Anderson-Darling Research Articles

Exploring Changing-look Active Galactic Nuclei with the Sloan Digital Sky Survey V: First Year Results

“Changing-look” active galactic nuclei (CL-AGNs) challenge our basic ideas about the physics of accretion flows and circumnuclear gas around supermassive black holes. Using first-year Sloan Digital Sky Survey V (SDSS-V) repeated spectroscopy of nearly 29,000 previously known active galactic nuclei (AGNs), combined with dedicated follow-up spectroscopy, and publicly available optical light curves, we have identified 116 CL-AGNs where (at least) one broad emission line has essentially (dis-)appeared, as well as 88 other extremely variable systems. Our CL-AGN sample, with 107 newly identified cases, is the largest reported to date, and includes ∼0.4% of the AGNs reobserved in first-year SDSS-V operations. Among our CL-AGNs, 67% exhibit dimming while 33% exhibit brightening. Our sample probes extreme AGN spectral variability on months to decades timescales, including some cases of recurring transitions on surprisingly short timescales (≲2 months in the rest frame). We find that CL events are preferentially found in lower-Eddington-ratio (f Edd) systems: Our CL-AGNs have a f Edd distribution that significantly differs from that of a carefully constructed, redshift- and luminosity-matched control sample (Anderson–Darling test yielding p AD ≈ 6 × 10−5; median f Edd ≈ 0.025 versus 0.043). This preference for low f Edd strengthens previous findings of higher CL-AGN incidence at lower f Edd, found in smaller samples. Finally, we show that the broad Mg ii emission line in our CL-AGN sample tends to vary significantly less than the broad Hβ emission line. Our large CL-AGN sample demonstrates the advantages and challenges in using multi-epoch spectroscopy from large surveys to study extreme AGN variability and physics.

New Goodness-of-Fit Tests for the Kumaraswamy Distribution

The two-parameter distribution known as the Kumaraswamy distribution is a very flexible alternative to the beta distribution with the same (0,1) support. Originally proposed in the field of hydrology, it has subsequently received a good deal of positive attention in both the theoretical and applied statistics literatures. Interestingly, the problem of testing formally for the appropriateness of the Kumaraswamy distribution appears to have received little or no attention to date. To fill this gap, in this paper, we apply a “biased transformation” methodology to several standard goodness-of-fit tests based on the empirical distribution function. A simulation study reveals that these (modified) tests perform well in the context of the Kumaraswamy distribution, in terms of both their low size distortion and respectable power. In particular, the “biased transformation” Anderson–Darling test dominates the other tests that are considered.

A novel flexible exponent power-X family of distributions with applications to COVID-19 mortality rate in Mexico and Canada.

This paper aims to introduce a novel family of probability distributions by the well-known method of the T-X family of distributions. The proposed family is called a "Novel Generalized Exponent Power X Family" of distributions. A three-parameters special sub-model of the proposed method is derived and named a "Novel Generalized Exponent Power Weibull" distribution (NGEP-Wei for short). For the proposed family, some statistical properties are derived including the hazard rate function, moments, moment generating function, order statistics, residual life, and reverse residual life. The well-known method of estimation, the maximum likelihood estimation method is used for estimating the model parameters. Besides, a comprehensive Monte Carlo simulation study is conducted to assess the efficacy of this estimation method. Finally, the model selection criterion such as Akaike information criterion (AINC), the correct information criterion (CINC), the Bayesian information criterion (BINC), the Hannan-Quinn information criterion (HQINC), the Cramer-von-Misses (CRMI), and the ANDA (Anderson-Darling) are used for comparison purpose. The comparison of the NGEP-Wei with other rival distributions is made by Two COVID-19 data sets. In terms of performance, we show that the proposed method outperforms the other competing methods included in this study.

Analysis of Wideband spectrum sensing using Anderson darling test statistics in underlay cognitive microcell

The most widely used sensing technique for detecting energy is due to its ease of use and lack of need for signal information beforehand. Traditional methods often employ a two-threshold strategy in low signal-to-noise ratio scenarios, which can lead to temporary detection outcomes when the signal's energy is between the low and high threshold values. This subsequently results in inadequate detection accuracy, characterized by a reduced probability of detection, which ultimately results in an extended duration of the spectrum sensing. The paper suggests enhancing the efficiency of spectrum sensing through the utilization of a modified version of the Anderson-Darling test statistic, as opposed to other test statistics.To detect the presence of licensed users in each subchannel from the base station, the cognitive user implements the Anderson Darling test statistics as a sensing technique. The proposed work involves the mathematical derivation of the Anderson-Darling test statistics under a bandwidth of 6MHz.The simulation outcomes show that the sensing technique presented in this research study achieved a detection probability of 0.963 at a false alarm probability of 1%, using a small sample size of 20 at a signal-to-noise ratio of -20dB. This performance outperformed the other three detection algorithms.

An investigation of traffic speed distributions for uninterrupted flow at blackspot locations in a mixed traffic environment

Modelling traffic characteristics is the foundation for resolving various traffic and transportation issues. Among them, traffic speed has a significant impact on roadway crashes at blackspot (BS) locations. Speed is a random variable; several studies have recommended normal distribution to characterize the distribution of traffic speed for uninterrupted flow. However, a mixed-traffic situation causes heterogeneity, and the distribution of speeds deviates from the normal distribution. The present study investigates the distributions of traffic speeds for uninterrupted flow at 18 blackspot locations and individual vehicle types in mixed-traffic environments. Seven distribution models, namely Normal, Lognormal, Gamma, Logistic, Weibull, Burr, and Generalized Extreme Value (GEV), are considered to determine the speed characteristics. Different parametric distribution models are fitted to the vehicular speeds using maximum likelihood estimation (MLE) methods. Kolmogorov-Smirnov (KS), Anderson-Darling (AD), and two penalized criteria, i.e., Akaike and Bayesian Information Criteria (AIC and BIC), are used as goodness-of-fit (GoF) measures to find the best-fitting distribution. The overall suitability of each predicted distribution is also determined using a novel ranking method. The test findings suggest that GEV and Burr are the most suitable empirical speed distributions, with GEV fitting best above 96%. When the heavy vehicle composition (truck, bus, and tractor) is below 10%, 10–14%, 15–20%, and above 20%, it follows the Weibull, Gamma, GEV, and Burr distributions, respectively, in a mixed traffic environment.

Developing Rainfall Intensity- Frequency- Duration Curves for Three Selected Sites in the Kurdistan Region, Iraq

Developing Rainfall Intensity- Frequency- Duration Curves for Three Selected Sites in the Kurdistan Region, Iraq

A new unit distribution: properties, estimation, and regression analysis

This research commences a unit statistical model named power new power function distribution, exhibiting a thorough analysis of its complementary properties. We investigate the advantages of the new model, and some fundamental distributional properties are derived. The study aims to improve insight and application by presenting quantitative and qualitative perceptions. To estimate the three unknown parameters of the model, we carefully examine various methods: the maximum likelihood, least squares, weighted least squares, Anderson–Darling, and Cramér-von Mises. Through a Monte Carlo simulation experiment, we quantitatively evaluate the effectiveness of these estimation methods, extending a robust evaluation framework. A unique part of this research lies in developing a novel regressive analysis based on the proposed distribution. The application of this analysis reveals new viewpoints and improves the benefit of the model in practical situations. As the emphasis of the study is primarily on practical applications, the viability of the proposed model is assessed through the analysis of real datasets sourced from diverse fields.

Lifetime Analysis of Dies Manufactured by Conventional Processes and Reconditioned by Deposition Welding Operation.

The refurbishment of dies by the deposition welding of wear areas is an efficient and economical process. The aim of this study was to conduct a comparative analysis of the lifetimes of different types of dies for the manufacturing of wagon wheels. The analyzed dies were manufactured by conventional processes (Type I) and reconditioned through a deposition welding procedure using a dedicated electrode (Type II). The Anderson-Darling test was conducted to analyze the goodness of fit of the lifetime data specific to the die types. The maximum likelihood estimation method (MLE) with a 95% confidence interval (CI) was applied in order to estimate the lifetime distribution parameters. It was found that the lifetimes of type II dies were longer than those of type I dies. The mean time to failure (MTTF) recorded for reconditioned dies was 426 min, while the mean time to failure of dies manufactured by conventional processes was approximatively 253 min. In addition, an accentuated hazard rate for type I dies compared to type II dies was observed. The results of this analysis emphasized the fact that dies can be restored to their initial operating capacity by successfully using deposition welding procedures that confer a high resistance to operational loads. At the same time, the use of these procedures allows for the sustainable development of resources and waste management.

A new flexible distribution: Statistical inference with application

A flexible distribution for the analysis of bounded data is proposed. The new model is generalized through the transmuted approach, and the resultant model is named “transmuted modified Lehmann-type II distribution.” A comprehensive analysis of key characteristics is performed, including the shape of the model, survival, and hazard function, analytical expressions of mode, quantile function, ordinary moments, quantile function, and stress–strength reliability. Some famous entropy measures are also derived. The model parameters have been estimated using four distinct methods, including maximum likelihood estimation, Anderson Darling, Cramer–von Misses, and ordinary least squares. A detailed simulation study is used to evaluate the behavior of all derived estimators. Finally, a dataset was used to demonstrate the utility of the proposed distribution.

Statistical Analysis and Modeling of the CO2 Series Emitted by Thirty European Countries

In recent decades, an increase in the earth’s atmospheric temperature has been noticed due to the augmentation of the volume of gases with the greenhouse effect (GHG) released into the atmosphere. To reduce this effect, the European Union’s directives indicate the action directions for reducing these emissions, among which carbon dioxide (CO2) recorded the highest amount. In this context, the article analyzes the CO2 series reported in 1990–2021 by 30 European countries. The Kruskal-Wallis test rejected the hypothesis that the series comes from the same underlying distribution. The Anderson-Darling test rejected the normality hypothesis for seven series out of thirty, and Sen’s procedure found a decreasing trend slope only for 17 series. ARIMA models have been built for all individual series. Grouping the series (by the k-means and hierarchical clustering) provided the base for building the Regional series (RegS), which describes the CO2 pollution evolution over Europe. The advantage of this approach is to provide the synthetic image of the regional evolution of the CO2 emission volume (mt), incorporating information from 30 series (one for each country) in only one—RegS. It is also shown that selecting the number of clusters involved in building RegS and assessing their stability is essential for the model’s goodness of fit.

Application of the Non-Parametric Signals Test to a Company

Objective: The aim of this paper is to show the application of the Non-Parametric Sign Test in problems involving the testing of central tendency values. Theoretical framework: Non-parametric methods are widely used in the study of populations that are taken in rank order (such as a movie that receives one to four star ratings). The use of non-parametric methods may also be necessary when the data has a ranking but no clear numerical interpretation, such as when accessing preferences. In terms of scale, non-parametric methods result in data that is "in order" (Thatcher et al., 2005) Method: Data was taken from a company in the South of Rio de Janeiro and a case study was carried out using the Non-Parametric Sign Test. Final Considerations: Initially it proved unfeasible to use a Parametric Test because the Anderson-Darling Test showed that the Assumption of Normality was not confirmed and finally the Non-Parametric Sign Test showed that the hypothetical Median really is the correct measure of Central Tendency. Implications of the research: The use of Non-Parametric Tests is widespread in scientific literature and has proven to be highly effective in dealing with data where the assumptions of Normality are not confirmed. Originality/value: Despite being well-known statistical tools, Non-Parametric Tests are widely used and can bring innovations to their application, as in the case of the company in question.

Tests of fit for the power function lognormal distribution.

In this study, tests of fit for the power function lognormal distribution is considered. The probability plot, probability plot correlation coefficient, and goodness-of-fit tests-the Kolmogorov-Smirnov (KS), Cramér-von Mises (CvM), and Anderson-Darling (AD) tests are provided. Tables of critical values are presented by using simulation techniques, and the AD test outperforms KS and CvM tests based on power comparisons. Finally, to illustrate these test procedures, we fit this distribution to the data which represent the survival times of 121 breast cancer patients from one hospital.

Statistical analysis of air quality dataset of Kathmandu, Nepal, with a New Extended Kumaraswamy Exponential Distribution.

This paper aims to create a new probability distribution and conducts statistical analysis on air quality dataset from Kathmandu. Using this innovative distribution, we have studied the ground reality of air quality conditions of Kathmandu, Nepal. In our research, we have developed a new probability distribution known as the New Extended Kumaraswamy Exponential Distribution by introducing an additional shape parameter to the Extended Kumaraswamy Exponential (EKwE) Distribution. Statistical characteristics such as cumulative distribution function, probability density function, hazard function, reversed hazard function, skewness, kurtosis, survival function, and hazard rate function are studied. The suggested model is non-normal and positively skewed with increasing and inverted bathtub-shaped hazard rate curves. To assess the model's suitability, we utilized a real dataset comprising air quality data from Kathmandu, Nepal, during the year 2021. Study shows that the air quality data exhibit an increasing failure rate, but the P2.5, P10, and total suspended particle concentrations exhibited its lowest levels during the monsoon season and its highest levels during the winter season. Parameters of the model are estimated by using the least square estimation (LSE), maximum likelihood estimation (MLE), and Cramér-von Mises (CVM) approach for P10 at Ratnapark Station, Kathmandu. To assess the model's validity, P-P plots and Q-Q plots are employed. Model comparisons are carried out using Akaike Information Criterion (AIC), Corrected Akaike Information Criterion (CAIC), Bayesian Information Criterion (BIC), and Hannan-Quinn Information Criterion (HQIC). Furthermore, the goodness of fit of the proposed model is evaluated using test statistics such as Anderson-Darling (A2) test, Cramér-von Mises (CVM) test, and the Kolmogorov-Smirnov (KS) test along with their respective p-values. From the findings, we have found that the air quality status of Kathmandu, Nepal, was found to be poor. Proposed distribution provides a better fit with greater flexibility for forecasting air quality data and conducting reliability data analyses. Dataset is analyzed and visualized using R programming.

Identifying the Probability Distribution of Exchange Rates in Sri Lanka

The exchange rate is one of the main components that represents the status of a country’s economic condition. This study mainly focuses on identifying an appropriate distribution that can capture the asymmetric behaviour of important currency exchange rates related to Sri Lanka. The true behaviour of the exchange rates of USD, EURO, JPY, AUD, GBP, CHF, SGD and CAD in terms of LKR was determined using flexible distributions of generalised lambda distribution (GLD), Normal Inverse Gaussian and Skew-Normal. The parameter estimation was carried out by maximum likelihood estimation (MLE), while for GLD, few more parameter estimation techniques were applied. To understand how well the fitted distributions identify the real behaviour of data, we conducted the goodness-of-fit (GOF) tests. The results from Cramer–Von Mises (CvM) and Anderson–Darling (AD) GOF tests pointed out that the exchange rates data follow a GLD with the parameter estimation of maximum product of spacings (MPS). Further, plots of histograms with theoretical densities, cumulative distribution functions (CDFs) with empirical CDF and Quantile–Quantile plots illustrated the same idea. This study presented the findings based on different suitable parameter estimation techniques and this study is the first investigation on the distribution identification of exchange rates in Sri Lanka. JEL Codes: C12,C13,G10

An exponentiated XLindley distribution with properties, inference and applications

In this paper, we propose exponentiated XLindley (EXL) distribution. The novel model is adaptable due to the mixt shapes of its density and failure rate functions. The following key statistical properties of EXL distribution are derived: quantile function, moments, hazard function, mean residual life, and Rényi entropy. The parameters are estimated using the maximum likelihood, Anderson Darling, Cramer von Misses, maximum product spacing, ordinary and weighted least square estimation procedures. To examine the behavior of the estimate, Monte Carlo simulation is used. Further Bayesian technique is also utilized to estimate the EXL parameters. The traceplot and Geweke diagnostics are used to track the convergence of simulated processes. The applicability of the EXL distribution is demonstrated by three datasets from different domains such as mortality rate due to COVID-19, precipitation in inches, and failure time for repairable items. The proposed distribution provides efficient results as compared to renowned competitive distributions.

Distribution of Floods Frequency of Manafwa River, Uganda

The objective of this study was to analyse Manafwa River flood frequency in Eastern Uganda. Analysis of Manafwa River maximum annual flows from 1949-2015 was undertaken using Log Pearson 3 distribution in comparison with Gumbel, Normal and Log Normal distributions to determine frequency of occurrence and magnitude of extreme floods. Statistical analysis including goodness of fit tests of chi-square, Kolmogorov-Smirnov and Anderson-Darling tests were used to generate the most suitable probability distribution model. The results show quantile magnitudes lowest for Log Normal distribution at 43.59 m3/s and highest for Log Pearson 3 distribution at 51.67 m3/s. The 5-year quantile estimates are highest for Normal and Log Pearson at 70.37 m3/s and 63.99 m3/s respectively. The 10-year quantile estimates are highest for Log Normal and lowest for Log Pearson 3 distributions at 87.57 m3/s and 75.13 m3/s respectively. The 100-year quantile estimates are lowest for Normal and highest for Log Normal distributions at 108.57 m3/s and 154.66 m3/s respectively. The 200-year quantile estimates are lowest for Normal and highest for Log Normal distributions respectively at 114.980 m3/s and 177.16 m3/s respectively. Log Pearson 3 distribution emerged as best fit for data. From the statistical analysis, LP 3 probability distribution presents the most accurate regression coefficient at 0.8486 and the most suitable distribution of goodness of best fit using A-D, K-S and Chi square tests followed by the Gumbel distribution. The tests yield 0.15666, 0.04855 and 0.88502 for A-D, K-S and Chi square tests respectively for the LP 3 distribution. There is an increasing upward trend of the discharges at Manafwa River floodplains at higher probabilities of exceedance across all the probability distributions due to varrying climatic changes and rapid landuse changes in the Manafwa catchment. Manafwa river floodplains have the capacity to accommodate and boost crop production and productivity. Any nutrients lost to leaching could be gained from subsequent fallowing and sustainable soil fertility management including; proper drainage, crop rotation, adding organic manure, cover cropping and among others

Manual Therapy with Neural Mobilization: Immediate Effect on the Vocal Quality of Women with Dysphonia.

To evaluate the immediate effect of neural mobilization on the voice quality, self-perceived phonatory effort, and laryngeal muscles of women with behavioral dysphonia. This is an intrasubject comparative study. The research included 21 women aged 18 to 59 years with vocal complaints. Therefore, the selection of this sample excluded the lower limit of the voice change period and the upper limit of presbyphonia. The participants were assessed by voice acoustic and auditory-perceptual analysis, self-reported vocal effort, and laryngeal palpation performed at three moments: at baseline, after 10 minutes of vocal resting, and after manual therapy. The participants were divided into two groups: the group with 10 minutes of vocal resting (G1) and the group with intervention (G2). The patients in the intervention group underwent manual therapy using neural mobilization in the laryngeal region. For the statistical analysis, a descriptive analysis of the data was performed first with measures of central tendency and dispersion. Subsequently, the Anderson-Darling test was used to verify sample normality. To analyze the difference between three groups were used the parametric One-Way ANOVA or the non-parametric Friedman's test. The McNemar's or chi-squared tests were used to compare categorical variables and to compare an ordinal variable a non-parametric Wilcoxon test was used. The Gwet's AC1 test was used to assess intra-rater agreement in the auditory-perceptual analysis response. Neural mobilization in the laryngeal region showed no positive effects on the acoustic voice parameters and voice quality of women with dysphonia. Phonatory effort improved after neural mobilization in the laryngeal region (p=0.004). There was no significant change in supralaryngeal resistance, lateral laryngeal resistance, and laryngeal position after neural mobilization in the laryngeal region. Neural mobilization improved phonatory comfort but did have any effect on the voice quality and laryngeal musculature of women with dysphonia.

A Bounded Lifetime Distribution Specified by a Trigonometric Function: Properties, Regression Model, and Applications

Trigonometric functions have gained considerable attention in recent studies for developing new lifetime distributions. This is due to their parsimonious framework, mathematical tractability, and flexibility of the newly developed distributions. In this paper, the Sine‐generated family is used to create a new bounded lifetime distribution, known as Sine‐Marshall–Olkin Topp–Leone distribution, for modeling data defined on the unit interval. Some statistical properties of the new bounded distribution, including quantile function, ordinary moments, incomplete moments, moment‐generating functions, inequality measures, Rényi entropy, and probability weighted moments are derived. Seven methods of parameter estimation, including maximum likelihood, ordinary least squares, weighted least squares, moment product spacing, Anderson–Darling, and Cramér–von Mises estimators are used to estimate the parameters of the new distribution. The behavior of the estimators obtained from the estimation methods are investigated using Monte Carlo simulation studies. The results show that the estimation methods are asymptotically efficient and consistent. The flexibility of the new bounded distribution is examined via data fitting using two proportional datasets. The results of the fittings show that the new bounded distribution performs significantly better than the competing bounded lifetime distributions. Finally, Sine‐Marshall–Olkin Topp–Leone regression model is presented as an alternative to the beta and Kumaraswamy regression models.

Digital Videokymography: Analysis of Glottal Closure in Adults.

High-speed videolaryngoscopy and quantitative analysis of laryngeal images are relevant in accurately diagnosing vocal fold closure patterns. To analyze the parameters of digital videokymography obtained through high-speed videolaryngoscopy in women and men with complete and incomplete glottal closure, and posterior glottal chink. We conducted an observational, analytical, cross-sectional study with data from 65 adults, which we divided into groups according to sex and glottal closure. Digital videokymography parameters were analyzed using an image-processing program. The Anderson-Darling and Mann-Whitney U tests were used to verify sample normality and compare videokymography parameters between groups, respectively. The significance level was set at 5%. Among 65 laryngeal images, 20 each were from women with complete and incomplete glottal closure, and 20 and 5 were from men with complete and incomplete glottal closure, respectively. Considering the clinical relevance of the evaluated data, groups of 11 women and 4 men with posterior glottal chink were compared with sex-similar groups with complete glottal closure. Digital videokymography showed a lower maximum and mean vocal fold opening in women with incomplete glottal closure, and a lower dominant left vocal fold-opening amplitude and higher dominant frequency of bilateral vocal fold opening in men with incomplete glottal closure. It also showed a lower closed phase percentage in the posterior region for women and men, with higher closed phase percentage in the anterior and middle regions in women. Both groups with posterior glottal chink showed similar results. Incomplete glottal closure may interfere with the results of the digital videokymography parameters, with higher impact on the posterior vocal fold region in males and the middle and anterior vocal fold regions in females.

The Sloan Digital Sky Survey Reverberation Mapping Project: Investigation of Continuum Lag Dependence on Broad-line Contamination and Quasar Properties

This work studies the relationship between accretion-disk size and quasar properties, using a sample of 95 quasars from the Sloan Digital Sky Survey Reverberation Mapping Project with measured lags between the g and i photometric bands. Our sample includes disk lags that are both longer and shorter than predicted by the Shakura and Sunyaev model, requiring explanations that satisfy both cases. Although our quasars each have one lag measurement, we explore the wavelength-dependent effects of diffuse broad-line region (BLR) contamination through our sample’s broad redshift range, 0.1 < z < 1.2. We do not find significant evidence of variable diffuse Fe ii and Balmer nebular emission in the rms spectra, nor from Anderson–Darling tests of quasars in redshift ranges with and without diffuse nebular emission falling in the observed-frame filters. Contrary to previous work, we do not detect a significant correlation between the measured continuum and BLR lags in our luminous quasar sample, similarly suggesting that our continuum lags are not dominated by diffuse nebular emission. Similar to other studies, we find that quasars with larger-than-expected continuum lags have lower 3000 Å luminosities, and we additionally find longer continuum lags with lower X-ray luminosities and black hole masses. Our lack of evidence for diffuse BLR contribution to the lags indicates that the anticorrelation between continuum lag and luminosity is not likely to be due to the Baldwin effect. Instead, these anticorrelations favor models in which the continuum lag increases in lower-luminosity active galactic nuclei, including scenarios featuring magnetic coupling between the accretion disk and X-ray corona, and/or ripples or rims in the disk.