependence as compared to the relation Qs （f） = 6 f^ 2.09 for the frequency range between 6 and 15 Hz. These results could be used to estimate source and site parameters for seismic hazard assessment in the region.

Objective Quantification of Small Motor Sound Quality Based on Psychoacoustic Metrics

With the widespread application of electrification and intelligence of automobiles,the number of electric devices with small DC motors in automobiles has gradually increased,and the in-terior of electric vehicles is quieter.The sound quality(SQ)of small motor directly affects the pas-senger experience.Therefore,the research on the SQ of small motor is of great significance.In this paper,the objective quantification of small motor sound quality was investigated based on tradi-tional psychoacoustic metrics.The time-frequency characteristics of sound signal was analyzed to quantify the subjective perception caused by the sound of small motor.And a new psychoacoustic metrics of objective evaluation which were suitable for small motor SQ evaluation were proposed,namely specific loudness energy(SLE),specific prominence ratio index(SPRI),relative pitch exceedance(RPE)and tremolo index(TI).Then,two objective evaluation models of small motor SQ were established to characterize the multi-dimensional subjective perception attributes by using multiple linear regression(MLR)and support vector regression(SVR)respectively,which can be used for the prediction and evaluation of the small motor SQ.The results show that the prediction accuracy of the model established by SVR method was higher than that of MLR,and SVR has stronger robustness.The objective evaluation model of small motors SQ established in this study is of great importance for improving the sound quality of small motors.

Study on the correlation between ambient environment-meteorological factors and the number of visits of acute otitis media,Lanzhou,China

To investigate the correlation between environmental-meteorological factors and daily visits for acute otitis media(AOM)in Lanzhou,China.Methods:Data were collected in 2014e2016 by the Departments of Otolaryngology-Head and Neck Surgery at two hospitals in Lanzhou.Relevant information,including age,sex and visiting time,was collected.Environmental data included air quality index,PM10,PM2.5,O3,CO,NO2 and SO2,and meteorological data included daily average temperature(T,C),daily mean atmospheric pressure(AP,hPa),daily average relative humidity(RH,%)and daily mean wind speed(W,m/s).The SPSS22.0 software was used to generate Spearman correlation coefficients in descriptive statistical analysis,and the R3.5.0 software was used to calculate relative risk(RR)and to obtain exposure-response curves.The relationship between meteorological-environmental parameters and daily AOM visits was summarized.Results:Correlations were identified between daily AOM visits and CO,O3,SO2,CO,NO2,PM2.5 and PM10 levels.NO2,SO2,CO,AP,RH and T levels significantly correlated with daily AOM visits with a lag exposure-response pattern.The effects of CO,NO2,SO2 and AP on daily AOM visits were significantly stronger compared to other factors(P<0.01).O3,W,T and RH were negatively correlated with daily AOM visits.The highest RR lagged by 3e4 days.Conclusions:The number of daily AOM visits appeared to be correlated with short-term exposure to mixed air pollutants and meteorological factors from 2014 through 2016 in Lanzhou.

Analysis of Evaluation Data Collected on Likert Type Items: Humanities-Courses

To improve high quality and/or retain achieved high quality of an academic program, time to time evaluation for quality of each covered course is often an integrated aspect considered in reputed institutions, however, there has been little effort regarding humanities courses. This research article deals with analysis of evaluation data collected regarding humanities course from a College of Commerce & Economics, Mumbai, Maharashtra, India, on Likert type items. Appropriateness of one parametric measure and three non-parametric measures are discussed and used in this regard which could provide useful clues for educational policy planners. Keeping in view of the analytical results using these four measures, regardless of the threshold regarding satisfaction among students, overall performance of almost every subject has been un-satisfactory. There is a need to make a focused approach to take every course at the level of high performance. The inconsistency noticed under every threshold further revealed that under such poorly performing subjects globally, one needs to analyze merely at the global level item. Once the global level analysis reveals high performance of a course, then only item specific analysis may need to be focused to find out the items requiring further improvements.