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AbstractHypothesis testing for the Weibull distribution always raises attention in the literature. It is challenging especially in small-sample scenarios. In this paper, we propose a new Kolmogorov-Smirnov type test based on the distance between two empirical functions, one from the data and another from the representative points of the underlying distribution. A bias correction technique is used to estimate unknown parameters in the Weibull distribution. We also discuss how to choose a suitable number of representative points based on the proposed loss function, which is recommended for constructing the empirical function from the Weibull distribution. To compare with tailor-made tests in the literature, a simulation study of the empirical type I error and testing power is conducted. It shows that our proposed test statistic is more powerful in most alternative scenarios, and it significantly improves the power of testing in small-sample scenarios. Finally, two real-world datasets are conducted to further demonstrate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Arab Journal for Scientific Publishing is the property of Research & Development of Human Recourses Center (REMAH) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Understanding rainfall variability and trends is crucial for effective water resource management and disaster preparedness, particularly in tropical regions like Uganda. This study analyzes the trends and variability of the Annual Maximum Series (AMS) and seasonal rainfall data across four rainfall stations in Uganda, comparing observed data with various Remotely Sensed Rainfall (RSR) products. The key methods used in this study include the Mann–Kendall test and Sen's slope estimator for trend analysis, AMS rainfall variability analysis using statistical performance metrics such as the Nash–Sutcliffe Coefficient of Efficiency (NSE) and Percent Bias (PBIAS), and data distribution comparisons based on goodness-of-fit evaluation using the Kolmogorov–Smirnov (KS) test. The results indicate that most trends in the seasonal rainfall and AMS data are statistically insignificant. However, the September to November (SON) observed rainfall at the Gulu station shows a statistically significant increasing trend of 7.68 mm/year (p-value = 0.03). Based on the PBIAS metric, GPCC and NOAA_CPC products outperform other RSR data products. At the Jinja station, NOAA_CPC has a PBIAS value of −12.93% and GPCC, −14.64%; at Soroti, GPCC has −9.66% and NOAA_CPC, −14.79%; at Mbarara, GPCC has −5.93% and NOAA_CPC, −11.63%; and at Gulu, GPCC has −3.05% and NOAA_CPC, −19.23%. The KS test results show significant differences in the distribution of RSR data and observed rainfall data, though GPCC shows significant agreement at the Gulu (p-value = 0.60) and Mbarara (p-value = 0.14) stations. Additionally, NOAA_CPC outperforms other RSR data products at the Mbarara station, with a KS p-value of 0.24. This study highlights the limitations of current RSR datasets in replicating observed AMS rainfall data. Based on KS test results, GPCC is identified as a better product for hydrological applications at the Gulu, Jinja, and Soroti station areas compared to other RSR products. For the Mbarara station, NOAA_CPC outperforms other RSR products. [ABSTRACT FROM AUTHOR]

Process monitoring and fault diagnosis systems are essential for ensuring the quality and safety of industrial processes. Real industrial process data always has dynamic features, autocorrelation features, and non‐Gaussian distributions, making many multivariate statistical process monitoring (MSPM) methods, such as principal component analysis (PCA), not work well. To solve the problem, a new process monitoring method (named DPCA‐ICA‐moving window Kolmogorov–Smirnov test [MWKS]) is proposed in the paper, which is a combination of DPCA and independent component analysis (ICA) with adaptive weight parameters and introduces a moving window technique and two‐sample K‐S test to obtain the K‐S distance of the commonly used Hotelling's T2 and square predictive error as new monitoring parameters. Experimental results on the Tennessee Eastman chemical process demonstrate the effectiveness and accuracy of the proposed method, which shows it has better performance than existing commonly used methods. The average fault detection rate (FDR) is 93.131%, while the average false alarm rate (FAR) of the proposed method is only 0.1%. [ABSTRACT FROM AUTHOR]

Motivated by empirical observations, we proposed a possible extension of Gibrat's law. By applying it into the random growth theory of income distribution, we found that the income distribution is described by a generalized Pareto distribution (GPD) with three parameters. We observed that there is a parameter $ \eta $ in the GPD that plays a key role in determining the shape of income distribution. By using the Kolmogorov-Smirnov test, we empirically showed that, for typical market-economy countries, $ \eta $ is significantly close to 0, indicating that the income distribution is characterized by a two-class pattern: The bottom 90% of the population is approximated by an exponential distribution, while the richest 1%~3% is approximated by an asymptotic power law. However, we empirically found that in China during the period of the planned economy and the early stages of market reform (from 1978 to 1990), $ \eta $ deviated significantly from 0, indicating that the bottom of the population no longer conformed to an exponential distribution.

Abstract The stress measurements determined by both the overcoring (OC) and hydraulic fracturing (HF) methods in the Shuichang iron mine and Sanshandao gold mine were compared and evaluated, respectively. The results indicate that the independent OC and HF data in the two mines reveal the same dominant faulting stress regime. The σ H orientations derived from the OC and HF methods in the Shuichang iron mine are dominantly oriented in the N81.1°W–N89.4°W and N77.0°E–N88.0°E, respectively, and the σ H orientations yielded from the OC and HF techniques in the Sanshandao gold mine are predominantly in the N30°W–N90°W and N55.5°W–N60.4°W, respectively; hence, the σ H orientations obtained by the two different methods in the two mines are comparatively similar. In addition, the shapes of the probability density diagrams using an improved Bayesian regression approach of the three principal stresses measured by the OC and HF methods in the same mine are quite similar, and all the obtained Kolmogorov–Smirnov test p-values are larger than the selected significance level of 0.01, indicating that the stress data interpreted by the two methods approximately follow the same distribution law. Thus, the performance of the two techniques and the reliability of the measured data are satisfactory.

One technique, especially in chaos-based cryptographic applications, is to include the message in the evolution of the dynamical system. This paper aims to find out if and to what extent the statistical behavior of the chaotic system is affected by the message inclusion in its dynamic evolution. The study is illustrated by the dynamical system described by the logistic map in cryptographic applications based on images. The evaluation of the statistical behavior was performed on an original scheme proposed. The Monte Carlo analysis of the applied Kolmogorov–Smirnov statistical test revealed that the dynamical system in the processing scheme with message inclusion does not modify its proper statistical behavior (revealed by definition relation). This was possible due to the proposed scheme designed. Namely, this scheme contains a decision switch which, supported by an appropriate choice of the magnitude of the scaling factor, ensures that the values of the dynamical system are maintained in the definition domain. The proposed framework for analyzing the statistical properties and for preserving the dynamical system behavior is one main contribution of this research. The message inclusion scheme also provides an enhancement with cryptographic mixing functions applied internally; the statistical behavior of the dynamical system is also analyzed in this case. Thus, the paper contributes to the theoretical complex characterization of the dynamical system considering also the message inclusion case. [ABSTRACT FROM AUTHOR]

We study a nonparametric test procedure based on order statistics for testing the null hypothesis of equality of two continuous distributions. The exact null distribution of the proposed test statistic is obtained using an enumeration method and a novel combinatorial argument. A recurrence relation for the probability generating function and a sequential approach for computing the mean and variance of the distribution are given. Critical values and characteristics of the distribution for selected small sample sizes are presented. For the Lehmann alternative family, the exact power function of the new test is derived, and its power performance is examined. We also study the power performance of the proposed test under the location-shift and scale-shift alternatives using Monte Carlo simulations and observe its superior performance when compared to commonly used nonparametric tests under various scenarios. A generalization of the proposed procedure for unequal sample sizes is discussed. An illustrative example and some concluding remarks are provided. [ABSTRACT FROM AUTHOR]

The shear strength (particularly soil cohesion) of rooted soil is an important parameter that reflects the true erodibility of meadows, particularly in meadows experiencing different degrees of degradation, ranging from undegraded (UD) through to lightly degraded (LD) and from moderately degraded (MD) to heavily degraded (HD). The cohesion of rooted soil is controlled not only by the soil moisture content and its natural density, but also by roots (including not only their density, spatial distribution, and diameters, but also their tensile strength). This study aimed to improve the current understanding of the erosion resistance of rooted soil and elucidate the impact of meadow degradation on the physical–mechanical indices of rooted soil. Based on this discussion, a one-way analysis of variance was performed at significance levels of 0.05 and 0.01 to test the impact of degradation on the physical–mechanical indices of rooted soil across degradation. Furthermore, the probabilistic distributions of the physical–mechanical indices were described using normal, gamma, Weibull, and generalized extreme values (GEV). The Kolmogorov–Smirnov (KS) test was used to identify the optimal distribution based on p-values and the Pearson correlation coefficient was used to quantify the correlation between the cohesion of rooted soil and other indices. The one-way ANOVA indicated that the soil's natural density and soil moisture content decreased firstly, followed by an increase, the root amount and cohesion decreased, the root content experienced an initial increasing and then decreasing trend, and the internal friction angle exhibited a stepwise increasing trend. The four distributions sufficiently described the actual distribution of data on the physical–mechanical indices of rooted soil, except for the internal friction angle in UD and MD soils and the soil moisture content in MD soil. Additionally, the KS tests showed that the optimal distribution depended on both the physical–mechanical indices themselves and the degree of degradation. The Pearson correlation analysis showed that the correlation between cohesion and the other physical–mechanical indices varied considerably depending on the degree of degradation. [ABSTRACT FROM AUTHOR]

The leaked signals, including electromagnetic, power, timing, and temperature generated during the operation of cryptographic devices, contain highly correlated key value information, leading to security vulnerabilities. In practical operations, due to information collection conditions and time limitations, attackers can only obtain limited valid data. At the same time, the attacker's data environment cannot be ideal, and noise can affect the acquisition of valid information. Therefore, to improve the effectiveness of obtaining key values from side-channel information analysis in cryptographic devices, we proposed a data augmentation method based on cycle-consistent generative adversarial networks named EME-CycleGAN. By using generators and discriminators, new data are generated to expand the original electromagnetic information dataset, aiming for better modeling effects. For evaluating the data augmentation effect on side-channel electromagnetic signals, we employed the Kolmogorov–Smirnov test to characterize the original and generated data, serving as the evaluation standard for our network model and work. We utilize the existing data to model and verify side-channel attacks, evaluating the impact of the generated information on the overall experimental results. The proposed structure consists of three main parts: side-channel information acquisition, data verification analysis, and determination of attack positions. Experimental results demonstrate that effective attacks on encryption algorithms can be achieved under small-sample dataset conditions. [ABSTRACT FROM AUTHOR]

Some complex models are frequently employed to describe physical and mechanical phenomena. In this setting, we have an input X$$ X $$ in a general space, and an output Y=f(X)$$ Y=f(X) $$ where f$$ f $$ is a very complicated function, whose computational cost for every new input is very high, and may be also very expensive. We are given two sets of observations of X$$ X $$, S1$$ {S}_1 $$ and S2$$ {S}_2 $$ of different sizes such that only fS1$$ f\left({S}_1\right) $$ is available. We tackle the problem of selecting a subset S3⊂S2$$ {S}_3\subset {S}_2 $$ of smaller size on which to run the complex model f$$ f $$, and such that the empirical distribution of fS3$$ f\left({S}_3\right) $$ is close to that of fS1$$ f\left({S}_1\right) $$. We suggest three algorithms to solve this problem and show their efficiency using simulated datasets and the Airfoil self‐noise data set. [ABSTRACT FROM AUTHOR]

The performance of the Standardized Precipitation Index (SPI) is affected by the choice of an incorrect probability distribution function, which can skew the values of the index, exaggerating or minimizing the severity of drought. This study aims to test data fitability of ten statistical distribution functions (gamma, weibull, exponential, lognormal, gumbel, cauchy, logistic, chi-square, burr, pareto) for SPI computation at time scales (TSs) of 3, 6, 9, 12, 15, 18, 21 and 24 months, and to quantify the errors made if the gamma function were used by default as is the case in general. Monthly precipitation data collected at 24 meteorological stations distributed in the five Agro-Ecological Zones (AEZs) of Cameroon were used for the period 1951-2005. The parameters of the distribution functions were estimated with the Maximum Likelihood (ML) method, and the Kolmogorov-Smirnov (K-S) test was applied to assess how well these functions fit the data. The results show that the logistic and burr distributions provide for several stations of the five AEZs the best data fits. A comparative study between the SPIs from the appropriate distribution and the gamma functions shows a significant qualitative and quantitative difference in several stations and the root mean square error (RMSE) increases with the TS and with the severity of drought. [ABSTRACT FROM AUTHOR]

The absolute maximum response (AMR) of structures might occur posterior to the end of earthquake excitations, hence, extending the dynamic analysis to the post-shaking phase is required. This phenomenon was found to be more common for long-period and low-damping structures, which are prevailing in regions of low-to-moderate seismicity like Australia. In this article, the mechanism of the occurrence of AMR in the post-shaking phase is first explained using single-degree-of-freedom (SDOF) systems subjected to a simple sine wave. Further investigation on real earthquake ground motions reveals that whether an extended analysis is required depends greatly on the characteristics of the post-significant duration phase. Finally, suitable metrics for describing ground motions that require extended analysis (EAGMs) are identified based on the Kolmogorov–Smirnov test. Regression analyses are then conducted to predict the probability of EAGMs. [ABSTRACT FROM AUTHOR]

The issue of earnings management has been of prominent global interests. Earnings management in the forms of loss avoidance have resulted to some notable corporate scandal amongst firms participating in capital markets. The study provides evidence on whether South African firms manipulate earnings to avoid reporting small earnings change and losses. The paper involves all listed firms on the Johannesburg Securities Exchange (2005–2020), but the sample selection adapts to only 205 companies. Based on prior studies, the study supposes prevalence of earnings management towards small profits and applies the nonparametric permutation (Kolmogorov-Smirnov, K-S) test to verify two hypotheses: (a) that managing earnings practice does not differ between financial and non-financial industries (b) that financial crisis does not influence managed earnings phenomenon. Applying the K-S permutation algorithm for three different simulation (1,000, 3,000 and 10,000 replications) using sample of different size (10, 30, and 100) at each bootstrapping, the result provide sufficient evidence to reject our null for all repetitions. The results offer strong evidence to suppose that managing earnings practices differ between financial and non-financial industries and that financial crisis have influence on the distribution of earnings. This has implications for policy purpose to prevent possible corporate scandals through use of discretions.

This paper proposes a novel and reliable leak-detection method for pipeline systems based on acoustic emission (AE) signals. The proposed method analyzes signals from two AE sensors installed on the pipeline to detect leaks located between these two sensors. Firstly, the raw AE signals are preprocessed using empirical mode decomposition. The time difference of arrival (TDOA) is then extracted as a statistical feature of the two AE signals. The state of the pipeline (leakage/normal) is determined through comparing the statistical distribution of the TDOA of the current state with the prior normal state. Specifically, the two-sample Kolmogorov–Smirnov (K–S) test is applied to compare the statistical distribution of the TDOA feature for leak and non-leak scenarios. The K–S test statistic value in this context functions as a leakage indicator. A new criterion called leak sensitivity is introduced to evaluate and compare the performance of leak detection methods. Extensive experiments were conducted using an industrial pipeline system, and the results demonstrate the excellence of the proposed method in leak detection. Compared to traditional feature-based indicators, our approach achieves a significantly higher performance in leak detection. [ABSTRACT FROM AUTHOR]

Introduction: In recent decades, along with the physical development of cities and population increase due to immigration, heat islands, which are mainly the result of human made activities, have gained significant importance. So, the reduction of vegetation cover, the high consumption of fossil fuels, the emission of greenhouse gases and the use of inappropriate materials in urban construction have created the microclimate of a heat island above the metropolis of the world. Finally, it causes an increase in the land surface temperature, atmospheric stability, persistence and stabilization of pollutants and an increase in respiratory diseases. Today, by using remote sensing methods and using digital satellite images, it is possible to examine the land surface temperature with high accuracy and in a short period using MODIS sensors and Terra satellite images. Therefore, the main objective of the current research was to analyze the temporal and spatial variations of heat islands of Isfahan using the principal component analysis method. With this technique, it is possible to identify the seasons and months when the land surface temperature and the heat island increased. Material and Methods: In this regard, the present research considered analyzing the temporal and spatial variations of this phenomenon in Isfahan during a period of 20 years (from 2000 to 2020 AD). To achieve this purpose, the land surface temperature (LST) data extracted by MODIS sensor and Terra satellite was used. These data are available for the whole world with a time resolution of 8 days and a spatial resolution of 1x1 km in a sinusoidal grid with dimensions of 1200x1200 km. Therefore, for Isfahan, 913 images were extracted from the MODIS sensor. Then, with the principal component analysis (PCA) method, the seasons and months that have the most influence on the occurrence of heat islands were identified and in order to analyze the trend of this phenomenon, the Mann-Kendall and the Kolmogorov-Smirnov tests were used. Results and Discussion: The findings of the research indicated that the land surface temperature in the last 20 years in Isfahan had an increasing trend with a rate of 4%. However, this trend in the population and urban centers has been completely different from the surrounding areas of Isfahan. In terms of spatial distribution, the highest occurrence of heat islands has been registered in the eastern and southeastern parts (including Hassan Abad and Jolgeh) due to proximity to the hot deserts of Kavir-Lout, proximity to Sepiddasht, Varzaneh, Shahrak Ramsheh industrial towns and changes in the land use of agricultural to industrial and residential. also in the North-West region (including Mahmudabad and Isfahan city) due to the increase in man-made heating caused by the increase in the consumption of fossil fuels, the growth of residential, industrial and commercial units, temperature has risen. In addition, the results showed that the intensity of the heat island in winter is higher than in summer, and the most intense time of its occurrence was recorded in January and the least in November. Conclusion: Isfahan is integrally affected by global climate conditions, but depending on the geographical location of each part of this province, the land surface temperature and the occurrence of heat islands are different, so that, eastern and central parts, adjacent to Kavir-Lout and close to industrial towns, have an increasing trend. Moreover, the southern parts have a decreasing rate of temperature and heat islands due to less urban population, larger agricultural lands and more vegetation. The increase in urbanization, migration, and the increase in fossil fuel consumption, the decrease in vegetation, the aggravation of drought, and the change in land use have a fundamental role in increasing the occurrence of this phenomenon. [ABSTRACT FROM AUTHOR]

The shear strength (particularly soil cohesion) of rooted soil is an important parameter that reflects the true erodibility of meadows, particularly in meadows experiencing different degrees of degradation, ranging from undegraded (UD) through to lightly degraded (LD) and from moderately degraded (MD) to heavily degraded (HD). The cohesion of rooted soil is controlled not only by the soil moisture content and its natural density, but also by roots (including not only their density, spatial distribution, and diameters, but also their tensile strength). This study aimed to improve the current understanding of the erosion resistance of rooted soil and elucidate the impact of meadow degradation on the physical–mechanical indices of rooted soil. Based on this discussion, a one-way analysis of variance was performed at significance levels of 0.05 and 0.01 to test the impact of degradation on the physical–mechanical indices of rooted soil across degradation. Furthermore, the probabilistic distributions of the physical–mechanical indices were described using normal, gamma, Weibull, and generalized extreme values (GEV). The Kolmogorov–Smirnov (KS) test was used to identify the optimal distribution based on p-values and the Pearson correlation coefficient was used to quantify the correlation between the cohesion of rooted soil and other indices. The one-way ANOVA indicated that the soil’s natural density and soil moisture content decreased firstly, followed by an increase, the root amount and cohesion decreased, the root content experienced an initial increasing and then decreasing trend, and the internal friction angle exhibited a stepwise increasing trend. The four distributions sufficiently described the actual distribution of data on the physical–mechanical indices of rooted soil, except for the internal friction angle in UD and MD soils and the soil moisture content in MD soil. Additionally, the KS tests showed that the optimal distribution depended on both the physical–mechanical indices themselves and the degree of degradation. The Pearson correlation analysis showed that the correlation between cohesion and the other physical–mechanical indices varied considerably depending on the degree of degradation.

Copyright of Journal of Financial Politic & Economic Reviews / Finans Politik & Ekonomik Yorumlar is the property of Journal of Financial Politic & Economic Reviews / Finans Politik & Ekomomik Yorumlar and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Hydrological modeling relies on the inputs provided by General Circulation Model (GCM) data, as this allows researchers to investigate the effects of climate change on water resources. But there is high uncertainty in the climate projections with various ensembles and variables. Therefore, it is very important to carry out bias correction in order to analyze the impacts of climate change at a regional level. The performance evaluation of bias correction methods for precipitation, maximum temperature, and minimum temperature in the Upper Bhima sub-basin has been investigated. Four bias correction methods are applied for precipitation viz. linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), and distribution mapping (DM). Three bias correction methods are applied for temperature viz. linear scaling (LS), variance scaling (VS), and distribution mapping (DM). The evaluation of the results from these bias correction methods is performed using the Kolmogorov–Smirnov non-parametric test. The results indicate that bias correction methods are useful in reducing biases in model-simulated data, which improves their reliability. The results of the distribution mapping bias correction method have been proven to be more effective for precipitation, maximum temperature, and minimum temperature data from CMIP5-simulated data. [ABSTRACT FROM AUTHOR]

This paper studies the effectiveness of the bus priority lane (BPL) for public transport buses in the city of Bengaluru in India. We use the travel times on the BPL corridor as a measure of the effectiveness of the BPL. We find that there is a significant improvement in the travel times after the introduction of the BPL; for the worst 10% of the travel times, we find an improvement between 4% and 28%. Our methodology involves extracting trips on the BPL and computing the travel times for these trips from a time series of GPS information. Our methodology is scalable and can be used to compute the travel times between any two given points in other similar studies. We supplement our results with a novel test (called the D-test) for comparing the levels of stressful driving in the following scenarios: (a) morning peak hours (IST 07:00 h to 11:00 h) versus evening peak hours (IST 17:00 h to 21:00 h), and (b) northward trips versus southward trips on the BPL. We are able to infer that the drivers are generally more stressed during the morning peak hours and during the southward trips on the BPL. Partitioning the BPL into segments, we show that a majority of the segments exhibit similar effectiveness and driver stress trends as the full BPL stretch. We anticipate that corrective measures for the betterment of travel times and driver stress levels (e.g., introducing additional buses subject to vehicle re-balancing constraints, carefully planning the bus schedules to regulate bus traffic throughout the day, etc.) in some segments can lead to further improvements in travel times and reduction in driver stress levels. • Implementation of the Bus Priority Lane can reduce travel times. • Segmented analysis helps identify traffic bottlenecks for targeted intervention. • Driver-stress hot-spots can be identified for targeted intervention. • A novel statistical test compares driving stress levels across a pair of routes. [ABSTRACT FROM AUTHOR]

The two-sample Kolmogorov–Smirnov and Anderson–Darling tests assess the hypothesis that two given samples come from the same population. In this paper, the power of each test was measured using a variety of alternative distributions and varying the sample size. Recommendations for the more powerful test for each common distribution are given, depending on whether the distribution has heavy or light tails. [ABSTRACT FROM AUTHOR]

In order to improve the computational efficiency of testing the hypothesis about the distributions of random variables, the paper proposes a modified testing procedure. This procedure is based on determining the maximum estimation discrepancy for the distribution functions of the compared random variables, followed by the calculation and analysis of confidence intervals for the determined distribution function values. The hypothesis about the identity of distributions is confirmed if obtained confidence intervals overlap at a given level of significance. According to the results of computational experiments, the Kolmogorov–Smirnov and Pearson's chi-squared tests were compared using the following formulas for sampling the intervals of random variables: Sturgess' rule, Heinhold–Gaede formula, and that of the modified method. Pairwise combinations of the following distributions of random variables are considered: uniform, normal, lognormal, and power- law. It is shown that the modified procedure can be generalized to the case of testing hypotheses about the distributions of multidimensional random variables. In contrast to Pearson's chi-squared test, the proposed modified procedure helps to bypass the problem associated with converting the range of random variables into multidimensional intervals. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Internet Applications & Management / İnternet Uygulamaları ve Yönetimi Dergisi is the property of Journal of Internet Applications & Management and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to the temperature rises global warming has become very popular. Increasing of global temperature will disturb the agronomic sector, intensification some of the contagious diseases that may lead to high mortality rates in humans, high demand for electricity, water and food which ultimately affecting the economy of Pakistan. The current research aims to study the best fitted probability distribution that describes the monthly mean temperature (MMT) of four sites in Pakistan are Islamabad, Lahore, Muzaffarabad and Karachi based on secondary data sets which was collected from Pakistan Meteorological department Lahore for the period 1991 to 2020. The Frechet, Weibull and Log-Logistic distributions are applied and the parameters of these distributions are estimated by maximum likelihood and Bayesian estimation methods. Additionally, Log-normal and Generalized Extreme value distribution are considered using the maximum likelihood estimation method to estimate the parameters. Moreover, the graphs of probability density functions also constructed for comparison purposes. The goodness of fit test and model selection criteria such as Kolmogorov-Smirnov test, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) are used to measure the accuracy of the predicted data using theoretical probability distributions. The results show that four sites favor the candidate distributions based on the Pvalues of Kolmogorov-Smirnov test at 5 percent level of significance. However, Log-Logistic distribution is the best fitted as compare to other candidate probability distributions based on AIC and BIC values. Furthermore, quantiles are also calculated using maximum likelihood and Bayesian estimation methods and concluded that quantile estimates are closed to the observed MMT series on the basis of Log-Logistic distribution. [ABSTRACT FROM AUTHOR]

Abstract Introduction: It is important to find a simple connection between physic-chemical parameters and indices due to complexity and difficulties of using some indices. This research was performed to determine the relationship between physicochemical parameters and water quality indices Methods: In this research, dominant water type of Gamasyab and GharehSou rivers was first determined using radial diagram. Then, to classify the water quality of two rivers, 14 quality water indices together with national standards for drinking water 1053 and WHO were used. Linear regression was used to determine the relationships between quality indices and physicochemical parameters. Finally, for the accuracy of the results of linear regression, the obtained relationships were tested for two other water sources (river and Gorgan Bay). Findings: Based on hydrochemical results, the predominant type of water in both rivers is Ca-HCO3. Based on diagrams and criteria related to drinking and agriculture, the water quality of both rivers is in the desired range. The results of Langelier, Ryznar, Larsson-Skold and Puckorius indices showed that water quality for the industrial sector was relatively corrosive. Also, the water quality at the study stations is suitable for livestock drinking. The results of linear regression between quality indices with physicochemical parameters of water and some ion ratios showed that the resulting relationships for calculating quality indices can be used for different water types from fresh to saline. Also, comparing the results of linear regression simulations with water quality indicators, indicates the high accuracy of the relationships in water classification for different uses.

Data analysis techniques that rely on standard statistical tools and algorithms often encounter problems when dealing with data sets that have large sample sizes. In this study, two statistical tests done in conducting simple linear regression analysis were revisited. In particular, the study simulated the effects of large sample sizes and amount of contamination in the data due to non-sampling errors on the false positive rate of the Kolmogorov-Smirnov (K-S) test in testing for normality of error terms. The study also characterized the effects of varying sample size and amount of contamination in the data on the false negative rate of the t-test in testing the significance of a regression coefficient. Lastly, an optimality index was developed to determine the sample sizes and the values of the percent noise at which both the false positive rate of the K-S test and the false negative rate of the t-test are minimized. [ABSTRACT FROM AUTHOR]

The analysis of COVID-19 data in Iraq is carried out. Data includes daily cases and deaths since the outbreak of the pandemic in Iraq on February 2020 until the 28th of June 2022. This is done by fitting some distributions to the data in order to find out the most appropriate distribution fit to both daily cases and deaths due to the COVID19 pandemic. The statistical analysis includes estimation of the parameters, the goodness of fit tests and illustrative probability plots. It was found that the generalized extreme value and the generalized Pareto distributions may provide a good fit for the data for both daily cases and deaths. However, they were rejected by the goodness of fit test statistics due to the high variability of the data [ABSTRACT FROM AUTHOR]

Variable selection is a well-studied problem in linear regression, but the existing works mostly deal with continuous responses. However, in many applications, we come across data with categorical responses. In the classical (frequentist) approach there exists penalized regression methods (e.g. logistic Lasso) which can be used for variable selection when we have a categorical response, and a large number of predictors. In this paper, we compare the performance of three alternative approaches for handling data with a single categorical response and multiple continuous (or count) predictors. In addition to the well-known logistic Lasso, we consider a model-based Bayesian approach, and a model-free approach for variable selection. We consider a binary response, and a response with three categories. Through extensive simulation studies we compare the performance of these three competing methods. We observe that the model-based methods can often accurately identify the important predictors, but sometimes fail to detect the unimportant ones. Also the model-based approaches are computationally expensive whereas the model-free approach is extremely fast. For misspecified models, the model-free method really outperforms in prediction. However, when the predictors are correlated (moderately or substantially) then the model-based methods perform better than the model-free method. We analyse the well-known Pima Indian Diabetes dataset for illustrating the effectiveness of three competing methods under consideration. [ABSTRACT FROM AUTHOR]

The coronavirus disease first identified in mid-December 2019 in Wuhan, China is an ongoing pandemic and the virus has spread around the world. As of 13 March 2020, the number of new cases started to increase significantly in Europe, and Europe was considered as the new center of the Covid-19 pandemic as announced by the WHO. Confirmed case rate (CCR), computed from the numbers of confirmed cases over numbers of tests of the countries can be used to confirm the quality of the numbers, and to detect the manipulation for health surveillance systems of the countries for managing the situation by testing -whether or notfollow Benford's Law (BL). The main aim of this study is to test CCRs of the countries in Europe by BL to detect the data qualities and to monitor the manipulations, which can help to take precautions for the health surveillance systems of the countries. [ABSTRACT FROM AUTHOR]

We propose kernel-type smoothed Kolmogorov-Smirnov and Cramér-von Mises tests for data on general interval, using bijective transformations. Though not as severe as in the kernel density estimation, utilizing naive kernel method directly to those particular tests will result in boundary problem as well. This happens mostly because the value of the naive kernel distribution function estimator is still larger than 0 (or less than 1) when it is evaluated at the boundary points. This situation can increase the errors of the tests especially the second-type error. In this article, we use bijective transformations to eliminate the boundary problem. Some numerical studies illustrating the estimator and the tests' performances will be presented in the last part of this article. [ABSTRACT FROM AUTHOR]

Recently, principal component analysis (PCA) and independent component analysis (ICA) have attracted increasing attention for fault detection in industrial processes. However, industrial processes usually have multimodal characteristics, in which case, the performance of traditional PCA and ICA methods is poor. A fault detection method based on the local entropy double subspace (LEDS) is proposed that effectively supervises the multimodal process with multiple steady modes. The proposed method uses the local probability density (LPD) to eliminate the multimodal characteristics of the data. Furthermore, local entropy (LE) is applied to mine the statistical information associated with the process data. The process variables are classified into Gaussian and non-Gaussian subspaces by the Kolmogorov–Smirnov (KS) test. PCA and ICA models are built in Gaussian and non-Gaussian subspaces, respectively, for fault detection. A Bayesian decision method is employed to transform the detection results into fault probabilities, and the detection results are combined with the final statistical information. This method effectively eliminates the multicentre structure of the data and improves the fault detection performance of traditional PCA and ICA. The proposed method is applied to the Tennessee Eastman multimodal process. The simulation results show that the fault detection rate of LEDS is better than the rates of traditional methods when the false alarm rate is low for most faults. [ABSTRACT FROM AUTHOR]

Dasometry is the branch of forest sciences that uses statistical methods to find solutions to problems related to the existence, growth and management of forests. When the Forest Law N° 29763 appears, decreed by the Peruvian State from the year 2011, the traceability or tracing of the trunk or the log coming from the forest began. At this stage, there is a noticeable difference between the census volume (standing tree) and the actual volume (tree in the storage yard), which led to penalties by the justice system. The objective of this article is to quantify this anomaly to demonstrate to forestry actors in both the private and public sectors the error that translates into costs. For this purpose, a random sample consisting of 196 trees declared by the Forestry Concessionaire Pucallpa Export SRL of Ucayali Peru and controlled by the forestry authority was taken. Dasometric calculation formulas were proposed both to find the census volume and the actual volume. The difference between one and the other was on average 2.92 m3 of wood per tree, therefore it is indicated that the morphic factor of 0.65 induces an unacceptable error so it is recommended to omit its application. To statistically justify the validity of the results, the normality of the data is checked and the t Student test is applied for paired samples, in order to verify that the difference between the two volumes is statistically significant. [ABSTRACT FROM AUTHOR]

Dividend pay-out policies often emanate from complex financial decisions and corporate managers do not consider dividend policies in isolation. However, some studies have indicated that capital structure significantly affects dividend pay-out and should be managed properly as it accounts for variations in dividends. The aim of this study was to investigate the relationship between capital structure and dividend pay-out. This study made use of a Kolmogorov-Smirnov and a Chi-square test. These tests are more reliable in investigating the variations and dependency between variables and are a leap from the models used in the prior literature. Using the R-square values of six published articles, the results revealed that capital structure cannot be used to explain variations in dividend pay-out as suggested by some authors. The implications of this study are that academic journals should scrutinise journal manuscripts where there are suggestions that there is a relationship between capital structure and dividend policy

This paper proposes a novel and reliable leak-detection method for pipeline systems based on acoustic emission (AE) signals. The proposed method analyzes signals from two AE sensors installed on the pipeline to detect leaks located between these two sensors. Firstly, the raw AE signals are preprocessed using empirical mode decomposition. The time difference of arrival (TDOA) is then extracted as a statistical feature of the two AE signals. The state of the pipeline (leakage/normal) is determined through comparing the statistical distribution of the TDOA of the current state with the prior normal state. Specifically, the two-sample Kolmogorov–Smirnov (K–S) test is applied to compare the statistical distribution of the TDOA feature for leak and non-leak scenarios. The K–S test statistic value in this context functions as a leakage indicator. A new criterion called leak sensitivity is introduced to evaluate and compare the performance of leak detection methods. Extensive experiments were conducted using an industrial pipeline system, and the results demonstrate the excellence of the proposed method in leak detection. Compared to traditional feature-based indicators, our approach achieves a significantly higher performance in leak detection.

Major purpose of this research is identification and evaluating of effective factors on implementation of Green Supply Chain Management (GSCM) at Fanavaran Petrochemical Company by using statistical methods of Kolmogorov-Smirnov, mean and decision making method by topic SWARA (Stepwise Weight Assessment Ratio Analysis). Research methodology of present research base on purpose is practical and based on data gathering method is descriptive-measurement. In order to extracting the effective factors on GSCM at the company, in first, by literature review, 22 factors were identified. Then data were gathered by using of opinions of population members containing 55 persons of experts and senior managers in the first class of company. Finally, after analyzing the questionnaires and statistical tests above, 11 factors were confirmed and selected. In continues, in order to evaluating the final factors and ranking them base on importance in success implementation of GSCM system, the SWARA technique is used. Final outcome of this technique showed that second factor as “Designing products to reduce energy and material consumption, reuse and recycling of materials, prevent the use of hazardous materials in the production process” by most weight is extracted as the most important factor. Such, the factors of “Materials and compliance with the standards required for the purchase of raw materials” and “Procurement, distribution and reverse logistics” placed in next ranks base on importance. The factor of “total environmental quality management” by least weight is identified as last factor in implementation of GSCM at the company. In the end of research, it is proposed that organizations focus on environmental problem to acquire skill green environment advantage through green supply chain activities.

Compression is essential to high bandwidth applications such as scientific simulations and sensing applications to reduce resource burden such as storage, network transmission, and more recently I/O. Existing lossy compression methods attempt to minimize the Euclidean distance between original data and reconstructed data, which significantly limits either compression performance or reconstruction quality since original and reconstructed data sequences should be aligned. Substituting the Euclidean distance for a statistical similarity maximizes the compression performance while retaining essential data features. By implementing this methodology, IDEALEM has recently demonstrated compression ratios far exceeding 100:1, better than best-known compression methods, while preserving reconstruction quality. This work proposes an online algorithm for streaming data compression which takes account of generally concave trend of compression ratio curve, and optimizes key operation parameters. We demonstrate that the proposed algorithm successfully adapts one of the key parameters in IDEALEM to the optimal value and yields near maximum compression ratios for time series data.

Estimates of extreme precipitation are commonly associated with different sources of uncertainty. One of the primary sources of uncertainty in the statistical modeling of precipitation extremes comes from extreme data series (i.e., sampling uncertainty). Therefore, this research aimed to quantify the sampling uncertainty in terms of confidence intervals. In addition, this article examined how the data record length affects predicted extreme precipitation estimates and data set statistics. A nonparametric bootstrap resample was utilized to quantify the precipitation quantile sampling distribution at a particular non exceedance probability. This sampling distribution can provide a point estimation of the precipitation quantile and the confidence interval at a particular non exceedance probability. It has been shown that the different types of probability distributions fit the extreme precipitation data series of various weather stations. Therefore, the uncertainty analysis should be conducted using the best-fit probability distribution for extreme precipitation data series rather than a predefined single probability distribution for all stations based on modern extreme value theory. According to the 95% confidence intervals, precipitation quantiles are subject to significant uncertainty and the band of the uncertainty intervals increases with the return period. These uncertainty bounds need to be integrated into any frequency analysis from historical data. The average, standard deviation, skewness and kurtosis are highly affected by the data record length. Thus, a longer record length is desirable to decrease the sampling uncertainty and, therefore, decrease the error in the predicted quantile values. Moreover, the results suggest that a series of at least 40 years of data records is needed to obtain reasonably accurate estimates of the distribution parameters and the precipitation quantiles for 100 years return periods and higher. Using only 20 to 25 years of data to obtain estimates of the higher return period quantile is risky, since it created high sampling variability relative to the full data length. [ABSTRACT FROM AUTHOR]