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Maintenance records of complex industrial equipment contain a large amount of unstructured data (e.g. technician notes) pertaining to repair actions and associated equipment sub-components, degradation conditions, failure mechanisms, etc. These unstructured data can yield valuable insights to improve the equipment design and maintenance plans, resulting in higher productivity and lower operating costs. Since manual review of information is time-consuming, companies make limited use of the maintenance records. To address this opportunity, we propose a taxonomy-guided method for automatically analysing the unstructured data and inferring critical information, specifically the hierarchy of the equipment's sub-assemblies and constituent parts that malfunctioned or failed during a breakdown event. Our method leverages syntactic (related to word frequency) as well as semantic (related to word co-occurrence and their meaning) knowledge. A novel contribution of our work is that we provide a confidence score for the information inferred by our method. Only the maintenance records which receive a low confidence score will require manual review to confirm the automated method's results, thus ensuring minimal use of human resources. We demonstrate the performance of our method using a real-world data set from equipment used in oil rigs. [ABSTRACT FROM AUTHOR]

Projections of the demographic indicators at the national and state level have been made for many years but for decentralized planning and better monitoring of already existing policies projections are needed for smaller subsections of the population. In this paper, an attempt has been made to present a simple nonparametric technique to extrapolate the demographic indicator Total Fertility Rate (TFR) at granular levels to a recent future, where the projected value of that indicator is available at the gross level only by applying a data mining technique. The approach has been used to project the total fertility rate of women in the districts of Uttar Pradesh, India for the year 2026. From this study, the TFR of Uttar Pradesh is found to be 1.98 for the year 2026 which is below the replacement level of fertility. But still, there are 28% (20 out of 71 districts) of the districts have TFR above the replacement level of fertility. [ABSTRACT FROM AUTHOR]

The article examines long-term precipitation trends in Punjab, India, analyzing the effects of climate change on rainfall variability, water availability, and agricultural yields. It highlights how anthropogenic factors such as deforestation, industrialization, urbanization, and greenhouse gas emissions have disrupted climatic patterns, leading to temperature rise, erratic monsoons, and groundwater exploitation.

Most classical statistical tests assume data are normally distributed. If this assumption is not met, researchers often turn to non-parametric methods. These methods have some drawbacks, and if no suitable non-parametric test exists, a normal distribution may be used inappropriately instead. A better option is to select a distribution appropriate for the data from dozens available in modern software packages. Selecting a distribution that represents the data generating process is a crucial but overlooked step in analysing data. This paper discusses several alternative distributions and the types of data that they are suitable for. [ABSTRACT FROM AUTHOR]

The Restricted Mean Survival Time (RMST) serves as a valuable and extensively utilized metric in clinical trials. However, its application becomes intricate when dealing with data affected by length-biased sampling, rendering traditional inference strategies inadequate. To overcome this challenge, we advocate for the adoption of nonparametric techniques. One notably promising approach is the Empirical Likelihood (EL) method, which furnishes robust results without the need for stringent parametric assumptions. In practical scenarios, the underlying sampling distributions often remain elusive, necessitating adjustments in the case of parametric methodologies. The EL method has demonstrated its efficacy in addressing such complexities. Consequently, this paper introduces the EL method for computing RMST in situations involving both length-biased and right-censored data. Additionally, we introduce the concept of adjusted empirical likelihood (AEL) to further enhance the coverage probability, particularly when dealing with smaller sample sizes. To gauge the performance of the EL and AEL methods, we conduct simulations and rigorously compare their results. The findings unequivocally demonstrate that AEL-based confidence intervals consistently provide superior coverage probability when juxtaposed with EL-based intervals. Lastly, we substantiate the practical applicability of our proposed method by employing it in the analysis of a real dataset.

Abstract Background Accurate prediction of subject recruitment, which is critical to the success of a study, remains an ongoing challenge. Previous prediction models often rely on parametric assumptions which are not always met or may be difficult to implement. We aim to develop a novel method that is less sensitive to model assumptions and relatively easy to implement. Methods We create a weighted resampling-based approach to predict enrollment in year two based on recruitment data from year one of the completed GRIPS and PACE clinical trials. Different weight functions accounted for a range of potential enrollment trajectory patterns. Prediction accuracy was measured by Euclidean distance for enrollment sequence in year two, total enrollment over time, and total weeks to enroll a fixed number of subjects, against the actual year two enrollment data. We compare the performance of the proposed method with an existing Bayesian method. Results Weighted resampling using GRIPS data resulted in closer prediction evidenced by better coverage of observed enrollment with the prediction intervals and smaller Euclidean distance from actual enrollment in year 2, especially when enrollment gaps were filled prior to the weighted resampling. These scenarios also produced more accurate predictions for total enrollment and number of weeks to enroll 50 participants. These same scenarios outperformed an existing Bayesian method for all 3 accuracy measures. In PACE data, using a reduced year 1 enrollment resulted in closer prediction evidenced by better coverage of observed enrollment with the prediction intervals and smaller Euclidean distance from actual enrollment in year 2, with the weighted resampling scenarios better reflecting the seasonal variation seen in year (1) The reduced enrollment scenarios resulted in closer prediction for total enrollment over 6 and 12 months into year (2) These same scenarios also outperformed an existing Bayesian method for relevant accuracy measures. Conclusion The results demonstrate the feasibility and flexibility for a resampling-based, non-parametric approach for prediction of clinical trial recruitment with limited early enrollment data. Application to a wider setting and long-term prediction accuracy require further investigation.

Potato is one of the main agricultural commodities and has high economic value. Yield is a trait that becomes a benchmark for user (industry and farmers) in selecting and developing varieties. The selection of superior newly potato somaclones using combined analysis and sustainability index (SI) is still underreported. This study aimed to identify the effect of genotype by environment (growing season) interactions (GEIs), as well as to select superior potato somaclones. The research was conducted in three years (2020 ‒ 2022) in Lembang, West Java, Indonesia. There were 38 somaclones tested in the field, consisting of 37 gamma ray-derived somaclones and one control genotype. The somaclones originated from commercial varieties Agria, Granola, Repita, and Vega. The field trial used a randomized block design that was repeated three times in each year. GEIs were calculated based on a combined ANOVA. Yield stability was assessed using the combined analysis (parametric and non-parametric), genotype plus genotype by environment interaction (GGE) biplot, and sustainability index (SI). Potato somaclones (G), seasons (E) and GEIs were found to have highly significant influence on yield (P < 0.01). According to the combined ANOVA, the GEIs impact accounted for 37.38% of the yield’s total sum of squares. Combining parametric and non-parametric measurements, seven somaclones, P3, P4, P5, P6, P8, P9, and P26, were selected. GGE biplot selected five stable somaclones, namely P3, P4, P22, P23, and P26, while SI selected eight high-yielding and stable somaclones, namely P3, P4, P5, P8, P13, P16, P26, and P32. Based on various stability measurements, three genotypes have been identified as the superior somaclones, namely P3, P4 and P26. This analysis can assist in selecting activities to determine superior somaclones.

The interquartile range is a statistical measure well suited to describe the variability of the data at hand, both at the population level and for sample data. The interquartile range is particularly useful when the distribution of the data is asymmetric or irregularly shaped. Here, the use of the interquartile range is investigated when the main aim is to compare the variability of two distributions using two independent random samples, without the need to make any distributional assumptions. Several techniques are compared through numerical studies and real data examples, with a particular attention given to the use of sample quantiles based on the Harrel-Davis estimator or the quantile regression. [ABSTRACT FROM AUTHOR]

The Restricted Mean Survival Time (RMST) serves as a valuable and extensively utilized metric in clinical trials. However, its application becomes intricate when dealing with data atiected by lengthbiased sampling, rendering traditional inference strategies inadequate. To overcome this challenge, we advocate for the adoption of nonparametric techniques. One notably promising approach is the Empirical Likelihood (EL) method, which furnishes robust results without the need for stringent parametric assumptions. In practical scenarios, the underlying sampling distributions often remain elusive, necessitating adjustments in the case of parametric methodologies. The EL method has demonstrated its eficacy in addressing such complexities. Consequently, this paper introduces the EL method for computing RMST in situations involving both length-biased and right-censored data. Additionally, we introduce the concept of adjusted empirical likelihood (AEL) to further enhance the coverage probability, particularly when dealing with smaller sample sizes. To gauge the performance of the EL and AEL methods, we conduct simulations and rigorously compare their results. The findings unequivocally demonstrate that AEL-based confidence intervals consistently provide superior coverage probability when juxtaposed with EL-based intervals. Lastly, we substantiate the practical applicability of our proposed method by employing it in the analysis of a real dataset. [ABSTRACT FROM AUTHOR]

The families of probability distributions with heavy tails are considered one of the most essential continuous distributions that have broad uses in various areas of life, especially in areas related to economics, which is concerned with the subject of oil prices and securities, so the research was estimated two types of regression functions, represented by the multivariable parametric, non-parametric regression function, depending on the Matrix-Variate Variance Gamma(M-VVG) distribution and Matrix-Variate Normal Inverse Gaussian(M-VNIG) distribution for the error of models. As the multivariate non-parametric regression model was converted into a linear model based on the local polynomial smoother and through the classical method, multivariate Nadarya Watson smoother (NW-S) and the multivariate local linear smoother (LL-S) were obtained, as well as estimating the multivariate parametric regression function through the use of the maximum likelihood method (ML-M). The results were applied to actual data represented by Brent crude oil price data for the period from (2/11/2020) to (8/12/2020) measured in US dollars, and through the results of the Matlab programming and depending on the MSE standard, we note the superiority of the multivariate (NW-S) for the multivariate nonparametric regression function and for the error that follows a (M-VVG) distribution and for a Gauss kernel function, the value of the criterion was (MSE=0.2314), followed by the (M-VNIG) where the criterion (MSE=0.5601). The superiority of the identical error distributions for the multivariate parametric regression function, as well as the value of the criterion, was (MSE=0.4321,0.6433) respectively. [ABSTRACT FROM AUTHOR]

Abstract Under different environmental conditions, crop yields differ primarily due to G and E interactions. The Global Rice Array (GRA-IV) is IRRI's fourth flagship project to identify climate-resilient rice genotypes. Use of Several univariate and multivariate methods can differentiate genotypes based on their behaviour under different environmental conditions. Since genotypes were ranked differently across models, ASR and Yield Stability Index (YSI) were combined in this study. It included 15 rice genotypes (from a collection of global rice arrays IV called the "Antenna Panel"). Experimentation done in five diverse environments in the Northern Tarai region of India. Grain yield over five diverse environments was significantly influenced by genotypes, G (24.51%), environments, E (40.79%), and genotype and environment effects combined (34.69%). G2, G5, G8, G15 and G10 exhibited lowest ASR values. G2 is the most stable high-yielder, concluded on the basis of new stability index calculated by combining the ASR's and YSI values; these superior genotypes can benefit breeding programs in the future. A stable-high yielder can be more accurately predicted with the new stability index.

Background: Pigeonpea is second most important pulse crop of India after chickpea and it is necessary to identify its high yielding and stable genotypes to feed the increasing population of country. Methods: The present study was laid down in a randomized block design with three replications during kharif season of 2016-2019 at GBPUAT, Pantnagar using twenty genotypes of pigeonpea with an aim to identify the high yielding and stable genotypes. The hierarchical cluster analysis (HCA) based on mean seed yield and Average of Sum of Ranks (ASR) of all measures (Parametric and nonparametric) was used in present study. Result: The pooled ANOVA revealed the presence of significant differences among genotypes, environments and G x E interaction effects. The results of hierarchical cluster analysis indicated the genotypes PA 622 (yield=1774.85 kg/ha, ASR=2.00), PA 620 (yield= 1579.92 kg/ha, ASR=2.18), UPAS 120 (yield=1268.57 kg/ha, ASR=2.87), PA 626 (yield=1571.40 kg/ha, ASR=5.56), PUSA 992 (yield= 1331.17 kg/ha, ASR=5.68) and PA 628 (yield= 1271.50 kg/ha, ASR=7.06) as most stable and high yielding and hence these genotypes can be recommended for pigeonpea improvement programmes. [ABSTRACT FROM AUTHOR]

Parametric methods are widely utilized in RGB-based human mesh recovery, relying on precise statistical human body model parameters that are challenging to obtain. In contrast, non-parametric transformer-based approaches excel but are applied only to monocular RGB tasks. To address these limitations, this paper presents Semi-Supervised Multi-View Human Mesh Recovery Transformer (SS-MVMETRO), which combines multi-view information with non-parametric methods for the first time. Our model encodes different images according to their respective view directions, fusing local features around key points of joints and vertices. Then, a residual-like structure is proposed to integrate the fused features in the mesh recovery transformer, which subsequently predicts the 3D coordinates of the human mesh vertices. Additionally, we divide different views into the main view and auxiliary views and propose a semi-supervised training approach that requires fewer matching labels. The efficacy of our work is validated on two datasets, Human3.6M and Mpi_inf_3dph, through quantitative and qualitative experiments. The results indicate that SS-MVMETRO improves the reconstruction accuracy, surpassing previous image-based methods by at least 8.9% in terms of Procrustes Analysis Mean-Per-Joint-Position-Error (PA-MPJPE). [ABSTRACT FROM AUTHOR]

Aims: Overlapping asymmetric data sets are where a large cohort of observations have a small amount of information recorded, and within this group there exists a smaller cohort which have extensive further information available. Missing imputation is unwise if cohort size differs substantially; therefore, we aim to develop a way of modelling the smaller cohort whilst considering the larger. Methods: Through considering traditionally once penalized P-Spline approximations, we create a second penalty term through observing discrepancies in the marginal value of covariates that exist in both cohorts. Our now twice penalized P-Spline is designed to firstly prevent over/under-fitting of the smaller cohort and secondly to consider the larger cohort. Results: Through a series of data simulations, penalty parameter tunings, and model adaptations, our twice penalized model offers up to a 58% and 46% improvement in model fit upon a continuous and binary response, respectively, against existing B-Spline and once penalized P-Spline methods. Applying our model to an individual's risk of developing steatohepatitis, we report an over 65% improvement over existing methods. Conclusions: We propose a twice penalized P-Spline method which can vastly improve the model fit of overlapping asymmetric data sets upon a common predictive endpoint, without the need for missing data imputation. [ABSTRACT FROM AUTHOR]

This paper compares the forecasting performance of three structural econometric models, namely the non-parametric, ARIMAX and the Kalman filter models, in predicting stock returns in an emerging market economy using South Africa as a case study. The proposed models have different functional forms. Each of the functional forms accounts for specific characteristics and properties of stock returns in general and in a small open economy in particular. The findings of the paper indicate that the Kalman filter and ARIMAX model both outperform the non-parametric model indicating the dominant characteristics of nonlinearity and Markov properties of stock market returns in South Africa. [ABSTRACT FROM AUTHOR]

It is well known among practitioner, majority collected data from industrial process plant are unlabeled. The collected historical data if utilize, able to provide vital information of process plant condition. Learning from unlabeled dataset, this study proposed Unsupervised LSTM-KDE approach as a measure to predict fault in industrial process plant. The residual based fault detection approach framework is utilized with long short-term memory (LSTM) as the main pattern learner for nonlinear and multimode condition that usually appear in process plant. Furthermore, kernel density approach (KDE) is used to determine the threshold value in non-parametric condition of unlabeled data. The LSTM-KDE approach later is evaluated with numerical data as well as Tennessee Eastman process plant dataset. The performance also was compared to Principal Component Analysis (PCA), Local outlier factor (LOF) and Auto-associative Kernel Regression (AAKR) to further examine the LSTM-KDE performance. The experimental results indicate that the LSTM-KDE fault detection approach has better learning performance and accuracy compared to other approaches.

With the widespread application of smart devices, the security of internet of things (IoT) systems faces entirely new challenges. The IoT data stream operates in a non-stationary, dynamic environment, making it prone to concept drift. This paper focused on addressing the issue of concept drift in data streams, with a key emphasis on introducing an innovative drift detection method-ensemble multiple non-parametric concept localization detectors, abbreviated as EMNCD. EMNCD employs an ensemble of non-parametric statistical methods, including the Kolmogorov-Smirnov, Wilcoxon rank sum and Mann-Kendall tests. By comparing sample distributions within a sliding window, EMNCD accurately detects concept drift, achieving precise localization of drift points, and enhancing overall detection reliability. Experimental results demonstrated the superior performance of EMNCD compared to classical methods on artificial datasets. Simultaneously, to enhance the robustness of data stream processing, we presented an online anomaly detection method based on the isolation forest (iForest). Additionally, we proposedwhale optimization algorithm (WOA)-extreme gradient boosting (XGBoost), a drift adaptation model employing XGBoost as a base classifier. This model dynamically updates using drift points detected by EMNCD and fine-tunes parameters through the WOA. Real-world applications on the edge-industrial IoTset (IIoTset) intrusion dataset explore the impact of concept drift on intrusion detection, where IIoT is a subclass of IoT. In summary, this paper focused on EMNCD, introducing innovative approaches for drift detection, anomaly detection, and drift adaptation. The research provided practical and viable solutions to address concept drift in data streams, enhancing security in IoT systems.

This study focuses on forecasting German generation-based CO2 emission factors to develop accurate prediction models, which help to shift flexible loads in time with low emissions. While most existing research relies on point forecasts to predict CO2 emission factors, the presented methods are utilized to perform interval forecasts. In addition, compared to other studies, recent data that extends over a long period is used. The study describes the used data and discusses the concept of walk-forward validation. Further, various models are employed and tuned to forecast the emission factors, including benchmark, parametric (e.g., SARIMAX), and non-parametric (bagging, random forest, gradient boosting, CNN, LSTM, MLP) models. The study reveals that all applied parametric and non-parametric models yield better results than the benchmark models, while the gradient boosting model has the lowest mean absolute error with 40.66 gCO2/kWh, the lowest mean absolute percentage error 8.17%, and the random forest has the lowest root mean square error with 57.61 gCO2/kWh. However, the potential of the deep learning models was not fully exploited. In a live application, the implementation effort should be evaluated against the benefit of better prediction. [ABSTRACT FROM AUTHOR]

Introduction: The appropriate choice of statistical tools for inferential data analysis is fundamental in science. Thus, identifying the behavior of the observations is essential; to select, with the greatest possible precision, the statistical technique that leads to accurate results and enriching conclusions. Objective: The distribution of raw and residual data from cattle and chicken farming was studied by verifying parametric assumptions; In turn, three statistical methods were compared, by zootechnical species, discussing their plasticity, adjustment and precision. Materials and methods: The following were analyzed in cattle: body condition, live weight, hair length and biochemical constants (calcium, phosphorus, magnesium). In chickens: live weight, breast width, thigh length, crest length, presence of endo and ectoparasites. Tests of normality (Shapiro Wilk and Kolmogorov (Lilliefors)) and homogeneity of variances (Levene) were applied. The inferential methods were considered in bovines: ANOVA with Tukey; Welch's ANOVA with the Games Howell test and Kruskal Wallis with Dunn's test. In birds: the student test, with Welch and Wilcoxon-Mann-Whitney correction. Results: Normality tests maintained similar results. A difference was found in decision criteria between the inferential analyses, for magnesium level and thigh length. Conclusions: It is explicitly recommended, in veterinary and zootechnical studies, with scientific rigor, to analyze the normality and homogeneity of variance, to appropriately identify and know the behavioral pattern of the data coming from the work, in order to properly implement the inferential statistical tool. that will contribute to discriminating chance and causality in the events treated. [ABSTRACT FROM AUTHOR]

The analysis of variance is a statistical technique widely used in the design of experiments and different research areas. It has been modeled using a classical or frequentist approach. With the computational power that is currently available, the Bayesian approach is an essential statistical tool related to hypothesis testing. However, conformity with classical techniques, ignorance of Bayesian statistics, and lack of easy-to-use software are obstacles to its frequent application. In this work, the use of a reproducible statistical package in R is proposed. It presents options to perform an analysis of variance in a classical (frequentist) and Bayesian way when the assumptions of the frequentist approach are not met or when a level of more specific inference such as quantifying the evidence provided by a data set for a given hypothesis, with the possibility of contributing to the understanding of the rejection or not of the statistical hypotheses raised, through interactive graphics presented in an emerging Shiny panel. [ABSTRACT FROM AUTHOR]

In the recent years, the trend of environmental sustainability is rapidly increasing by adopting renewable energy resources. However, the main concern is that whether renewable energy consumption contributes to economic growth. To investigate the issue, this study analyzes renewable energy led economic growth hypothesis in the Next-11 economies over the period 1990–2020. Also, this study aims to examine the influence of industry value added, gross national expenditure, and trade openness on economic growth of these economies. Along with the second-generation panel unit root test, this study employed the non-parametric panel data approach, i.e., quantile method of moments regression. The estimated results reveal the slopes coefficients are heterogeneous and cross-sectional dependency is present in the panel. The non-parametric approach reveals that validity of renewable energy led growth hypothesis. Also, the industry value added, gross national expenditure, and trade openness are found positively affecting economic growth of these economies. The panel causality test gives indication of the two way causal association between the variables. Based on the obtained results, policy implications are also provided for governors and researchers. [ABSTRACT FROM AUTHOR]

Globalization is an undeniable phenomenon in the current era and different theories of globalization-economic growth show that there is no theoretical agreement in this regard and there are many supporters and oppositions in this field. Considering that, firstly, globalization has different aspects including economic, cultural and political. Second, the effects of globalization are different in developing and developed countries and thirdly, according to the theoretical point of view, the effect of globalization on economic growth varies over time. Therefore, in the present study, using the time-varying non-parametric panel data model and applying three different aspects of economic, cultural and political globalization KOF index, and considering the de facto and de jure effects of the mentioned index. It has been investigated the different effects of globalization over time on the GDP per capita of countries with high per capita income (28 countries) and middle per capita income (36 countries). The results of estimating the model used in this research based on the local linear dummy variable method for time-varying non-parametric panel data showed that except for the first few years in the period from 1980 to 2019, the economic globalization index has increased the per capita income of countries with high per capita income, However, the index of economic globalization in countries with middle per capita income had a positive effect on per capita income only in the years 1996 to 2008, and had a negative effect on it in the rest of the years. Also by dividing globalization into de facto and de jure aspects determined Both de facto and de jure aspects of economic globalization during the period from 1980 to 2019 on average have led to economic growth in countries with high per capita income. But de jure aspects of economic globalization almost had a positive effect on the economic growth of countries with middle per capita income especially after 1995 to 2019 And economic globalization has a negative effect on the economic growth of these types of countries from an de facto aspect in most of the investigated years. [ABSTRACT FROM AUTHOR]

Abstract The basic Wilcoxon rank-sum test is a nonparametric method that allows for the comparison of characteristics between two different populations. It is commonly used to analyze associations between a binary outcome (a population indicator) and the parameters of interest. However, when the binary outcome is transformed into a count variable (e.g. 0, 1, 2, 3…), the Wilcoxon test can still be applied to examine the association between the count variable and the dependent parameter in a nonparametric manner. This can be done by dichotomizing the count as zeros and non-zeros (or by using other thresholds). Nevertheless, this approach may result in a loss of information and potentially lower statistical power. To address this limitation, we propose a modification to the conventional test called the count weighted Wilcoxon test. This method utilizes all the available data without the need to create a binary variable. By assigning weights to the count variable, the count weighted method has the potential to increase the detection power of the test. In this way, we demonstrate the applicability of this new method to medical data, particularly when researchers are interested in analyzing associations between numerical and count variables. [ABSTRACT FROM AUTHOR]

Multiclass probability estimation is the problem of estimating conditional probabilities of a data point belonging to a class given its covariate information. It has broad applications in statistical analysis and data science. Recently a class of weighted Support Vector Machines (wSVMs) has been developed to estimate class probabilities through ensemble learning for K-class problems (Wu et al., 2010; Wang et al., 2019), where K is the number of classes. The estimators are robust and achieve high accuracy for probability estimation, but their learning is implemented through pairwise coupling, which demands polynomial time in K. In this paper, we propose two new learning schemes, the baseline learning and the One-vs-All (OVA) learning, to further improve wSVMs in terms of computational efficiency and estimation accuracy. In particular, the baseline learning has optimal computational complexity in the sense that it is linear in K. Though not the most efficient in computation, the OVA is found to have the best estimation accuracy among all the procedures under comparison. The resulting estimators are distribution-free and shown to be consistent. We further conduct extensive numerical experiments to demonstrate their finite sample performance. [ABSTRACT FROM AUTHOR]

Abstract As technology improves, how to extract information from vast datasets is becoming more urgent. As is well known, k-nearest neighbor classifiers are simple to implement and conceptually simple to implement. It is not without its shortcomings, however, as follows: (1) there is still a sensitivity to the choice of k-values even when representative attributes are not considered in each class; (2) in some cases, the proximity between test samples and nearest neighbor samples cannot be reflected accurately due to proximity measurements, etc. Here, we propose a non-parametric nearest neighbor classification method based on global variance differences. First, the difference in variance is calculated before and after adding the sample to be the subject, then the difference is divided by the variance before adding the sample to be tested, and the resulting quotient serves as the objective function. In the final step, the samples to be tested are classified into the class with the smallest objective function. Here, we discuss the theoretical aspects of this function. Using the Lagrange method, it can be shown that the objective function can be optimal when the sample centers of each class are averaged. Twelve real datasets from the University of California, Irvine are used to compare the proposed algorithm with competitors such as the Local mean k-nearest neighbor algorithm and the pseudo-nearest neighbor algorithm. According to a comprehensive experimental study, the average accuracy on 12 datasets is as high as 86.27 $$\%$$ % , which is far higher than other algorithms. The experimental findings verify that the proposed algorithm produces results that are more dependable than other existing algorithms.

According to the importance of assessing the presence of time delay between the occurrence of various hydrological and meteorological phenomena, the study aim is to introduce a new method (with high ability and non-sensitivity to the abnormality of datasets and the existence of outliers) for determining the time delay in mentioned data series. In this research, a new measure to detect the time delay between two stationary time series (Non-Parametric Cross-Correlation Function or NCCF, called Spearman's CCF or SCCF) is introduced, which has very low sensitivity to abnormality of data series and also the existence of outliers in the data series. The numerical studies verify the ability of the proposed measure. In standard uniform and exponential (with mean 1) time series, at 100% of numerical analyses and in standard Gaussian time series at more than 60% of numerical studies, the ability of SCCF was more than the CCF. The applicability of the proposed measure in practice was also studied using the Reconnaissance Drought Index (RDI) data series of 20 stations over Iran during 1967–2019 in 1, 3, and 12-month time scales. The results of the practical study also proved the appropriate performance of the proposed model in all time scales. HIGHLIGHTS A modified version of the cross-correlation function was presented.; Output of this research is a new measure to detect the time delay between two stationary time series.; In this research, data series of 20 stations with various climate conditions was used.; The results are usable in better understanding the behavior of climatic parameters (especially drought).;

Regression analysis is known as statistical methods applied to model and analyze the relationship between variables. Regression method can be examined as parametric, non-parametric and semiparametric regression methods.The parametric regression method assumes that the dependent variable is in a linear relationship with the independent variables and that the shape of the relationship is known. If these assumptions are not met, non-parametric regression methods are applied. However, these methods cause difficulties especially in the interpretation part due to the problem of multidimensionality when there is more than one independent variable. Thus, when there is more than one independent variable, some of the independent variables may be in a linear relationship with the dependent variable, while the other part may be in a nonlinear relationship. Thus, in order to model these relationships, semiparametric regression methods, which are the additive combination of parametric and non-parametric regression methods, are used.In this study, parametric regression method, definition of non-parametric regression method and assumption conditions are given. It has been shown that the semiparametric regression method can be applied in cases where these assumptions are not met. Thus, in the study, regression methods were examined in three different parts, and parametric, non-parametric and semiparametric regression methods were examined theoretically.

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The orientation and focus of this research piece are the extraction of the foreground and the compositing of this extracted region into another background. This phenomenon is termed as, 'Image Matting', which is frequently employed in film production or the digital media world. The proposed method approaches the ill-posed nature of image matting via a non-parametric sampling-based method along with the clustering technique known as ‘Superpixel’. In the proposed method, pixels of the entire image(s) tend to gather in close proximity under one unit (Superpixel) with respect to color, intensity, and texture. This gathering in close proximity reduces the search space more than 20 times and helps in efficiently finding the association of unknown regions with the samples from the background and foreground. The use of samples facilitates the pixel color assimilating with local image structure, which is significant to calculate a good resultant alpha matte particularly in the image having complex texture, and in natural images. As per my knowledge and study, the matting problem using centroids of Superpixels has not previously been explored. Results are evaluated on different images on an online standard open-source dataset, available for image matting. Results are comparable to the different matting algorithms applied independently on images of the dataset. Confidence and ameliorated refinements of proposed methods are evident in the obtained results compared to other image matting methods. [ABSTRACT FROM AUTHOR]

In this study, we construct an incentive model under which the optimal wage incentive scheme is a linear contract. This linear incentive contract can induce truth telling by workers about their inefficiency types and promote optimal efforts by them. We use the non‐parametric kernel estimation method to estimate the model primitives to analyse the insufficient efforts in the Chinese labour market. The empirical results based on data collected from the China Health and Nutrition Survey show that efficient male workers in white‐collar occupations do not exert sufficient efforts in the Chinese labour market. And wages for male workers in white‐collar occupations are undervalued. [ABSTRACT FROM AUTHOR]

Background: Conventional cerebrovascular reactivity (CVR) estimation has demonstrated that many brain diseases and/or conditions are associated with altered CVR. Despite the clinical potential of CVR, characterization of temporal features of a CVR challenge remains uncommon. This work is motivated by the need to develop CVR parameters that characterize individual temporal features of a CVR challenge. Methods: Data were collected from 54 adults and recruited based on these criteria: (1) Alzheimer's disease diagnosis or subcortical Vascular Cognitive Impairment, (2) sleep apnea, and (3) subjective cognitive impairment concerns. We investigated signal changes in blood oxygenation level dependent (BOLD) contrast images with respect to hypercapnic and normocapnic CVR transition periods during a gas manipulation paradigm. We developed a model-free, non-parametric CVR metric after considering a range of responses through simulations to characterize BOLD signal changes that occur when transitioning from normocapnia to hypercapnia. The non-parametric CVR measure was used to examine regional differences across the insula, hippocampus, thalamus, and centrum semiovale. We also examined the BOLD signal transition from hypercapnia back to normocapnia. Results: We found a linear association between isolated temporal features of successive CO2 challenges. Our study concluded that the transition rate from hypercapnia to normocapnia was significantly associated with the second CVR response across all regions of interest (p < 0.001), and this association was highest in the hippocampus (R2 = 0.57, p < 0.0125). Conclusion: This study demonstrates that it is feasible to examine individual responses associated with normocapnic and hypercapnic transition periods of a BOLD-based CVR experiment. Studying these features can provide insight on between-subject differences in CVR. [ABSTRACT FROM AUTHOR]

Motivated by an open problem of validating protein identities in label-free shotgun proteomics work-flows, we present a testing procedure to validate class (protein) labels using available measurements across N instances (peptides). More generally, we present a non-parametric solution to the problem of identifying instances that are deemed as outliers relative to the subset of instances assigned to the same class. The primary assumption is that measured distances between instances within the same class are stochastically smaller than measured distances between instances from different classes. We show that the overall type I error probability across all instances within a class can be controlled by some fixed value (say α). We also demonstrate conditions where similar results on type II error probability hold. The theoretical results are supplemented by an extensive numerical study illustrating the applicability and viability of our method. Even with up to 25% of instances initially mislabeled, our testing procedure maintains a high specificity and greatly reduces the proportion of mislabeled instances. The applicability and effectiveness of our testing procedure is further illustrated by a detailed example on a proteomics data set from children with sickle cell disease where five spike-in proteins acted as contrasting controls. [ABSTRACT FROM AUTHOR]

Complex time series appear in numerous applications and are related to some essential physiological and natural systems. Their comparison faces big challenges: 1) with different complexity; 2) with significant phase shift in one series or shift∖on the time axis. Existing methods work well for periodic time-series data, but fail to produce satisfactory results in complex time-series. In this paper, we introduce a novel distance function based on the evolution rule for complex time series comparison. Here, the evolution rule, as the innate generative mechanism of time series, is creatively used to characterize complicated dynamics from complex time series. The comparison includes different level comparisons: the coarse level is to compare the difference in complexity, and the fine level is to compare the difference in actual evolution behavior. The proposed method is inspired by the observation that similar sequences come from the same source, e.g. a person's heart, in the case of ECG, thus two similar series will have the same innate generative mechanism. The performance has been verified by the conducting experiments, and the experiment results show that the proposed method is superior to the previously existing methods in clustering and classification on some real data sets. [ABSTRACT FROM AUTHOR]