Two-sample Kolmogorov-Smirnov Test Research Articles

Correlation between Demographic Status and Nutritional Status against the Occurrence of Anemia toward Female Students in SMP Negeri 3 Kediri

This research used cross sectional design. The population was 194 respondents and the sample was 54 respondents which are taken by using simple random sampling technique. Independent variable in this research was demographic status that was measured by questionnaire and nutritional status that was measured based on Body Mass Index (BMI). Meanwhile, dependent variable in this research was the occurrence of anemia that was measured by using spectrophotometry. Data analysis used Fisher's Exact test and Two-Sample Kolmogorov-Smirnov test (α = 0,05). Result of Fisher's Exact test was obtained that p value = 1,000 > 0,05, which meant that there was no significant correlation between demographic status and the occurrence of anemia. Meanwhile, result of Two-Sample Kolmogorov-Smirnov test was obtained that p value = 0,017 < 0,05, which meant that there was a significant correlation between nutritional status and the occurrence of anemia.

Nonparametric Predictive Inference Bootstrap with Application to Reproducibility of the Two-Sample Kolmogorov\u2013Smirnov Test

This paper introduces a new bootstrap method based on the nonparametric predictive inference (NPI) approach to statistics.NPI is a frequentist statistics framework which explicitly focuses on prediction of future observations. The NPI framework enables a bootstrap method (NPI-B) to be introduced which, different to Efron’s classical bootstrap (Ef-B), is aimed at prediction of future observations instead of estimation of population characteristics.A brief initial comparison of NPI-B and Ef-B is presented. The main reason for introducing NPI-B here is for its application to NPI for reproducibility of statistical tests, which is illustrated for the two-sample Kolmogorov–Smirnov test.

Radio observations of magnetic cataclysmic variables

The NSF’s Karl G. Jansky Very Large Array (VLA) is used to observe 122 magnetic cataclysmic variables (MCVs) during three observing semesters (13B, 15A, and 18A). We report radio detections of 33 stars with fluxes in the range 6–8031 μJy. Twenty-eight stars are new radio sources, increasing the number of radio detected MCVs to more that 40. A surprising result is that about three-quarters (24 of 33 stars) of the detections show highly circularly polarized radio emission of short duration, which is characteristic of electron cyclotron maser emission. We argue that this emission originates from the lower corona of the donor star, and not from a region between the two stars. Maser emission enables a more direct estimate of the mean coronal magnetic field of the donor star, which we estimate to be 1–4 kG assuming a magnetic filling factor of 50%. A two-sample Kolmogorov-Smirnov test supports the conclusion that the distribution function of radio detected MCVs with orbital periods between 1.5 and 5 h is similar to that of all MCVs. This result implies that rapidly-rotating (Pspin < 10 days), fully convective stars can sustain strong magnetic dynamos. These results support the model of that the change in angular momentum loss across the fully convective boundary at Porb ≈3 h is due to a change in the magnetic field structure of the donor star from a low-order to high-order multipolar field.

Robust Forecasting of River-Flow Based on Convolutional Neural Network

In this paper, a novel method is developed for day-ahead daily river-flow forecasting based on convolutional neural network (CNN). The proposed method incorporates both spatial and temporal information to improve the forecasting performance. A CNN model is usually trained by minimizing the mean squared error which is, however, sensitive to few particularly large errors. This character of squared error loss function will result in a poor estimator. To tackle the problem, a robust loss function is proposed to train the CNN. To facilitate the training of CNNs for multiple sites forecasting, transfer learning is also applied in this study. With transfer learning, a new CNN inherits the structure and partial learnable parameters from a well-trained CNN to reduce the training complexity. The forecasting performance of the proposed method is validated with real data of four rivers by comparing with widely used benchmarking models including the autoregressive model, multilayer perception network, kernel ridge regression, radial basis function neural network, and generic CNN. Numerical results show that the proposed method performs best in terms of the root mean squared error, mean absolute error, and mean absolute percentage error. The two-sample Kolmogorov-Smirnov test is further applied to assess the confidence on the conclusion.

The Effect of Pre-Transplant Mechanical Circulatory Support Devices on the Post-Transplant dd-cfDNA and GEP in Heart Transplantation

Evidence around sensitization and increased risk of rejection from use of pre-transplant mechanical circulatory support (MCS) devices remains conflicting. Some evidence suggests devices cause inflammation and association with increased antibody production, which has been shown to be associated with AMR, CMR, and early onset of microvasculopathy at one year post-heart transplant. However other evidence suggests no difference or increased risk of adverse outcome using pre-transplant MCS devices. dd-cfDNA (AlloSure) has been shown to rise in advance to the new formation of de-novo anti-HLA antibodies, with changes in AlloMap also seen in patients who become newly sensitized. The aim of this study was to review MCS patients post-transplant with AlloSure and AlloMap to assess if they had different patterns from those who had no MCS, to support suggestion of early post-transplant complications. Patients who were enrolled in Outcomes AlloMap® Registry (OAR) and the dd-cfDNA extension (D-OAR) were included in this analysis if pre-transplant MCS history was recorded and signs or symptoms of rejection during a visit with an accompanying blood draw were absent (i.e. no biopsy accompanied the visit or if a biopsy occurred it yielded Grade 0R). A total of 282 patients met these criteria, 130 had pre-transplant MCS and 152 did not. A total of 652 samples were collected where both AlloSure and AlloMap scores were obtained. The cumulative distribution of AlloSure and AlloMap scores were assessed via a two-sample Kolmogorov-Smirnov test to determine if differences were observed that may be attributed to mechanical assist support prior to transplant. No difference in the distribution of AlloMap scores from patients that received pre-transplant mechanical support versus those that did not was observed, similarly for AlloSure measurements, there was no statistically significant difference in the distribution of AlloSure scores between those that receive pre-transplant mechanical assist support versus those that do not receive support. Pre-transplant MCS does not correlate with increased dd-cfDNA or GEP score, suggesting the adverse outcomes seen early post-transplant are unlikely to be due to MCS devices.

Influence of CAV clustering strategies on mixed traffic flow characteristics: An analysis of vehicle trajectory data

Being one of the most promising applications enabled by connected and automated vehicles (CAV) technology, Cooperative Adaptive Cruise Control (CACC) is expected to be deployed in the near term on public roads. Thus far, the majority of the CACC studies have been focusing on the overall network performance with limited insights on the potential impacts of CAVs on human-driven vehicles (HVs). This paper aims to quantify such impacts by studying the high-resolution vehicle trajectory data that are obtained from microscopic simulation. Two platoon clustering strategies for CACC- an ad hoc coordination strategy and a local coordination strategy-are implemented. Results show that the local coordination outperforms the ad hoc coordination across all tested market penetration rates (MPRs) in terms of network throughput and productivity. According to the two-sample Kolmogorov-Smirnov test, however, the distributions of the hard braking events (as a potential safety impact) for HVs change significantly under local coordination strategy. For both of the clustering strategy, CAVs increase the average lane change frequency for HVs. The break-even point for average lane change frequency between the two strategies is observed at 30% MPR, which decreases from 5.42 to 5.38 per vehicle. The average lane change frequency following a monotonically increasing pattern in response to MPR, and it reaches the highest 5.48 per vehicle at 40% MPR. Lastly, the interaction state of the car-following model for HVs is analyzed. It is revealed that the composition of the interaction state could be influenced by CAVs as well. One of the apparent trends is that the time spent on approaching state declines with the increasing presence of CAVs.

Evaluation of tube-shaped bus shelters as a noise mitigation solution for passengers

Environmental noise pollution, a worldwide problem, interferes in the physical and mental health of humans. Therefore, noise mitigation measures should be part of urban planning around the world. In Curitiba, Brazil, many bus shelters have a tube-shaped design. The purpose of this work was to ascertain if these tube-shaped bus shelters can mitigate environmental noise that reaches their users. To this end, equivalent sound pressure levels were measured simultaneously inside and outside 40 shelters and the Insertion Loss (IL) was calculated. The difference between the external and internal sound pressure measurements was determined statistically by means of a parametric analysis of variance (one-way ANOVA) and the nonparametric two-sample Kolmogorov-Smirnov test (K-S test). These measurements indicated that tube-shaped shelters reduced environmental noise levels, with IL varying from 2.1 to 8.9 dB(A), and an average IL of 4.9 dB(A). The one-way ANOVA and K-S significance tests, with p-values of 0.0113 and 0.0022, respectively, and with 95% certainty, indicated that IL was generated by the null hypothesis corresponding to the insertion of the tube-shaped bus shelter. Hence, it can be concluded that tube-shaped bus shelters not only enable fast passenger traffic but can also mitigate environmental noise levels.

Abstract WP291: Stochastic Methods Can Resolve the Dilemma of Emergency Stroke Transport

Introduction: Drip and Ship (DNS) and Mothership (MS) are well-known emergency transport strategies in acute stroke care, but the criteria for choosing between the two is widely debated. Existing models define time-dependent outcomes but cannot resolve this debate with statistical significance because the independent variables are deterministic. We propose a novel stochastic framework that quantifies statistical significance between DNS and MS in a network of primary and comprehensive stroke centers. Methods: We represented the physiology of ischemic core growth as a stochastic first-order differential equation, enabling infarct volume at time of reperfusion to be calculated and mapped to 90-day mRS. Using Texas as a case study, we configured the state’s stroke network within 15,811 geographic blocks as defined by census data. For each block, we ran Monte Carlo simulations to generate Beta distributions of large- and small-vessel infarct volumes, which were then translated into cumulative distribution functions of mRS. A two-sample Kolmogorov-Smirnov test for significance, and Cohen’s d effect size statistic for practical significance were computed between each DNS and MS pair. Stable effect sizes were assured by sampling &gt; 5,000 total infarct volumes for each block. All model parameters were established from large cohort studies or trials. Results: Of the 13,113 blocks where the primary stroke center is the closest hospital from origin, DNS produces significantly better stroke outcomes than MS in 79.0% (0.3% SEM; P &lt; 0.05; 0.2 &lt; d &lt; 0.5). For the subset of patients with large-vessel strokes, MS produces significantly better outcomes in 44.6% of blocks (1.3% SEM; P &lt; 0.05; 0.4 &lt; d &lt; 0.85). Conclusion: Stochastic methods enable the use of clinically relevant metrics for comparative significance of DNS and MS in a geographic region. This formalism, which has not been incorporated in previous models, can be further generalized beyond stochastic infarct volumes if sufficiently large datasets become available. For example, the kinetic growth model can integrate the statistical distributions of times (pre-hospital and hospital) leading up to intervention, and patient attributes that affect outcomes, such as the degree of collateral flow and comorbidities.

A Myth of Preferred Days of Strong Earthquakes?

AbstractExisting evidence on the origin times of magnitude M≥7.5 earthquakes worldwide, based on authoritative earthquake catalogs, does not permit rejection of the null hypotheses of random coincidental occurrence at any time during the Earth or the Moon cycles. Specifically, the nonparametric Kuiper test statistics for cyclic variations applied to seismic evidence resulting from the empirical distributions of the M≥7.5 earthquake origin time Julian day (JD) and the Moon phase (MP) do not allow the rejection of the null hypotheses of uniform distributions within the corresponding cycles. On the other hand, the same Kuiper test permits the rejection of the null hypotheses of the same chance of occurrence on any JD or MP for strong magnitude M≥6.0 earthquakes, at least for the past four decades of presumably the best-earthquake determinations and, in particular, for earthquakes in the Northern Hemisphere (with an evident seasonal pattern). The nonparametric two-sample Kolmogorov–Smirnov test statistics suggest no preferred distances to the Moon at the occurrence of large earthquakes. All of this contributes, to the results of solid statistical testing of hypotheses, for a better understanding of the complex seismic response of the Earth’s lithosphere to periodic gravitational loading.

Centrality and shortest path length measures for the functional analysis of urban drainage networks

The objective of this research is to evaluate whether complex dynamics of urban drainage networks (UDNs) can be expressed in terms of their structure, i.e. topological characteristics. The present study focuses on the application of topological measures for describing the transport and collection functions of UDNs, using eight subnetworks of the Dresden sewer network as study cases. All UDNs are considered as weighted directed graphs, where edge weights correspond to structural and hydraulic pipe characteristics which affect flow. Transport functions are evaluated in terms of travel time distributions (TTDs), under the hypothesis that frequency distributions of Single Destination Shortest Paths (SDSP) of nodes to the outlet had similar shapes than TTDs. Assessment of this hypothesis is done based on two-sample Kolmogorov-Smirnov tests and comparisons of statistical moments. Collection analysis, i.e. determination of flow paths, is done based on two approaches: (1) using Edge Betweenness Centrality (EBC), and (2) based on the number of SDSP going through an edge connecting a node to the outlet, referred as Paths. Hydrodynamic simulation results are used to validate the outcomes of graph analysis with actual flow behaviors. Results indicate that given an appropriate edge weighting factor, in this case Residence Time, SDSP has the potential to be used as an indicator for flow transport in UDNs. Moreover, both EBC and Paths values were highly correlated to average flows. The first approach, however, proved to be inadequate for estimating flows near the outlet but appropriate for identifying different paths in meshed systems, while the second approach lead to better results in branched networks. Further studies regarding the influence of UDNs layout are needed.

The Optimal Morphological Model for Arterial Blood Pressure Wave Related Classification: Comparison of Two Types of Kernel Function Mixtures

The morphological modeling methods are efficient in quantifying the change of arterial blood pressure (ABP) waves. The related works focus on minimizing the modeling error but ignore the classification related modeling expression in practical applications. In this study, we explored the optimal modeling method for ABP wave related classifications. Two types of conventional models, Gaussian or Lognormal kernel function mixtures, were employed to quantitively describe the change of ABP signals, and the parameters of different models were engaged to train the different classifiers by probabilistic neural network (PNN) and random forest (RF) for identifying the ABP waves by age, gender, and whether belonging to extreme bradycardia (EB) or extreme tachycardia (ET). Then, we defined some indexes about the performance of modeling and classifications as the references to compare the different models. The ABP signals of Fantasia and 2015 PhysioNet/CinC Challenge databases were exploited as the experimental data to select the optimal model. The modeling results show that the Lognormal kernel function mixtures have a lower error in ABP wave modeling. The two-sample Kolmogorov-Smirnov test (ks-test) results indicate that the parameters of all models are markedly different at a highly significant level (h = 1, p <; 0.05) between different groups. The classification results show that the classifiers based on the four-Gaussian function model have the best performance with the average Kappa coefficients (KC) of 99.160 ± 0.123%, while the average KC for the classifiers of two-Lognormal function models is 97.585 ± 0.172%, which means there is excessive information redundancy in the classifications by the three and four kernel functions models. Considering some other indexes such as time consumption and RAM space, the 2 Lognormal function model has more potential in practical applications.

Modeling of time-dependent safety performance using anonymized and aggregated smartphone-based dangerous driving event data

Safety performance functions (SPFs) are generally used to relate exposure to the expected number of crashes aggregated over a long time (e.g. a year) by holding all other risk factors constant, and to identify hotspots that have excessive crashes regardless of different time periods. However, it is highly likely that the relationships of exposure, risk factors and crash occurrence can vary across different times of day. This study aims to establish time-dependent SPFs for urban roads by using large-scale dangerous driving event data captured by smartphones in different times of day. Multivariate conditional autoregressive (MVCAR) models are developed to jointly account for spatial and temporal dependence of crash observations. Results of two-sample Kolmogorov-Smirnov tests affirm the heterogeneity of the safety effects of dangerous driving events in different time periods. Time-dependent hotspots are identified using potential for safety improvement (PSI) metric. The assumption here is that due to the change of traffic conditions and environment across different times of day, safety hotspots for different time periods should be different from each other. According to the results of Wilcoxon signed-rank tests, hotspots identified by times of day are found to be mostly different from each other. The findings of this study provide insights into temporal effects of risk factors and can support the development of time-dependent safety countermeasures. Besides, this study also shows the potential of leveraging anonymized and aggregated dangerous driving data to assess traffic safety issues.

Mountain glacier-to-rock glacier transition

Abstract In many of the world's high mountain systems, glacier recession in response to climate change is accompanied by a paraglacial response whereby glaciers are undergoing a transition to rock glaciers. We hypothesise that this transition has important implications for hydrological resources in high mountain systems and the surrounding lowlands given the insulating effects that debris cover can have on glacier ice. Despite this, however, little is known about how this transition occurs nor how quickly, which glaciers are liable to transition, the factors driving this process and the water supply implications that follow. This paper assesses the role of glacier and rock glacier textural properties from a deglaciating region of the Himalayas to begin to address some of these issues. We investigated six landsystems on the spectrum from glaciers-to-rock glaciers in the Khumbu Himal, Nepal, and sampled for clast shape and roundness during 2016 and 2017. Kite aerial photography was additionally used to capture aerial images of an ongoing glacier-to-rock glacier transitional landform (Chola Glacier) to elucidate the surface geomorphic features of a fully transitioned landform. This image data, processed using a structure-from-motion multi-view stereo photogrammetry approach, revealed the presence of a spatially coherent ridge-and-furrow surface morphology in the lower reaches of Chola Glacier, which is potentially indicative of an ongoing glacier-to-rock glacier transition. We show that glacier-derived and slope-derived clast roundness significantly statistically different (Kolmogorov–Smirnov two-sample test: Dmax = 0.62, two-tail p

A Sequence-Based Damage Identification Method for Composite Rotors by Applying the Kullback–Leibler Divergence, a Two-Sample Kolmogorov–Smirnov Test and a Statistical Hidden Markov Model

Composite structures undergo a gradual damage evolution from initial inter-fibre cracks to extended damage up to failure. However, most composites could remain in service despite the existence of damage. Prerequisite for a service extension is a reliable and component-specific damage identification. Therefore, a vibration-based damage identification method is presented that takes into consideration the gradual damage behaviour and the resulting changes of the structural dynamic behaviour of composite rotors. These changes are transformed into a sequence of distinct states and used as an input database for three diagnostic models, based on the Kullback–Leibler divergence, the two-sample Kolmogorov–Smirnov test and a statistical hidden Markov model. To identify the present damage state based on the damage-dependent modal properties, a sequence-based diagnostic system has been developed, which estimates the similarity between the present unclassified sequence and obtained sequences of damage-dependent vibration responses. The diagnostic performance evaluation delivers promising results for the further development of the proposed diagnostic method.

Digital impressions in dentistry-accuracy of impression digitalisation by desktop scanners.

To test if the partially digital workflow by digitalisation of the impression reveals a comparable accuracy as the indirect digitalisation of the gypsum cast for 4-unit fixed dental prostheses (FDPs). A titanium model with a tapered full veneer preparation of a molar and premolar was used as analysis model. To receive a virtual three-dimensional reference dataset (REF), it was digitised by industrial computed tomography. Three impression materials were used with individual impression trays (N = 36, n/material = 12): (1) PE (Impregum Penta), (2) PVS-I (Imprint 4 Penta: Super Quick Heavy plus Super Quick Light), and (3) PVS-D (Dimension Penta: H Quick plus L). For partially digital workflow (group IMP), two desktop scanners were used: (1) D810 (3Shape D810) and (2) ZZ (Zirkonzahn S600ARTI). For indirect digitalisation (group CAST), gypsum master casts were manufactured and digitalised using the same desktop scanners. Virtual datasets were superimposed by best fit algorithm, and accuracy was analysed by calculating the Euclidean distances (ED) to the REF (Geomagic Qualify). Statistic was determined (Kruskal-Wallis H test, Mann-Whitney U post hoc analysis, two-sample Kolmogorov-Smirnov test, p < 0.05). ZZ showed for positive deviations superior accuracy for IMP than for CAST. PE and PVS-I showed superior accuracy than PVS-D. D810 showed partially significant better performance with PVS-I and PVS-D than ZZ. The partially digital workflow by digitalisation of the impression can be used for clinical indications of small-span fixed dental prostheses. However, for this indication, the impression material and the desktop scanner are more decisive for the accuracy of virtual model datasets. Despite the rapid advancement of the computer-aided technology for dental therapy purposes, the implementation of this technique is not as fast as the technical development. In order to combine the well-established procedure to use elastomeric materials for a conventional impression and to avoid the drawbacks of casting it by gypsum, the digitalisation of the impression itself by a desktop scanner may be a logical procedure as an access point to the digital workflow. However, there is only limited information about the accuracy of this partially digital workflow by the digitalisation of modern impression materials in comparison to the well-known process of indirect digitalisation of gypsum casts.

Refined asymptotic Kolmogorov-Smirnov tests for the case of finite support

We derive asymptotic critical values for the one-sample and two-sample Kolmogorov-Smirnov tests in the case where the distribution or distributions have finite support of known size. When the number of points in the support is small, these critical values are significantly smaller than those for the standard Kolmogorov-Smirnov tests, thus leading to a major increase in power. We also examine how the asymptotic critical values perform for small sample sizes, finding that they typically lead to conservative inference procedures.

927-P: Use of Mobile-Enabled Reminders Feature Is Associated with Improved Behavioral and Glycemic Outcomes in the Real World

In the real-world, lifestyle and environmental barriers can adversely impact self-management of diabetes. Emerging health technologies such as mobile diabetes management apps can potentially help people with diabetes navigate such barriers with features like reminders for self-monitoring blood glucose (SMBG) checks and medications. While such features are designed to help people with diabetes improve the structure and adherence of SMBG and medication regimens, real-world evidence of their effectiveness have been inconclusive. In the current study, we assessed whether the use of reminders features corresponded with improved behavioral and glycemic outcomes in the real world. We compared the distributions of SMBG check rates, and low (&amp;lt;70 mg/dL), in-range (between 70-180 mg/dL), and high SMBG readings (&amp;gt;180 mg/dL) between users of a mobile diabetes management app who had set up reminders and app users who did not (n=500 in each sample, balanced by age, gender, and app use duration). Two-sample Kolmogorov-Smirnov tests showed that app users with reminders performed more frequent SMBG checks (D=.20, P&amp;lt;.001), had more in-range readings (D=.096, P=.020), and had fewer high readings (D=.086, P=.049) than users without reminders. No difference was observed in the distributions of low readings, though a trend may be present (D=.084, P=.059). These findings indicate that mobile-enabled reminders can contribute to improved SMBG behavior and glycemic outcomes in the real world. As mobile health/digital therapeutic tools (and their ever-growing features/capabilities) continue to evolve, patients and clinicians have reason for optimism that such tools can improve diabetes management and reduce burden. Disclosure R. Abad Juan: Employee; Self; Glooko, Inc. T. Sheng: Employee; Self; Glooko, Inc. S. Babikian: None. M. Greenfield: Stock/Shareholder; Self; Glooko, Inc.

Novel Stopping Criteria for Optimization-Based Microwave Breast Imaging Algorithms.

A discontinuous Galerkin formulation of the Contrast Source Inversion algorithm (DGM-CSI) for microwave breast imaging employing a frequency-cycling reconstruction technique has been modified here to include a set of automated stopping criteria that determine a suitable time to shift imaging frequencies and to globally terminate the reconstruction. Recent studies have explored the use of tissue-dependent geometrical mapping of the well-reconstructed real part to its imaginary part as initial guesses during consecutive frequency hops. This practice was shown to improve resulting 2D images of the dielectric properties of synthetic breast models, but a fixed number of iterations was used to halt DGM-CSI inversions arbitrarily. Herein, a new set of stopping conditions is introduced based on an intelligent statistical analysis of a window of past iterations of data error using the two-sample Kolmogorov-Smirnov (K-S) test. This non-parametric goodness-of-fit test establishes a pattern in the data error distribution, indicating an appropriate time to shift frequencies, or terminate the algorithm. The proposed stopping criteria are shown to improve the efficiency of DGM-CSI while yielding images of equivalent quality to assigning an often liberally overestimated number of iterations per reconstruction.

Fault detection of fuel cell systems based on statistical assessment of impedance data

Accurate online health assessment of fuel cell systems is a key for the timely mitigation and maintenance actions to be taken in order to maximise reliability of operation and useful life span of the cells. The majority of approaches rely on occasional probing of the system with small-amplitude signals around an operating point. The responses are then used to create either a parametric or a non-parametric model of the linearised system dynamics. However, during the probing session, the measurements might be corrupted with random noise and disturbances. Consequently, the evaluated parameters, being points on the impedance curve, parameters of the equivalent circuit models or the distribution of relaxation times, contain some uncertainty. That fact is largely ignored in the state of the art techniques, meaning that only mean value estimates are taken into account in the further analysis. In this paper we use a non-parametric two-sample Kolmogorov-Smirnov test to detect a change in the internal condition by evaluating changes at each frequency point on the Nyquist curve. Moreover, we show that in some cases it is even possible to isolate the fault origin from the pattern of detected changes. The applicability of the approach is demonstrated on the detection of water management faults of an industrial proton exchange membrane fuel cell system.

An analytical approach for the simulation of realistic low-dose fluoroscopic images.

The quality of X-ray images plays an important role in computer-assisted interventions. Although learning-based denoising techniques have been shown to be successful in improving the image quality, they often rely on pairs of associated low- and high-dose X-ray images that are usually not possible to acquire at different dose levels in a clinical scenario. Moreover, since data variation is an important requirement for learning-based methods, the use of phantom data alone may not be sufficient. A possibility to address this issue is a realistic simulation of low-dose images from their related high-dose counterparts. We introduce a novel noise simulation method based on an X-ray image formation model. The method makes use of the system parameters associated with low- and high-dose X-ray image acquisitions, such as system gain and electronic noise, to preserve the image noise characteristics of low-dose images. We have compared several corresponding regions of the associated real and simulated low-dose images-obtained from two different imaging systems-visually as well as statistically, using a two-sample Kolmogorov-Smirnov test at 5% significance. In addition to being visually similar, the hypothesis that the corresponding regions-from 80 pairs of real and simulated low-dose regions-belonging to the same distribution has been accepted in 81.43% of the cases. The results suggest that the simulated low-dose images obtained using the proposed method are almost indistinguishable from real low-dose images. Since extensive calibration procedures required in previous methods can be avoided using the proposed approach, it allows an easy adaptation to different X-ray imaging systems. This in turn leads to an increased diversity of the training data for potential learning-based methods.