Empirical Cumulative Distribution Research Articles

A statistical method for removing unbalanced trials with multiple covariates in meta-analysis

In meta-analysis literature, there are several checklists describing the procedures necessary to evaluate studies from a qualitative point of view, whereas preliminary quantitative and statistical investigations on the “combinability” of trials have been neglected. Covariate balance is an important prerequisite to conduct meta-analysis. We propose a method to identify unbalanced trials with respect to a set of covariates, in presence of covariate imbalance, namely when the randomized controlled trials generate a meta-sample that cannot satisfy the requisite of randomization/combinability in meta-analysis. The method is able to identify the unbalanced trials, through four stages aimed at achieving combinability. The studies responsible for the imbalance are identified, and then they can be eliminated. The proposed procedure is simple and relies on the combined Anderson-Darling test applied to the Empirical Cumulative Distribution Functions of both experimental and control meta-arms. To illustrate the method in practice, two datasets from well-known meta-analyses in the literature are used.

LIDAR MATCHING STRATEGIES FOR HD POINT CLOUD MAP GENERATION IN URBAN AREA

Abstract. Autonomous driving relies on high accuracy point vector map, which was generated by the point cloud map, and pre-provides the vehicle preliminary road environment information. Lidar Odometry and Mapping (LOAM) has always been a promising research topic in the field of robotics, environment sensing, and currently autonomous driving. However, in certain urban environments like basement parking lot, tunnels, highways, or other similar settings, the geometric features are not clearly discernible. As a result, algorithms resembling the LOAM framework may encounter difficulties in accurately mapping these areas. The paper utilized relative low-cost LiDAR expecting to propose a state-of-the-art point cloud mapping/update scheme. We compared the GNSS-challenge area with straight line and loop area separately, simultaneously considered the DG, ICP, NDT matching algorithm for the low-cost mapping/update strategy. With the realistic experiment conduction, our result evaluated by point to point corresponding mean error and standard error. For the straight line environment, ICP has the fastest convergence in empirical cumulative distribution under 0.4 meters. For the loop scenario, point-to-point ICP still has the fastest convergence in empirical cumulative distribution under 0.22 meters. Yet both of them still suffer from the fault matching.

NOx formation model for utility boilers using robust two-step steady-state detection and multimodal residual convolutional auto-encoder

BackgroundTo achieve carbon neutrality, thermal power plants will take on greater peak-shaving responsibilities, resulting in utility boilers running at more frequently-changing states. This variation inevitably contributes to dirtier measurement data, and the data will follow new distributions that represent asynchronous operational characteristics. MethodTo predict the NOx formation concentration in this new context, a novel modelling framework is proposed. First, a two-step steady-state detection algorithm, including a basic and a precise identification procedure, is proposed to select steady-state samples from operational data. This algorithm also combines with a bayesian optimization–variational mode decomposition (BO–VMD) approach to enhance its robustness by eliminating high-frequency noise, and with an empirical cumulative distribution based outlier detection (ECOD) to remove outliers. Therefore, a high-quality steady-state dataset is acquired. Based on these data, a multimodal residual convolutional auto-encoder is developed to extract the latent variables that vary with the operation modes. These variables are then used to construct the multimodal NOx prediction model. Significant findingsThe findings on a 660 MW utility boiler reveal that, the proposed modelling framework has a lower misdiagnosis rate in steady-state identification compared to the one-step detection technique, and achieves higher fitting and generalization accuracy than the global modelling method.

ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions

Outlier detection refers to the identification of data points that deviate from a general data distribution. Existing unsupervised approaches often suffer from high computational cost, complex hyperparameter tuning, and limited interpretability, especially when working with large, high-dimensional datasets. To address these issues, we present a simple yet effective algorithm called ECOD (Empirical-Cumulative-distribution-based Outlier Detection), which is inspired by the fact that outliers are often the "rare events" that appear in the tails of a distribution. In a nutshell, ECOD first estimates the underlying distribution of the input data in a nonparametric fashion by computing the empirical cumulative distribution per dimension of the data. ECOD then uses these empirical distributions to estimate tail probabilities per dimension for each data point. Finally, ECOD computes an outlier score of each data point by aggregating estimated tail probabilities across dimensions. Our contributions are as follows: (1) we propose a novel outlier detection method called ECOD, which is both parameter-free and easy to interpret; (2) we perform extensive experiments on 30 benchmark datasets, where we find that ECOD outperforms 11 state-of-the-art baselines in terms of accuracy, efficiency, and scalability; and (3) we release an easy-to-use and scalable (with distributed support) Python implementation for accessibility and reproducibility.

Stochastic MPC based double-time-scale voltage regulation for unbalanced distribution networks with distributed generators

With the increasing penetration of renewable energy resources, the uncertainty of renewable energy resources output introduces challenges to voltage control in distribution networks (DNs). To solve these problems, this paper proposes a double-time-scale voltage control scheme for unbalanced DNs based on stochastic model predictive control (SMPC) to coordinate multiple voltage control devices. In fast-time-scale control, the active and reactive power outputs of distributed generators (DGs) are optimized to minimize voltage deviation, active power curtailment and reactive power fluctuation. In the slow-time-scale control, optimal tap changes of on-load tap changers (OLTC), step voltage regulators (SVR), and switched capacitor banks (CBs) are calculated to reduce voltage deviation and tap operations. Additionally, in the fast-time-scale control, the SMPC-based scheme adopts a scenario generation approach to represent the uncertainty of DG output. Considering the temporal correlation of the DG output, different prediction scenarios and scenario probabilities are obtained by the empirical cumulative distribution function fitted by the historical measurement data. In this paper, the effectiveness of the proposed double-time-scale voltage control scheme is verified by the unbalanced IEEE123 node system. The case study demonstrates that the proposed voltage scheme can effectively keep the three-phase voltages in unbalanced DN within the secure operation range and achieve better voltage profiles.

Portfolio choice algorithms, including exact stochastic dominance

Assume data on Nj stock (asset) returns are available for p stocks, allowing us to construct approximate density functions f(xj) for (j=1, 2, …, p) from p empirical cumulative distribution functions (ECDFs). Our portfolio choice is designed to rank ECDF-induced, ill-behaved f(xj) densities subject to multiple modes, asymmetric fat tails, dips, turns, and numerous overlaps. Older portfolio theory assumes that parameters like the mean, variance, and percentiles fully describe f(xj). All six of our algorithms avoid (expected) utility theory. The only available algorithm by Anderson for order-k Stochastic Dominance (SDk) needs a trapezoidal approximation. Our new exact algorithm for SDk is based on ECDFs and overcomes pairwise comparisons. We include algorithms for statistical inference using the bootstrap and one for “pandemic proof” out-of-sample portfolio performance comparisons from our R package ‘generalCorr’. We suggest a test for “zero cost profitable arbitrage” and illustrate our algorithms in action by using two sets of recent 169-month stock returns. We do not claim to suggest new optimal portfolios.

Patient-Reported Improvements in Fatigue and Health-Related Quality of Life in the Phase 3 Studies Apply-PNH and Appoint-PNH Evaluating the Use of Iptacopan in C5 Inhibitor-Treated and Treatment-Naïve Patients with Paroxysmal Nocturnal Hemoglobinuria

Introduction: Iptacopan is a first-in-class, oral, selective complement factor B inhibitor that recently demonstrated efficacy and safety as monotherapy in both C5 inhibitor-treated (APPLY-PNH [NCT04558918], Peffault de Latour et al) and treatment-naïve (APPOINT-PNH [NCT04820530], Risitano et al) patients with paroxysmal nocturnal hemoglobinuria (PNH). The primary results of active-comparator controlled APPLY-PNH and single-arm APPOINT-PNH have been reported, showing clinically meaningful increases in hemoglobin levels while controlling intravascular hemolysis and preventing extravascular hemolysis. As patient-perceived benefits of therapies are increasingly important, we present here additional results of the patient-reported outcomes measures (PROMs) including responder analyses of changes from baseline in these two studies based on meaningful within-patient thresholds. Methods: The PROMs included an assessment of fatigue (FACIT-Fatigue) as the basis of a secondary endpoint, a health-related quality of life questionnaire (EORTC QLQ-C30), and a Patient Global Impression of Severity (PGIS) of Fatigue. Both studies analyzed change from baseline in PROMs between Day 1 and Day 168. In APPLY-PNH, another focus was on comparing treatments on the average of model-derived estimates for visits between Day 126 and Day 168. While 5 points on the FACIT-Fatigue was prespecified in the protocol, anchor-based meaningful within-patient change analyses utilizing the PGIS as the anchor determined the range for the magnitude of meaningful change for the FACIT-Fatigue to be 7.5 to 9.5 points (derived from APPLY-PNH); 9 points within the upper segment of the range was chosen as the threshold value used in this analysis. Analyses of the EORTC QLQ C-30 focused on the Physical and Role Functioning subscales and symptoms subscales evaluating Fatigue and Dyspnea. Responder analysis thresholds were determined for all 4 subscales using a combination of anchors including PGIS to determine meaningful change thresholds for each subscale. The ranges and threshold choices were based on the assessment of empirical cumulative distribution function (eCDF) of the scores by scale levels of the anchor and used the visit median values as representative levels to determine the range supporting threshold choices. Results: Ninety-five patients were from APPLY-PNH, of whom 62 received iptacopan and 33 received anti-C5 treatment, and 40 patients were from APPOINT-PNH. In APPLY-PNH, responder analysis showed that more patients in the iptacopan group achieved the threshold of ≥9 points compared with the anti-C5 group (51% vs 11%) ( Table 1). The strength of this result was confirmed by a non-parametric Mann-Whitney test with P values ≤0.0039. In APPOINT-PNH, 56% (95% CI, 43-68) achieved the threshold of ≥9 points and approximately half reached a higher threshold of 11 points ( Table 1). Analyses of EORTC QLQ-C30 further corroborated the results for both trials. Responder analyses in APPLY-PNH of the four subscales using internally derived thresholds showed average proportions of responders in the iptacopan group 39% to 49% and in the anti-C5 group ranging from 9% to 20% ( Table 2). The strength of these results was confirmed by a non-parametric assessment of cumulative distribution functions and Mann-Whitney with P values ≤0.0088. In APPOINT-PNH, the estimated average proportions of responders for EORTC QLQ C30 ranged from 41% to 55% ( Table 2). Conclusion: Both anti-C5-experienced and treatment-naïve patients receiving iptacopan exhibited clinically meaningful improvements in fatigue, health-related quality of life, and disease-related symptoms as measured by predetermined meaningful thresholds of response in FACIT-Fatigue and EORTC QLQ-C30. Together with previous presented primary efficacy data from APPLY-PNH and APPOINT-PNH, these results indicate that iptacopan not only results in clinically meaningful improvements in markers of disease control but provides meaningful improvements in aspects that are important to patients (ie, fatigue and other aspects of health-related quality of life, such as physical functioning and role functioning).

Geospatial modeling of hydrological ecosystem services in an ungauged upper Yamuna catchment using SWAT

Hydrological ecosystem services (HES) are vital for resource allocation, conservation prioritization, and climate change adaptation. However, research gaps persist due to limited understanding of complex hydrological systems and inadequate ground-station data, especially in ungauged watersheds with complex terrains. The present study addresses these gaps by estimating and mapping HES descriptors using regionalization techniques in an ungauged Aglar watershed. Additionally, it conducts a temporal analysis of hydrological fluxes using Soil and Water Assessment Tool (SWAT). Aglar is a constituent sub-watershed of a gauged watershed named Bausan having two discharge observation stations located at Naugaon and Bausan sites in Uttarakhand, India. A nested parameterization approach was adopted to represent the hydrological variabilities over the Bausan watershed with one outlet at Aglar. The stream flow derived from the calibrated Bausan watershed was used as synthetic observations in SWAT model setup for Aglar and found to yield very good statistical results. Calibration yielded coefficient of determination (R2) = 0.91, Nash Sutcliff Efficiency (NSE) = 0.91, and standardized root mean square error (RSR) = 0.29, while the validation yielded R2 = 0.68, NSE = 0.50, and RSR = 0.37. Parameters related to base flow or stream flow were the most sensitive in the model's output. Water balance analysis reveals 36% of precipitation transformed into stream flow, with direct runoff accounting for 72% and base flow for 28%. Forest cover contributed approximately 50.68% of precipitation through evapotranspiration. The study identifies a maximum sediment load of approximately 26 t/ha, indicating fragility of the landscape. Non-parametric tests such as Mann Kendall and Sen's slope indicated increasing trends in surface runoff, lateral flow, water yield, and groundwater recharge. The analysis of the empirical cumulative distribution function demonstrated that all hydrological components exhibit trends similar to precipitation. Spatially and temporally, variations in HES provisioning were observed, with forests surpassing non-forest areas. These findings emphasize the value of HES descriptors in analyzing spatiotemporal changes in HES provisioning and offer valuable insights for policymakers for future policy dialogues. This study lays the groundwork for further investigations into the hydrological processes of ungauged watersheds.

The Daily Patterns of Emergency Medical Events.

This study examines population-level daily patterns of time-stamped emergency medical service (EMS) dispatches to establish their situational predictability. Using visualization, sinusoidal regression, and statistical tests to compare empirical cumulative distributions, we analyzed 311,848,450 emergency medical call records from the US National Emergency Medical Services Information System (NEMSIS) for years 2010 through 2022. The analysis revealed a robust daily pattern in the hourly distribution of distress calls across 33 major categories of medical emergency dispatch types. Sinusoidal regression coefficients for all types were statistically significant, mostly at the p < 0.0001 level. The coefficient of determination ranged from 0.84 and 0.99 for all models, with most falling in the 0.94 to 0.99 range. The common sinusoidal pattern, peaking in mid-afternoon, demonstrates that all major categories of medical emergency dispatch types appear to be influenced by an underlying daily rhythm that is aligned with daylight hours and common sleep/wake cycles. A comparison of results with previous landmark studies revealed new and contrasting EMS patterns for several long-established peak occurrence hours-specifically for chest pain, heart problems, stroke, convulsions and seizures, and sudden cardiac arrest/death. Upon closer examination, we also found that heart attacks, diagnosed by paramedics in the field via 12-lead cardiac monitoring, followed the identified common daily pattern of a mid-afternoon peak, departing from prior generally accepted morning tendencies. Extended analysis revealed that the normative pattern prevailed across the NEMSIS data when reorganized to consider monthly, seasonal, daylight-savings versus civil time, and pre-/post-COVID-19 periods. The predictable daily EMS patterns provide impetus for more research that links daily variation with causal risk and protective factors. Our methods are straightforward and presented with detail to provide accessible and replicable implementation for researchers and practitioners.

Statistical Interpretation and Comparison of Waterfall Plots.

Waterfall plots have gained popularity as a visualization tool to present antitumor activity of treatments in oncology, especially for phase I and II trials. The typical waterfall plot in oncology is a bar plot with each bar representing the best percent tumor size reduction from baseline for a patient sorted in descending order along the x-axis. As new therapies are routinely developed in combination with standard of care or other investigational treatments, waterfall plot comparison between combination therapy and monotherapy may facilitate development decisions in addition to overall response rate or duration of response. However, waterfall plots are often assessed heuristically in practice with lack of statistical rigor. In this work, we examine the correspondence between the waterfall plot and the empirical cumulative distribution function. We demonstrate how to derive key summary statistics directly from the waterfall plot. Using real examples from published waterfall plots, we show how comparisons of waterfall plots can elucidate clinically meaningful information, such as treatment effect patterns in progression-free survival and overall survival.

Reliability of Ensemble Climatological Forecasts

AbstractEnsemble climatological forecasts play a critical part in benchmarking the predictive performance of hydroclimatic forecasts. Accounting for the skewness and censoring characteristics of hydroclimatic variables, ensemble climatological forecasts can be generated by the log, Box‐Cox and log‐sinh transformations, by the combinations of the Bernoulli distribution with the Gaussian, Gamma, log‐normal, generalized extreme value, generalized logistic and Pearson type III distributions and by the non‐parametric resampling, empirical cumulative distribution function and kernel density estimation methods. This paper is concentrated on the reliability of the 12 types of ensemble climatological forecasts. Specifically, mathematical formulations are presented and large‐sample tests are devised to verify the forecast reliability for the Multi‐Source Weighted‐Ensemble Precipitation version 2 across the globe. Climatological forecasts of monthly precipitation over 18,425 grid cells are generated for 30 years under leave‐one‐year‐out cross validation, leading to 6,633,000 (12 × 18425 × 30) sets of ensemble climatological forecasts. The results point out that the reliability of climatological forecasts considerably varies across the 12 methods, particularly in regions with high hydroclimatic variability. One observation is that climatological forecasts tend to deviate from the distributions of observations when there is inadequate flexibility to fit precipitation data. Another observation is that ensemble spreads can be overly wide when there exist overfits of sample‐specific noises in cross validation. Through the tests of global precipitation, the robustness of the log‐sinh transformation and the Bernoulli‐Gamma distribution is highlighted. Overall, the investigations can serve as a guidance on the uses of transformations, distributions and non‐parametric methods in generating climatological forecasts.

Effect of ocean circulation and chlorophyll-a concentration on yellowfin tuna catch rates in Sri Lankan logline fishery

Sri Lanka has access to fishery resources in the Indian Ocean due to its location. While traditional fishing methods are still used in the Sri Lankan fishery industry, modern technologies such as remote sensing and GIS are employed to determine spatiotemporal distribution of tuna fish resources in the offshore fishery industry. However, due to uncertain catch rates, Sri Lankan fishers report low catch per unit effort, leading to increased fishing duration to meet targeted catch. Potential fishing areas can be identified based on oceanographic conditions to reduce search time and improve efficiency, particularly for highly migratory species like yellowfin tuna. Therefore, this study is focused on identifying the impact of subsurface ocean currents and chlorophyll-a concentration on yellowfin tuna aggregations. The study analysed fishery and oceanographic data from January 2018 to December 2019 within a latitudinal range of 0°N to 20°N and a longitudinal range of 70°E to 90°E. The data were gridded at a resolution of 0.25 degrees and then matched for analysis. The generalized additive model (GAM) and the empirical cumulative distribution function (ECDF) were applied to identify the nature of relationships between CPUE and the oceanographic conditions. The results showed high tuna aggregations occurred in chlorophyll-a concentration between 0.3– 0.4 mg/m3 and current speed less than 0.4 m/s at a depth of 60 – 75 m of Sri Lankan longliners. Yellowfin tuna fishable aggregations were available throughout the year, although the spatial distribution of yellowfin tuna varied depending on the prevailing oceanographic conditions. Thus, searching for fishing locations based on oceanographic factors is essential for successful fishing operations. Hence, tuna harvest can be enhanced by maintaining fishing areas and the longline depth according to the oceanographic factors.

Statistical approach to electro-optic and infrared transmission within the atmosphere through empirical cumulative distributions via modeling, simulation, and data on a global scale

Statistical approach to electro-optic and infrared transmission within the atmosphere through empirical cumulative distributions via modeling, simulation, and data on a global scale

Methods for Enhancing the Robustness of the Generalized Contrast-to-Noise Ratio.

The generalized contrast-to-noise ratio (gCNR) is a new but increasingly popular metric for measuring lesion detectability due to its use of probability distribution functions that increase robustness against transformations and dynamic range alterations. The value of these kinds of metrics has become increasingly important as it becomes clear that traditional metrics can be arbitrarily boosted with advanced beamforming or the right kinds of postprocessing. The gCNR works well for most cases; however, we will demonstrate that for some specific cases the implementation of gCNR using histograms requires careful consideration, as histograms can be poor estimates of probability density functions (PDFs) when designed improperly. This is demonstrated with simulated lesions by altering the amount of data and the number of bins used in the calculation, as well as by introducing some extreme transformations that are represented poorly by uniformly spaced histograms. In this work, the viability of a parametric gCNR implementation is tested, more robust methods for implementing histograms are considered, and a new method for estimating gCNR using empirical cumulative distribution functions (eCDFs) is shown. The most consistent methods found were to use histograms on rank-ordered data or histograms with variable bin widths, or to use eCDFs to estimate the gCNR.

A guide towards optimal detection of transient oscillatory bursts with unknown parameters

Objectives. Recent event-based analyses of transient neural activities have characterized the oscillatory bursts as a neural signature that bridges dynamic neural states to cognition and behaviors. Following this insight, our study aimed to (1) compare the efficacy of common burst detection algorithms under varying signal-to-noise ratios and event durations using synthetic signals and (2) establish a strategic guideline for selecting the optimal algorithm for real datasets with undefined properties. Approach. We tested the robustness of burst detection algorithms using a simulation dataset comprising bursts of multiple frequencies. To systematically assess their performance, we used a metric called ‘detection confidence’, quantifying classification accuracy and temporal precision in a balanced manner. Given that burst properties in empirical data are often unknown in advance, we then proposed a selection rule to identify an optimal algorithm for a given dataset and validated its application on local field potentials of basolateral amygdala recorded from male mice (n=8) exposed to a natural threat. Main Results. Our simulation-based evaluation demonstrated that burst detection is contingent upon event duration, whereas accurately pinpointing burst onsets is more susceptible to noise level. For real data, the algorithm chosen based on the selection rule exhibited superior detection and temporal accuracy, although its statistical significance differed across frequency bands. Notably, the algorithm chosen by human visual screening differed from the one recommended by the rule, implying a potential misalignment between human priors and mathematical assumptions of the algorithms. Significance. Therefore, our findings underscore that the precise detection of transient bursts is fundamentally influenced by the chosen algorithm. The proposed algorithm-selection rule suggests a potentially viable solution, while also emphasizing the inherent limitations originating from algorithmic design and volatile performances across datasets. Consequently, this study cautions against relying solely on heuristic-based approaches, advocating for a careful algorithm selection in burst detection studies.

Simultaneous Network Reconfiguration and Wind Power Plants Participation in Day‐Ahead Electricity Market Considering Uncertainties

Recently, the participation of wind sources in electricity markets has become a severe challenge due to their intermittent nature. Reconfiguration of power systems can effectively reduce the negative effects of uncertainties. So, this article presents a new method for participating in wind power plants and uncertain customers in a day‐ahead electricity market considering the reconfiguration process. This method tries to maximize social welfare through a two‐level optimization problem. To this end, uncertainties are modeled using the empirical cumulative distribution function and the Monte–Carlo method, and a probabilistic analysis of the market is performed. Then, by defining some indices to evaluate the participants’ satisfaction and using the analytic hierarchy process method (AHP), a new objective function is proposed so that its minimization leads to planning the system configuration and market participants to optimize mentioned indices. The proposed methodology also assumes that the participation of uncertain participants in the spot market will eliminate the imbalances caused by uncertainties. The simulations are implemented using real data on an 8‐bus sample network. The results confirm the efficiency of the proposed method in significantly reducing power producers’ and customers’ costs along with increasing total income and profit from the sale of energy.

Establishing clinically meaningful within-individual improvement thresholds for eight patient-reported outcome measures in people with relapsing-remitting multiple sclerosis

BackgroundAs disease-modifying therapies do not reverse the course of multiple sclerosis (MS), assessment of therapeutic success involves documenting patient-reported outcomes (PROs) concerning health-related quality of life, disease and treatment-related symptoms, and the impact of symptoms on function. Interpreting PRO data involves going beyond statistical significance to calculate within-patient meaningful change scores. These thresholds are needed for each PRO in order to fully interpret the PRO data. This analysis of PRO data from the PROMiS AUBAGIO study, which utilized 8 PRO instruments in teriflunomide-treated relapsing-remitting MS (RRMS) patients, was designed to estimate clinically meaningful within-individual improvement thresholds in the same manner, for 8 PRO instruments.ResultsThe analytical approach followed a triangulation exercise that considered results from anchor- and distribution-based methods and graphical representations of empirical cumulative distribution functions in PRO scores in groups defined by anchor variables. Data from 8 PRO instruments (MSIS-29 v2, FSMC, MSPS, MSNQ, TSQM v1.4, PDDS, HRPQ-MS v2, and HADS) were assessed from 434 RRMS patients. For MSIS-29 v2, FSMC, MSPS, and MSNQ total scores, available anchor variables enabled both anchor- and distribution-based methods to be applied. For instruments with no appropriate anchor available, distribution-based methods were applied. A recommended value for meaningful within-individual improvement was defined by comparing mean change in PRO scores between participants showing improvement of one or two categories in the anchor variable or those showing no change. A “lower bound” estimate was calculated using distribution-based methods. An improvement greater than the lower-bound estimate was considered “clinically meaningful”.ConclusionThis analysis produced estimates for assessing meaningful within-individual improvements for 8 PRO instruments used in MS studies. These estimates should be useful for interpreting scores and communicating study results and should facilitate decision-making by regulatory and healthcare authorities where these 8 PROs are commonly employed.

Statistical Studies of Auroral Activity and Perturbations of the Geomagnetic Field at Middle Latitudes R.

In this paper, we statistically analyzed substorm activity at auroral latitudes for 2007–2020 and itsrelationship with magnetic disturbances at middle latitudes using the INTERMAGNET, SuperMAG, andIMAGE magnetometer data. The appearance and development of magnetic disturbances at auroral latitudeswas monitored by the IL index (similar to the AL index, but calculated according to IMAGE data). For the2007–2020 period, events that were observed near the meridian of the IMAGE network, in the night sector(2103 MLT), were selected. Two samples of events were used: (1) IL –200 nT for at least 10 min, with anadditional criterion for the presence or absence of positive bays at the Panagyurishte station in Bulgaria, and(2) isolated substorms observed on the IMAGE meridian according to the list of Ohtani and Gjerloev (2020).The distributions of the IL index, as well as the empirical and theoretical cumulative distribution functions,are obtained, and the of the occurrence of extreme events are also estimated. It is shown that, in general, theIL distributions are described well by exponential functions, and out of all events, events accompanied bymid-latitude positive bays were observed in ~65% of cases while their fraction increased with increasing disturbanceintensity. Events with positive bays at midlatitudes of MPB and isolated substorms were betterdescribed by the Weibull distribution for extreme events. From both distributions, annual and semi-annualvariations were identified: annual variations have a summer minimum and a winter maximum, and semiannualvariations have maxima near the equinoxes, which is most likely due to the Russell-McPherron effect.The semi-annual variation is also shown to be more pronounced for events with accompanying mid-latitudinalpositive bays.

Obstructive Sleep Apnea Patients With Atrial Arrhythmias Suffer From Prolonged Recovery From Desaturations

We aimed to investigate how acute and long-term effects of atrial arrhythmias affect the desaturation severity and characteristics determined from the oxygen saturation signal in OSA patients. 520 suspected OSA patients were included in retrospective analyses. Eight desaturation area and slope parameters were calculated from blood oxygen saturation signals recorded during polysomnographic recordings. Patients were grouped based on whether they had previously diagnosed atrial arrhythmia (i.e., atrial fibrillation (AFib) or atrial flutter) or not. Furthermore, patients with a previous atrial arrhythmia diagnosis were sub-grouped based on whether they had continuous AFib or sinus rhythm during the polysomnographic recordings. Empirical cumulative distribution functions and linear mixed models were utilized to investigate the connection between diagnosed atrial arrhythmia and the desaturation characteristics. Patients with previous atrial arrhythmia diagnosis had greater desaturation recovery area when the 100% oxygen saturation baseline reference was considered (β=0.150-0.127, p≤0.039) and more gradual recovery slopes (β=-0.181- -0.199, p<0.004) than patients without a previous atrial arrhythmia diagnosis. Furthermore, patients with AFib had more gradual oxygen saturation fall and recovery slopes than patients with sinus rhythm. Desaturation recovery characteristics in the oxygen saturation signal contains essential information about the cardiovascular response to hypoxemic periods. More comprehensive consideration of the desaturation recovery section could provide more detailed information about OSA severity, for example when developing new diagnostic parameters.

A family of consistent normally distributed tests for Poissonity

A family of consistent tests, derived from a characterization of the probability generating function, is proposed for assessing Poissonity against a wide class of count distributions, which includes some of the most frequently adopted alternatives to the Poisson distribution. Actually, the family of test statistics is based on the difference between the plug-in estimator of the Poisson cumulative distribution function and the empirical cumulative distribution function. The test statistics have an intuitive and simple form and are asymptotically normally distributed, allowing a straightforward implementation of the test. The finite sample properties of the test are investigated by means of an extensive simulation study. The test shows satisfactory behaviour compared to other tests with known limit distribution.