Two-sample Kolmogorov-Smirnov Test Research Articles

Adaptive hybrid prediction model for adapting to data distribution shifts in machining quality prediction

Abstract The accuracy of data-driven intelligent prediction for machining quality relies on the training samples. However, in actual applications, the continuous operation of machining equipment leads to gradual distribution shifts between the process data and the training samples for modeling. The shifts result in a degradation in the performance of predictive model, previous studies have often overlooked this issue. To tackle with the intricate problem, this research proposes a real-time model optimization approach. Firstly, a method for detecting machining data distribution shifts based on the two-sample Kolmogorov–Smirnov test is proposed. Then, an adaptive hybrid prediction model (AHPM) capable of real-time optimization is developed. This model consists of a deep neural network (DNN) and a broad learning system (BLS). DNN plays a primary role in prediction within the hybrid model with excellent generalization capability. BLS quickly completes optimization prior to DNN with its unique parameter update mechanism to compensate for prediction loss. Experimental results indicate that AHPM achieves the shortest optimization time while maintaining high accuracy, with post-optimization error reduction rates for mean squared error, mean absolute error, and mean absolute percentage error all exceeding 10%. In the test of application to actual machining cases, accuracy improved by 8.88% compared to traditional methods without optimization.

Reducing uncertainty of building shape information in urban building energy modeling using Bayesian calibration

This study proposes urban building energy modeling that extends beyond single-building-level models to the urban level. However, most urban building energy models use representative buildings that may not accurately reflect the diversity of building shapes, systems, and envelope performance when conducting building energy evaluations at the urban scale. To address this issue, previous studies have utilized representative buildings and Bayesian calibration to estimate uncertain building information parameters without considering building shape information. Therefore, the primary objective of this study is to estimate building shape information using artificial neural networks and Bayesian calibration based on building energy consumption data to identify the shape information uncertainty of representative buildings. The results indicate that some shape information can be estimated by comparing the overall distribution of the building stock using the two-sample Kolmogorov–Smirnov test. Furthermore, we found that the proposed energy modeling methodology yields energy consumption patterns similar to those of the target building stock. This preliminary investigation addresses the uncertainty of representative buildings in urban-scale modeling, elucidates the relationship between building form and energy consumption, and introduces a method for inferring shape information from energy consumption data.

Evolution of rift faulting in incipient, magma-poor divergent plate boundaries: New insights from the Okavango-Makgadikgadi Rift Zone, Botswana

Continental rifts are thought to transition from a power-law fault length distribution during the juvenile stages of extension, to an exponential distribution during break-up and oceanic spreading. However, fault scaling relationships have rarely been quantified in natural incipient rifts, particularly in the absence of magmatic influence. We address this knowledge-gap in the incipient Okavango-Makgadikgadi Rift Zone, Northern Botswana, consisting of the amagmatic Okavango Rift Basin (ORB) and Makgadikgadi Rift Basin (MRB). We utilize high-resolution satellite topography data (12.5 m TanDEM-X and 30 m SRTM) and aeromagnetic data to image and map faults across the rift zones and generate a new robust fault map for the region. Further, we analyze the length-frequency distribution of faults using a two-sample Kolmogorov-Smirnov test. The results show that the Makgadikgadi Rift Basin exhibits an exponential distribution associated with a nucleating rift as it exhibits diffuse faulting and lacks a border-fault, whereas the Okavango Rift Basin exhibits a power-law distribution that is consistent with its relatively more evolved rift structure with established border faults. Thus, we propose that continental divergent plate boundaries commonly nucleate with an initial exponential distribution of fault lengths which subsequently transition into a power-law distribution as the rift evolves into its stretching phase, and may again transition into an exponential distribution as break-up initializes.

Variations in the Radius Distribution of Single- and Compact Multiple-transiting Planets

Previous work has established the enhanced occurrence of compact systems of multiple small exoplanets around metal-poor stars. Understanding the origin of this effect in the planet formation process is a topic of ongoing research. Here we consider the radii of planets residing in systems of multiple transiting planets, compared to those residing in single-transiting systems, with a particular focus on late-type host stars. We investigate whether the two radius distributions are consistent with being drawn from the same underlying planetary population. We construct a planetary sample of 290 planets around late K and M dwarfs containing 149 planets from single-transiting planetary systems and 141 planets from multi-transiting compact multiple planetary systems (54 compact multiples). We performed a two-sample Kolmogorov–Smirnov test, Mann–Whitney U test, and Anderson–Darling k-sampling test on the radius distributions of our two samples. We find statistical evidence (p < 0.0026) that planets in compact multiple systems are larger, on average, than their single-transiting counterparts for planets with R p < 6 R ⊕. We determine that the offset cannot be explained by detection bias. We investigate whether this effect could be explained via more efficient outgassing of a secondary atmosphere in compact multiple systems due to the stress and strain forces of interplanetary tides on planetary interiors. We find that this effect is insufficient to explain our observations without significant enrichment in H2O compared to Earth-like bulk composition.

Examining the Effect of Birth Order on Neuroticism and Extraversion among Adolescents and Young Adults

This study investigates the influence of birth order on personality traits, specifically Neuroticism and Extraversion, among adolescents and young adults. A total of 120 participants aged between 14-25 years completed a survey distribution via Google Forms. The sample was evenly split into two groups: Elder children (First-born) and Younger children (Last-born), each comprising 60 individuals. The personality traits were assessed using Neuroticism and extraversion subscales of the Neo-Five Factor Inventory (NEO-FFI, Costa &amp; McCrae, 1992). Data analysis involved the use of a two-sample Kolmogorov-Smirnov (K-S) test to compare the distribution of Neuroticism and Extraversion scores between the two birth groups. The results indicate that there is no statistically significant difference in the distribution of Neuroticism and Extraversion traits between elder and younger children. These findings contribute to understanding how birth order is not the sole influence on neuroticism and extraversion among adolescents and young adults.

Barriers to people's participation in watershed intervention projects: perspectives of experts and local communities

ABSTRACT The present study aimed to identify and prioritize people's participation barriers (PPBs) in watershed intervention projects (WIPs), considering the perspectives of both experts and local people in the Dastgerd watershed, Iran. Thirteen important PPBs in the implementation of WIPs were identified in the study. The findings indicated that a lack of participatory guidelines, an expert-oriented decision-making process, and a deficiency in economic incentives during the implementation of WIPs constituted the most crucial PPBs. In addition, according to the two-sample Kolmogorov–Smirnov test, there is a significant difference in the perspectives of the two groups regarding the importance of 60% of PPBs. The biggest differences were observed concerning the two barriers of ‘ignoring people's benefit from the project’ and ‘lack of people consultation and attention to their suggestions’. Given the substantial impact of expert opinions on policy-making in watersheds, any misconceptions they hold regarding barriers and incentives for people's participation can lead to adverse outcomes in the planning of soil and water conservation projects. To address this issue, the establishment of think tanks and the facilitation of brainstorming sessions play a pivotal role in fostering mutual understanding, aligning the perspectives of local people and experts, and making informed decisions to overcome challenges.

Enhanced drift self-calibration of low-cost sensor networks based on cluster and advanced statistical tools

The escalating use of low-cost sensors in environmental monitoring demands effective management of sensor drift and errors. To tackle this challenge, lightweight calibration techniques are essential for accurate sensor readings. This paper presents a novel calibration method for low-cost sensors, prioritizing the detection and correction of sensor drifts. The approach incorporates clustering for efficient task distribution and leverages Inverse Distance Weighting (IDW) for calibration precision. Also, advanced statistical tools, including the Two-Sample Kolmogorov–Smirnov test (TSKS test) and Exponential Moving Average (EMA), assess and enhance sensor data stability based on historical measurements. Additionally, the Root Update Estimator (RUE) is utilized to adjust predicted values and improve the model’s adaptability to changes in the underlying data distribution. Extensive simulation experiments using an Intel Berkeley Research Laboratory (IBRL) dataset affirm the method’s effectiveness in swiftly addressing sensor drifts. These findings advance low-cost sensor calibration, boosting reliability and precision in environmental monitoring.

Assessing reliability of naïve respondent-driven sampling samples by using repeated surveys among people who inject drugs (PWID) in New Jersey

IntroductionRespondent-driven sampling (RDS) is widely used to sample populations with higher risk of HIV infection for whom no sampling frames exist. However, few studies have been done to assess the reliability of RDS in real world settings. MethodsWe assessed the reliability of naïve RDS samples using five rounds of the National HIV Behavioral Surveillance – People Who Inject Drugs surveys in Newark, New Jersey from 2005 to 2018. Specifically, we compared the distributions of time-insensitive demographic characteristics in temporally adjacent RDS samples with Monte Carlo Two-Sample Kolmogorov-Smirnov Test with 100,000 replicates. The distributions of time-sensitive demographic characteristics were also compared as sensitivity analyses. ResultsThe study showed that repeated RDS samples among people who inject drugs in the greater Newark area, New Jersey were reliable in most of time-insensitive demographics and recruitment homophily statistics. Sensitivity analyses of time-sensitive demographics also presented consistencies in most of temporally adjacent samples. ConclusionsIn conclusion, RDS has the potential to provide reliable samples, but demographic characteristics of RDS samples may be easily biased by homophily. Future studies using RDS may need to pay more attention to potential homophily bias and consider necessary diagnostic procedures and sample adjustments.

Traffic Circle—An Example of Sustainable Home Zone Design

A significant number of new metered parking systems have been introduced in recent years by the local authorities of various spa towns in Poland in connection with home zone conversion projects. The traffic signs posted in these locations were limited to the beginning and end of the demarcated parking area. Traffic circle (TC) is an example of a traffic calming measure (TCM) used in home zones to slow down the traffic (case study—home zone in a small spa village). This article presents the results of a study investigating the speed reductions obtained within a home zone and a traffic circle used as traffic calming measure. The indispensable speed surveys were carried out in relation to this study in two periods: in summer when the streets are crowded with tourists and in September with little pedestrian traffic. Two research hypotheses were formulated as part of the speed data analysis to verify the slowing effect of the traffic circle and the relevance of the traffic circle’s design parameters and location, road function and the surrounding streetscape. For each hypothesis, statistical analyses were carried out using two nonparametric tests: two-sample Kolmogorov–Smirnov test and median test. The third research hypothesis formulated in this study was related to sustainable development factors related to fuel consumption and traffic-related air pollution, including carbon dioxide, carbon monoxide, nitrogen oxide and hydrocarbons. This hypothesis was verified by estimating the amount of air pollution in the home zone under analysis in three different situations (scenarios): in summer with the travel speed reduced by pedestrian traffic to ca. 8–10 km/h, in September with a small number of pedestrians and 20–25 km/h resulting speed between traffic circles, reduced at the traffic circle, and in a theoretical 30 km/h zone with 25–30 km/h assumed speed between traffic circles, dropping at the traffic circle. These analyses confirmed the appropriateness of the traffic circle as a home zone traffic calming measure, as long as its design is based on a detailed analysis of the relevant factors, including location, road function and the surrounding streetscape.

Determination of mathematical expectations of two weighting statistics for ensemble time scales

The Bureau International des Poids et Mesures uses the ALGOS algorithm to generate the Echelle Atomique Libre. The original and new ALGOS weighting algorithms employ two different statistics to calculate the weights, resulting in different experimental phenomena. These two statistics are believed to be the keys to analyzing these experimental phenomena, because the statistical characteristics (e.g. the mathematical expectations and variances) of the statistics completely determine the statistical characteristics of the weights, and subsequently the experimental phenomena. The core work conducted in this study is to derive the analytical expressions of the mathematical expectations of these two statistics under ideal conditions and to validate them empirically. This paper provides a theoretical analysis of these expressions and discusses the relationships between them and the mathematical expectations of the Allan variances. These expressions theoretically reveal the reasons behind the experimental phenomena, especially why hydrogen masers are assigned lower weights in the original ALGOS weighting algorithm but higher weights in the new one. According to the derivation process, these analytical expressions are universally applicable and can also be applied to time-keeping laboratories. In the future, this work can be extended to determine the theoretical statistic and weight performances of different types of frequency standards quantitatively for time-keeping laboratories under both ideal and non-ideal conditions, providing a theoretical basis for improving the design of ensemble time scale algorithms. In the , a method of obtaining the experimental variances and probability density functions (PDFs) of these two statistics is additionally proposed. The two-sample Kolmogorov–Smirnov test is presented to compare the experimental and theoretical PDFs of an example.

Exercising prediction validation methods for analysis of quiet supersonic overflight

En route noise certification procedures for quiet supersonic aircraft are in development under the International Civil Aviation Organization for a proposed standard that could enable civil supersonic overland flight. While these quiet supersonic aircraft do not exist yet, methods are being developed and evaluated using existing N-wave empirical datasets and low-boom simulation datasets. One potential aspect of future certification methods is the ability to use a validated prediction model to predict the certification noise level under reference day weather conditions. Methods to validate predictions, such as the two-sample t-test, Fisher's combined probability test, two-sample Kolmogorov–Smirnov test, and equivalence testing, have been exercised and modified for this application. While some methods compare mean values, others compare values across the distribution, and a combination of methods can be used. The methods were applied to a subset of an N-wave dataset to demonstrate the applicability and identify potential areas for improvement. Atmospheric turbulence variability was included as a factor in these analyses through KZKFourier propagation simulations with parameters matching actual flights and weather conditions. Results are presented for six noise metrics (PL, ASEL, BSEL, DSEL, ESEL, and ISBAP), and plans to expand the analyses are discussed.

Influence of gadolinium, field-strength and sequence type on quantified perfusion values in phase-resolved functional lung MRI.

The purpose of this study is to evaluate the influences of gadolinium-based contrast agents, field-strength and different sequences on perfusion quantification in Phase-Resolved Functional Lung (PREFUL) MRI. Four cohorts of different subjects were imaged to analyze influences on the quantified perfusion maps: 1) at baseline and after 2 weeks to obtain the reproducibility (26 COPD patients), 2) before and after the administration of gadobutrol (11 COPD, 2 PAH and 1 asthma), 3) at 1.5T and 3T (12 healthy, 4 CF), and 4) with different acquisition sequences spoiled gradient echo (SPGR) and balanced steady-state free precession (bSSFP) (11 COPD, 7 healthy). Wilcoxon-signed rank test, Bland-Altman plots, voxelwise Pearson correlations, normalized histogram analyses with skewness and kurtosis and two-sample Kolmogorov-Smirnov tests were performed. P value ≤ 0.05 was considered statistically significant. In all cohorts, linear correlations of the perfusion values were significant with correlation coefficients of at least 0.7 considering the entire lung (P<0.01). The reproducibility cohort revealed stable results with a similar distribution. In the gadolinium cohort, the quantified perfusion increased significantly (P<0.01), and no significant change was detected in the histogram analysis. In the field-strength cohort, no significant change of the quantified perfusion was shown, but a significant increase of skewness and kurtosis at 3T (P = 0.01). In the sequence cohort, the quantified perfusion decreased significantly in the bSSFP sequence (P<0.01) together with a significant decrease of skewness and kurtosis (P = 0.02). The field-strength and sequence cohorts had differing probability distribution in the two-sample Kolmogorov-Smirnov tests. We observed a high susceptibility of perfusion quantification to gadolinium, field-strength or MRI sequence leading to distortion and deviation of the perfusion values. Future multicenter studies should strictly adhere to the identical study protocols to generate comparable results.

Patterns of engagement in a digital mental health service during COVID-19: a cohort study for children and young people.

The COVID-19 pandemic increased public use of digital mental health technologies. However, little is known about changes in user engagement with these platforms during the pandemic. This study aims to assess engagement changes with a digital mental healthcare service during COVID-19. A cohort study based on routinely collected service usage data from a digital mental health support service in the United Kingdom. Returning users aged 14-25 years that interacted for a maximum of two months were included. The study population was divided into pre-COVID and COVID cohorts. Demographic and usage information between cohorts were compared and usage clusters were identified within each cohort. Differences were tested using Chi-squared, two-sample Kolmogorov-Smirnov tests and logit regressions. Of the 624,103 users who joined the service between May 1, 2019, and October 1, 2021, 18,889 (32.81%) met the inclusion criteria: 5,048 in the pre-COVID cohort and 13,841 in the COVID cohort. The COVID cohort wrote more journals; maintained the same focus on messaging practitioners, posting discussions, commenting on posts, and having booked chats; and engaged less in writing journals, setting personal goals, posting articles, and having ad-hoc chats. Four usage profiles were identified in both cohorts: one relatively disengaged, one focused on contacting practitioners through chats/messages, and two broadly interested in writing discussions and comments within the digital community. Despite their broad similarities, usage patterns also exhibited differences between cohorts. For example, all four clusters had over 70% of users attempting to have ad-hoc chats with practitioners in the pre-COVID cohort, compared to one out of four clusters in the COVID cohort. Overall, engagement change patterns during the COVID-19 pandemic were not equal across clusters. Sensitivity analysis revealed varying strength of these defined clusters. Our study identified changes in user activity and engagement behavior within a digital mental healthcare service during the COVID-19 pandemic. These findings suggest that usage patterns within digital mental health services may be susceptible to change in response to external events such as a pandemic. Continuous monitoring of engagement patterns is important for informed design and personalized interventions.

A Markov Model of Gap Occurrence in Continuous Glucose Monitoring Data for Realistic in Silico Clinical Trials

Background and objectiveContinuous glucose monitoring (CGM) sensors measure interstitial glucose concentration every 1-5 min for days or weeks. New CGM-based diabetes therapies are often tested in in silico clinical trials (ISCTs) using diabetes simulators. Accurate models of CGM sensor inaccuracies and failures could help improve the realism of ISCTs. However, the modeling of CGM failures has not yet been fully addressed in the literature. This work aims to develop a mathematical model of CGM gaps, i.e., occasional portions of missing data generated by temporary sensor errors (e.g., excessive noise or artifacts). MethodsTwo datasets containing CGM traces collected in 167 adults and 205 children, respectively, using the Dexcom G6 sensor (Dexcom Inc., San Diego, CA) were used. Four Markov models, of increasing complexity, were designed to describe three main characteristics: number of gaps for each sensor, gap distribution in the monitoring days, and gap duration. Each model was identified on a portion of each dataset (training set). The remaining portion of each dataset (real test set) was used to evaluate model performance through a Monte Carlo simulation approach. Each model was used to generate 100 simulated test sets with the same size as the real test set. The distributions of gap characteristics on the simulated test sets were compared with those observed on the real test set, using the two-sample Kolmogorov-Smirnov test and the Jensen-Shannon divergence. ResultsA six-state Markov model, having two states to describe normal sensor operation and four states to describe gap occurrence, achieved the best results. For this model, the Kolmogorov-Smirnov test found no significant differences between the distribution of simulated and real gap characteristics. Moreover, this model obtained significantly lower Jensen-Shannon divergence values than the other models. ConclusionsA Markov model describing CGM gaps was developed and validated on two real datasets. The model describes well the number of gaps for each sensor, the gap distribution over monitoring days, and the gap durations. Such a model can be integrated into existing diabetes simulators to realistically simulate CGM gaps in ISCTs and thus enable the development of more effective and robust diabetes management strategies.

On Detecting Route Hijacking Attack in Opportunistic Mobile Networks

In this paper, we show that Hybrid Routing and Prophet protocols in Opportunistic Mobile Networks (OMNs) are vulnerable to the <i>CollusiveHijack</i> attack, in which a malicious attacker, Eve, compromises a set of nodes and lies about their Inter-Contact-Times (ICTs). Eve claims that her nodes meet more frequently than in reality to hijack the routes of legitimate nodes in OMNs. The CollusiveHijack attack enables Eve to launch more severe attacks like packet modification, traffic analysis, and incentive seeking attacks. To identify the CollusiveHijack attack, we propose the Kolmogorov-Smirnov two-sample test to determine whether the statistical distribution of the packets&#x2019; delays follows the derived distribution from the ICTs among the nodes. We propose three techniques to detect the CollusiveHijack attack, the Path Detection Technique (PDT), the Hop Detection Technique (HDT), and the Early Hop Detection Technique (EHDT), which trade off compatibility with the Bundle Security Protocol, the detection rate, and the detection latency. We evaluated our techniques through extensive trace-driven simulations and a proof-of-concept system implementation and show that they can detect CollusiveHijack attacks with 80.0&#x0025; to 99.4&#x0025; detection rates (when Eve hijacks more than 60 packets) while maintaining a low false positive rate (<inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>3.6&#x0025;) and a short detection latency (7-14 hours) for EHDT (75&#x0025;-85&#x0025; enhancement compared to PDT and HDT).

A Novel Method for Automatic Detection and Elimination of the Jumps Caused by the Instantaneous Disturbance Torque in the Maglev Gyro Signal

The signal measured by the maglev gyro sensor is sensitive to the influence of the instantaneous disturbance torque caused by the instantaneous strong wind or the ground vibration, which reduced the north-seeking accuracy of the instrument. To address this issue, we proposed a novel method combining the heuristic segmentation algorithm (HSA) and the two-sample Kolmogorov-Smirnov (KS) test (named HSA-KS method) to process the gyro signals and improve the north-seeking accuracy of the gyro. There were two key steps in the HSA-KS method: (i) all the potential change points were automatically and accurately detected by HSA, and (ii) the jumps in the signal caused by the instantaneous disturbance torque were quickly located and eliminated by the two-sample KS test. The effectiveness of our method was verified through a field experiment on a high-precision global positioning system (GPS) baseline at the 5th sub-tunnel of the Qinling water conveyance tunnel of the Hanjiang-to-Weihe River Diversion Project in Shaanxi Province, China. Our results from the autocorrelograms indicated that the jumps in the gyro signals can be automatically and accurately eliminated by the HSA-KS method. After processing, the absolute difference between the gyro and high-precision GPS north azimuths was enhanced by 53.5%, which was superior to the optimized wavelet transform and the optimized Hilbert-Huang transform.

Overt and Occult Hypoxemia in Patients Hospitalized With COVID-19.

Measure overt and occult hypoxemia using ePFR. We retrospectively studied COVID-19 hospital encounters (n = 5,319) at two academic centers (University of Virginia [UVA] and Emory University). We measured primary outcomes (death or ICU transfer within 24 hr), ePFR, conventional hypoxemia measures, baseline predictors (age, sex, race, comorbidity), and acute predictors (National Early Warning Score [NEWS] and Sequential Organ Failure Assessment [SOFA]). We updated predictors every 15 minutes. We assessed predictive validity using adjusted odds ratios (AORs) and area under the receiver operating characteristic curves (AUROCs). We quantified disparities (Black vs non-Black) in empirical cumulative distributions using the Kolmogorov-Smirnov (K-S) two-sample test. Overt hypoxemia (low ePFR) predicted bad outcomes (AOR for a 100-point ePFR drop: 2.7 [UVA]; 1.7 [Emory]; p < 0.01) with better discrimination (AUROC: 0.76 [UVA]; 0.71 [Emory]) than NEWS (0.70 [both sites]) or SOFA (0.68 [UVA]; 0.65 [Emory]) and similar to S/F ratio (0.76 [UVA]; 0.70 [Emory]). We found racial differences consistent with occult hypoxemia. Black patients had better apparent oxygenation (K-S distance: 0.17 [both sites]; p < 0.01) but, for comparable ePFRs, worse outcomes than other patients (AOR: 2.2 [UVA]; 1.2 [Emory]; p < 0.01). The ePFR was a valid measure of overt hypoxemia. In COVID-19, it may outperform multi-organ dysfunction models. By accounting for biased oximetry as well as clinicians' real-time responses to it (supplemental oxygen adjustment), ePFRs may reveal racial disparities attributable to occult hypoxemia.

A SPECTRAL-SPATIAL AUGMENTED ACTIVE LEARNING METHOD FOR HYPERSPECTRAL IMAGE CLASSIFICATION

Abstract. In this paper, a new classification technique for hyperspectral images (HSIs) based on an augmented active learning (AL) is introduced. The proposed method consists of two main steps: first, a 2-D non-subsampled shearlet transform (NSST) is applied to each spectral band of HSIs to extract the spatial features. After that, the kernel minimum noise fraction (KMNF) is used to reduce the spectral dimension. Second, the classification task using an augmented active learning technique is performed. For this purpose, an iterative process is considered. At each iteration, a discriminative sample selection and augmentation are used to create the training set. Then, the support vector machine (SVM) is iteratively applied to the training set. In the proposed method, the most informative samples are selected by a new query function combination of a posterior probability-based uncertainty and angle-based diversity criteria. The augmentation strategy during the training process is chosen by two-sample Kolmogorov-Smirnov test and the existing outliers are removed by k-means clustering. Finally, the proposed algorithm is applied to the real datasets and compared with three state-of-the-art AL algorithms. The obtained results show that the proposed method significantly increases accuracy considering the most informative samples.

A new approach to recognizing the correct pattern of cross-peaks from a noisy 2D asynchronous spectrum by detecting intrinsic symmetry via the Kolmogorov-Smirnov test.

The characteristic cluster pattern of cross-peaks in a 2D asynchronous spectrum provides an effective way to reveal the specific physicochemical nature of subtle spectral changes caused by intermolecular interactions. However, the inevitable presence of noise in the 1D spectra used to construct a 2D asynchronous spectrum is significantly amplified, which poses a serious challenge in identifying the correct cluster pattern of the cross-peaks. While mirror symmetry occurs in some types of cross-peaks, it does not occur in other types. The Kolmogorov-Smirnov test provides a statistical means to check whether the mirror symmetry exists or not between a pair of cross-peaks covered by heavy noise. Thus, different types of cross-peak clusters can be distinguished by excavating intrinsic spectral features from the noisy 2D asynchronous spectrum. The effectiveness of this approach in investigating the nature of intermolecular interactions was showcased in both a simulated model system and a real artemisinin/N-methyl pyrrolidone system.

Two-sample test with g-modeling and its applications.

Many real data analyses involve two-sample comparisons in location or in distribution. Most existing methods focus on problems where observations are independently and identically distributed in each group. However, in some applications the observed data are not identically distributed but associated with some unobserved parameters which are identically distributed. To address this challenge, we propose a novel two-sample testing procedure as a combination of the -modeling density estimation introduced by Efron and the two-sample Kolmogorov-Smirnov test. We also propose efficient bootstrap algorithms to estimate the statistical significance for such tests. We demonstrate the utility of the proposed approach with two biostatistical applications: the analysis of surgical nodes data with binomial model and differential expression analysis of single-cell RNA sequencing data with zero-inflated Poisson model.