Series Of Monte-Carlo Simulations Research Articles

Eighteen new fast radio bursts in the High Time Resolution Universe survey

Current observational evidence reveals that fast radio bursts (FRBs) exhibit bandwidths ranging from a few dozen MHz to several GHz. Traditional FRB searches primarily employ matched filter methods on time series collapsed across the entire observational bandwidth. However, with modern ultrawideband receivers featuring gigahertz-scale observational bandwidths, this approach may overlook a significant number of events. We investigate the efficacy of sub-banded searches for FRBs, whereby we look for bursts within limited portions of the bandwidth. The aim of these searches is to enhance the significance of FRB detections by mitigating the impact of noise outside the targeted frequency range, thereby improving signal-to-noise ratios. We conducted a series of Monte Carlo simulations for the $400$-MHz bandwidth Parkes 21-cm multi-beam (PMB) receiver system and the Parkes Ultra-Wideband Low (UWL) receiver, simulating bursts down to frequency widths of about $100$\,MHz. Additionally, we performed a complete reprocessing of the high-latitude segment of the High Time Resolution Universe South survey (HTRU-S) of the Parkes-Murriyang telescope using sub-banded search techniques. Simulations reveal that a sub-banded search can enhance the burst search efficiency by $67_ $\,<!PCT!> for the PMB system and $\,<!PCT!> for the UWL receiver. Furthermore, the reprocessing of HTRU led to the confident detection of 18 new bursts, nearly tripling the count of FRBs found in this survey. These results underscore the importance of employing sub-banded search methodologies to effectively address the often modest spectral occupancy of these signals.

Enabling analytical power calculations for multilevel models with autocorrelated errors through deriving and approximating the precision matrix.

To unravel how within-person psychological processes fluctuate in daily life, and how these processes differ between persons, intensive longitudinal (IL) designs in which participants are repeatedly measured, have become popular. Commonly used statistical models for those designs are multilevel models with autocorrelated errors. Substantive hypotheses of interest are then typically investigated via statistical hypotheses tests for model parameters of interest. An important question in the design of such IL studies concerns the determination of the number of participants and the number of measurements per person needed to achieve sufficient statistical power for those statistical tests. Recent advances in computational methods and software have enabled the computation of statistical power using Monte Carlo simulations. However, this approach is computationally intensive and therefore quite restrictive. To ease power computations, we derive simple-to-use analytical formulas for multilevel models with AR(1) within-person errors. Analytic expressions for a model family are obtained via asymptotic approximations of all sample statistics in the precision matrix of the fixed effects. To validate this analytical approach to power computation, we compare it to the simulation-based approach via a series of Monte Carlo simulations. We find comparable performances making the analytic approach a useful tool for researchers that can drastically save them time and resources.

Economic losses due to foot-and-mouth disease (FMD) in Ethiopian cattle

Ethiopia’s cattle population is among the largest in Africa and is burdened by frequent foot-and-mouth disease (FMD) outbreaks. FMD is caused by several distinct and highly contagious viral strains that can result in acute disease in cattle, causing losses in productivity and impeding international trade. This economic simulation study considered four main sources of losses due to FMD in cattle: reduced milk yield, draft power yield, fertility, and increased mortality. Economic losses were estimated per case across age-sex strata in 89 Ethiopian administrative zones for the years 2010–2021 using a wide range of data to estimate distributions for 30 input variables in a series of Monte Carlo simulations. It was estimated that an average case of FMD in Ethiopian cattle results in losses (mean values reported followed 95 % confidence intervals in brackets) of US dollars (USD) 11 (USD 7–USD 16) per case. Losses resulting from an average outbreak were estimated to be USD 2300 (USD 1400–USD 3300), while national annual losses were estimated to be USD 0.9 Mil. (USD 0.2 Mil.–USD 2.3 Mil.). Per cow-year, based on a national cow population of approximately 39 Mil. head, these estimated annual losses are equivalent to losses of only USD 0.02 (USD 0.01–USD 0.06). Nationally, these losses were significantly less than previously estimated in the literature, with currently estimated losses more accurately reflecting the economic burden of FMD in Ethiopian cattle over the past decade. The relatively small estimated losses suggest that control efforts based on widespread vaccination in countries with primarily extensive cattle production systems, such as Ethiopia, are unlikely to be economically sound. Sensitivity analyses suggested losses would be far greater in intensive systems, and that certainty surrounding incidence rates is paramount to the formulation of economically sound animal healthpolicy in regions with endemic FMD.

Global losses due to dairy cattle diseases: A comorbidity-adjusted economic analysis

An economic simulation was carried out over 183 milk-producing countries to estimate the global economic impacts of 12 dairy cattle diseases and health conditions: mastitis (subclinical and clinical), lameness, paratuberculosis (Johne's disease), displaced abomasum, dystocia, metritis, milk fever, ovarian cysts, retained placenta, and ketosis (subclinical and clinical). Estimates of disease impacts on milk yield, fertility, and culling were collected from the literature, standardized, meta-analyzed using a variety of methods ranging from simple averaging to random-effects models, and adjusted for comorbidities to prevent overestimation. These comorbidity-adjusted disease impacts were then combined with a set of country-level estimates for lactational incidence or prevalence or both, herd characteristics, and price estimates within a series of Monte Carlo simulations that estimated and valued the economic losses due to these diseases. It was estimated that total annual global losses are US$65 billion (B). Subclinical ketosis, clinical mastitis, and subclinical mastitis were the costliest diseases modeled, resulting in mean annual global losses of approximately US$18B, US$13B, and US$9B, respectively. Estimated global annual losses due to clinical ketosis, displaced abomasum, dystocia, lameness, metritis, milk fever, ovarian cysts, paratuberculosis, and retained placenta were estimated to be US$0.2B, US$0.6B, US$0.6B, US$6B, US$5B, US$0.6B, US$4B, US$4B, and US$3B, respectively. Without adjustment for comorbidities, when statistical associations between diseases were disregarded, mean aggregate global losses would have been overestimated by 45%. Although annual losses were greatest in India (US$12B), the United States (US$8B), and China (US$5B), depending on the measure of losses used (losses as a percentage of gross domestic product, losses per capita, losses as a percentage of gross milk revenue), the relative economic burden of these dairy cattle diseases across countries varied markedly.

Multilevel Latent Differential Structural Equation Model with Short Time Series and Time-Varying Covariates: A Comparison of Frequentist and Bayesian Estimators

Continuous-time modeling using differential equations is a promising technique to model change processes with longitudinal data. Among ways to fit this model, the Latent Differential Structural Equation Modeling (LDSEM) approach defines latent derivative variables within a structural equation modeling (SEM) framework, thereby allowing researchers to leverage advantages of the SEM framework for model building, estimation, inference, and comparison purposes. Still, a few issues remain unresolved, including performance of multilevel variations of the LDSEM under short time lengths (e.g., 14 time points), particularly when coupled multivariate processes and time-varying covariates are involved. Additionally, the possibility of using Bayesian estimation to facilitate the estimation of multilevel LDSEM (M-LDSEM) models with complex and higher-dimensional random effect structures has not been investigated. We present a series of Monte Carlo simulations to evaluate three possible approaches to fitting M-LDSEM, including: frequentist single-level and two-level robust estimators and Bayesian two-level estimator. Our findings suggested that the Bayesian approach outperformed other frequentist approaches. The effects of time-varying covariates are well recovered, and coupling parameters are the least biased especially using higher-order derivative information with the Bayesian estimator. Finally, an empirical example is provided to show the applicability of the approach.

A multivariate spatial and spatiotemporal ARCH Model

This paper introduces a multivariate spatiotemporal autoregressive conditional heteroscedasticity (ARCH) model based on a vec-representation. The model includes instantaneous spatial autoregressive spill-over effects, as they are usually present in geo-referenced data. Furthermore, spatial and temporal cross-variable effects in the conditional variance are explicitly modelled. We transform the model to a multivariate spatiotemporal autoregressive model using a log-squared transformation and derive a consistent quasi-maximum-likelihood estimator (QMLE). For finite samples and different error distributions, the performance of the QMLE is analysed in a series of Monte-Carlo simulations. In addition, we illustrate the practical usage of the new model with a real-world example. We analyse the monthly real-estate price returns for three different property types in Berlin from 2002 to 2014. We find weak (instantaneous) spatial interactions, while the temporal autoregressive structure in the market risks is of higher importance. Interactions between the different property types only occur in the temporally lagged variables. Thus, we see mainly temporal volatility clusters and weak spatial volatility spillovers.

Bootstrapping Long-Run Covariance of Stationary Functional Time Series

A key summary statistic in a stationary functional time series is the long-run covariance function that measures serial dependence. It can be consistently estimated via a kernel sandwich estimator, which is the core of dynamic functional principal component regression for forecasting functional time series. To measure the uncertainty of the long-run covariance estimation, we consider sieve and functional autoregressive (FAR) bootstrap methods to generate pseudo-functional time series and study variability associated with the long-run covariance. The sieve bootstrap method is nonparametric (i.e., model-free), while the FAR bootstrap method is semi-parametric. The sieve bootstrap method relies on functional principal component analysis to decompose a functional time series into a set of estimated functional principal components and their associated scores. The scores can be bootstrapped via a vector autoregressive representation. The bootstrapped functional time series are obtained by multiplying the bootstrapped scores by the estimated functional principal components. The FAR bootstrap method relies on the FAR of order 1 to model the conditional mean of a functional time series, while residual functions can be bootstrapped via independent and identically distributed resampling. Through a series of Monte Carlo simulations, we evaluate and compare the finite-sample accuracy between the sieve and FAR bootstrap methods for quantifying the estimation uncertainty of the long-run covariance of a stationary functional time series.

Bias correction based on AR model in spurious regression

<abstract><p>The regression of mutually independent time series, whether stationary or non-stationary, will result in autocorrelation in the random error term. This leads to the over-rejection of the null hypothesis in the conventional t-test, causing spurious regression. We propose a new method to reduce spurious regression by applying the Cochrane-Orutt feasible generalized least squares method based on a bias-corrected method for a first-order autoregressive model in finite samples. This method eliminates the requirements for a kernel function and bandwidth selection, making it simpler to implement than the traditional heteroskedasticity and autocorrelation consistent method. A series of Monte Carlo simulations indicate that our method can decrease the probability of spurious regression among stationary, non-stationary, or trend-stationary series within a sample size of 10–50. We applied this proposed method to the actual data studied by Yule in 1926, and found that it can significantly minimize spurious regression. Thus, we deduce that there is no significant regressive relationship between the proportion of marriages in the Church of England and the mortality rate in England and Wales.</p></abstract>

Challenges of incorporating wounded personnel into small arms combat simulations

Small arms combat modeling is one method to describe raw human performance data in terms of lethality. This process uses a series of Monte Carlo simulations based on observed data to convert measurements of speed and accuracy into a quantifiable chance of winning a combat engagement. A major issue within these modeling efforts involves the assumptions of incorporating wounded personnel. Realistic combat will have scenarios where shots fired strike adversaries without killing them, and therefore, this element cannot be ignored. However, there are at least four significant assumptions made during the modeling and simulation effort when incorporating wounded personnel: (1) assigning damage inflicted by shots, (2) tracking wounded personnel, (3) reducing combat effectiveness of wounded personnel, and (4) burdening other fighters in the simulation. Here, we outline the challenges posed by each assumption and discuss possible solutions. Whatever the final decision for a particular modeling effort, the assumptions made should always be clearly documented in the “Methods” section. Wounded personnel will likely require several such assumptions be made that could affect the outcome; nevertheless, wounded personnel should be represented in some capacity in any small arms combat modeling effort.

Joint estimation of Ising model parameters with Hamiltonian constraint

We propose a new method for the joint estimation of parameters of the 2D Ising model. Our estimation method is the solution to the constrained optimization problem in which the objective function is a pseudo-log-likelihood and the constraint is the Hamiltonian of the external field. We used a series of Monte Carlo simulations with different shapes and sizes of our models to evaluate the behavior of a method without a Hamiltonian constraint and a method with it. We observe that both methods remain consistent with an increased number of parameters and our estimation method tends to deliver a lower variance across all model shapes and sizes compared to a simple pseudo-maximum likelihood.

Monte Carlo simulations of the underwater detection of illicit war remnants with neutron-based sensors

In recent years, the demand for accurate detection and identification of hazardous substances in an aquatic environment, especially in the Baltic Sea, has seen a significant rise, with a specific focus on unexploded ordnance (UXO) containing conventional explosives and various chemical agents, including, but not limited to, mustard gas, Clark I and II and other lethal compounds. These substances pose a significant threat to human health and the environment, and their identification is crucial for effective demining and environmental protection efforts. In this article, a novel approach for fast, remote, and non-destructive recognition of dangerous substances based on a SABAT sensor installed on an ROV is described. The performance of the proposed neutron-based sensor in an aquatic environment was verified based on a series of Monte Carlo simulations for mustard gas, Clark I and II, and TNT, as they are the most common chemical threats at the bottom of the Baltic Sea. The sensor’s ability to accurately discriminate hazardous and non-hazardous materials is described in the paper in terms of the ratio of chlorine to hydrogen (Cl/H), carbon to oxygen (C/O), and nitrogen to hydrogen (N/H) activation lines integrals. The authors also discussed the future directions of work to validate SABAT (Stoichiometry Analysis By Activation Techniques) sensors in the operational environment.

Unfolding the Neutron Flux Spectrum on the Surface of Mars Using the MSL‐RAD and Odyssey‐HEND Data

AbstractUnderstanding the long‐term radiation environment at the surface of Mars allows us to estimate the exposure for future robotic and crewed missions. Typically, the radiation environment includes charged particles (i.e., protons and heavier ions) and neutral particles (i.e., gamma rays and secondary neutrons). Previous studies used in‐situ measurements, models, or both to determine the characteristics of the radiation at Mars. For example, the Mars Science Laboratory instrument, the Radiation Assessment Detector (RAD), has provided invaluable in‐situ data since landing in 2012. However, the RAD instrument is only sensitive to neutrons with energies > ∼6 MeV and therefore misses what is expected to be a substantial flux of lower‐energy neutrons. To address this gap, we have developed an approach to derive the surface neutron spectrum using the MSL RAD data augmented by orbital data from the High Energy Neutron Detector (HEND) onboard Mars Odyssey (neutron energy < ∼10 MeV). Using a power law fit, we determine neutron flux spectra that reproduce the measurements recorded by both RAD and HEND. Our approach involves a series of Monte Carlo simulations to develop a set of atmospheric transmission functions that enables us to convert the on‐orbit HEND data to their corresponding surface neutron flux spectra. The combined RAD—HEND data present a unique opportunity to obtain a complete picture of the surface neutron environment.

Unveiling the similarities and dissimilarities between dynamic and thermodynamic phase transitions in a magnetic binary alloy system: a Monte Carlo study

Being inspired by the results of recent experimental work ‘Phys. Rev. E 102 022 804’, we perform a series of Monte Carlo simulations to investigate the dynamic critical properties of a binary magnetic alloy system composed of two different components with different spin moments with the chemical formula . In the first part of the work, we concentrate our attention on the similarities between dynamic critical behavior and thermodynamic phase transition properties of the model. Finite-size scaling analyses suggest that the proposed binary alloy model exhibits 2D Ising universality class. In the second part, we examine the dynamic critical behavior in the presence of a magnetic bias field for which a metamagnetic sideband behavior originates in dynamic susceptibility curves. Our results yield that these metamagnetic anomalies survive for a wide range of magnetic atom concentration p A , and the strength of the anomalies observed in the field-dependent dynamic susceptibility varies very slowly with the T/T c ratio of the binary magnetic alloy.

Effective permeability of fractured porous media with fracture density near the percolation threshold

This study investigates the effective permeability of fractured porous media with fracture density near the percolation threshold. A new equivalent matrix-fracture network (EMFN) model was introduced, the code was used to efficiently deal with the huge number of modeling works for steady state flow in fractured porous media. The numerical model was verified by comparing with two previous seepage methods. We conducted a series of Monte Carlo simulations to explore the relationship between permeability discontinuity and three factors, namely fracture density, contrast between fracture and matrix permeability, and the scale of research domain. It was found that the effective permeability presents a bimodal distribution with specified fracture density in the transition regime. Each bimodal function can be subdivided into two well-fitting log-normal functions according to the percolation state to obtain analyzable average permeabilities. The results revealed significant differences between percolating and non-percolating configurations in the response to the change of each of the three factors mentioned above. More importantly, the finite-size effect plays an important role when fracture density is lower than or close to the percolation threshold; but this effect disappears gradually as the increase of fracture density. A stereoscopic approach is introduced to rationalize the scale effect.

Comparing Fisher information and Bayesian statistical approaches on measuring the Hubble Constant from binary black hole merger gravitational wave signals

Gravitational waves from binary black-hole mergers can be used to provide distance and angular location information of these sources, which, upon comparison with galaxy catalogs, can be used to measure the Hubble Constant. For a given galaxy catalog, and binary merger rates associated with each galaxy, a Fisher information approach can be used to estimate the measurement error for the Hubble Constant from the dark siren approach. In this paper, we validate the predictions from the Fisher information approach by carrying out a series of Monte Carlo simulations. Except in situations with very low event rates, the Fisher information approach is shown to predict the statistical errors of the Bayesian approach with good accuracy.

Testing for short explosive bubbles: A case of Brent oil futures price

This paper considers the problem of detecting short explosive bubbles in financial data. Based on the backward sup Dickey-Fuller (BSDF), we propose a modified version of BSDF, namely mBSDF. We define the dating statistics by adding a modified term to the BSDF, enhancing the bubble emergence signal. We demonstrate the asymptotic distribution and the bubble duration estimates. A series of Monte Carlo simulations show that mBSDF significantly outperforms BSDF for shorter bubbles, and mBSDF can improve the bubble detection rate by up to 22.7%. As an empirical application, we apply the methods to the Brent crude oil futures price and the results confirm that mBSDF detects the latest oil spike shocked by the Russia-Ukraine conflict and almost all periods of explosive bubbles the oil market has experienced in recent years in contrast to BSDF, which further supports the superiority of mBSDF.

Hyperia: A novel methodology of developing anthropomorphic breast phantoms for X-ray imaging modalities - Part I: Concept and initial findings.

To introduce a novel methodology for developing anthropomorphic breast phantoms for use in X-ray-based imaging modalities. "Hyperization" is a quasi-stippling mapping operation in which regions of varying grayscale values in a 2D image are transformed into regions of varying holes on a surface. The holes can be cut or engraved on the sheet of paper using a high-resolution laser cutter/engraver. In hyperization, the main parameters are the size and the distance between the holes. Here, we introduce the concept and chronicle the development and characterization of a proof-of-concept prototype. In this study, we hypothesized that a resulting "Hyperia" phantom would be a realistic representative of a patient's breast tissue: it would exhibit similar X-ray properties and show textural complexities. We used breast computed tomography (bCT) images of real patients as the input models. Using a previously developed segmentation method, the input CT images were segmented into different tissue classes (skin, adipose, and fibroglandular). The segmented images were then "Hyperized". A series of Monte Carlo simulations were conducted to find the optimal hyperization parameters. Different laser cutter/engraver systems and substrate materials were explored to find a viable option for developing an entire Hyperia breast phantom. The resulting phantom was imaged on a prototype breast CT system, and the resulting images were evaluated based on physical properties and similarity to the original patient data. The simulation results indicate close similarities - both in the distribution of different tissue types and the resulting CT numbers - between the patient bCT image and the bCT of the Hyperia phantom, regardless of the breast size and density: the Pearson correlation coefficient (ρ) ranged from 0.88 in a BIRADS A breast to 0.94 in BIRADS C and D breasts (ρ of 1.00 suggests perfect structural similarity), and the volumetric mean squared error ranged from 0.0033 (in BIRADS D breast) to 0.0059 (in BIRADS A), suggesting good agreement between the resulting CT numbers. For fabricating the slices, the office paper was found to be an optimal substrate material, with the Hyperization parameters of (α, β) = (0.200mm, 0.400mm). A novel phantom can be used for X-ray-based breast cancer imaging systems. The main advantage is that only one material is used for creating a contrast between different tissue types in an image.

Modified Heider balance on Erdös-Rényi networks.

The lack of signed random networks in standard balance studies has prompted us to extend the Hamiltonian of the standard balance model. Random networks with tunable parameters are suitable for better understanding the behavior of standard balance as an underlying dynamics. Moreover, the standard balance model in its original form does not allow preserving tensed triads in the network. Therefore, the thermal behavior of the balance model has been investigated on a fully connected signed network recently. It has been shown that the model undergoes an abrupt phase transition with temperature. Considering these two issues, we examine the thermal behavior of the structural balance model defined on Erdös-Rényi random networks within the range of their connected regime. We provide a mean-field solution for the model. We observe a first-order phase transition with temperature for a wide range of connection probabilities. We detect two transition temperatures, T_{cold} and T_{hot}, characterizing a hysteresis loop. We find that with decreasing the connection probability, both T_{cold} and T_{hot} decrease. However, the slope of decreasing T_{hot} with decreasing connection probability is larger than the slope of decreasing T_{cold}. Hence, the hysteresis region gets narrower until it disappears in a certain connection probability. We provide a phase diagram in the temperature-tie density plane to accurately observe the metastable or coexistence region behavior. Then we justify our mean-field results with a series of Monte Carlo simulations.

Covariate Balance for Observational Effectiveness Studies: A Comparison of Matching and Weighting

Propensity score matching and weighting methods are often used in observational effectiveness studies to reduce imbalance between treated and untreated groups on a set of potential confounders. However, much of the prior methodological literature on matching and weighting has yet to examine performance for scenarios with a majority of treated units, as is often encountered with programs and interventions that have been widely disseminated or “scaled-up.” Using a series of Monte Carlo simulations, we compare the performance of k:1 matching with replacement and weighting methods with respect to covariate balance, bias, and mean squared error. Results indicate that the accuracy of all methods declined as treatment prevalence increased. While weighting produced the largest reduction in covariate imbalance, 1:1 matching with replacement provided the most unbiased treatment effect estimates. An applied example using empirical school-level data is provided to further illustrate the application and interpretation of these methods to a real-world scale-up effort. We conclude by considering the implications of propensity score methods for observational effectiveness studies with a particular focus on educational research.

SIMULTANEOUS CONFIDENCE BANDS FOR CONDITIONAL VALUE-AT-RISK AND EXPECTED SHORTFALL

Conditional value-at-risk (CVaR) and conditional expected shortfall (CES) are widely adopted risk measures which help monitor potential tail risk while adapting to evolving market information. In this paper, we propose an approach to constructing simultaneous confidence bands (SCBs) for tail risk as measured by CVaR and CES, with the confidence bands uniformly valid for a set of tail levels. We consider one-sided tail risk (downside or upside tail risk) as well as relative tail risk (the ratio of upside to downside tail risk). A general class of location-scale models with heavy-tailed innovations is employed to filter out the return dynamics. Then, CVaR and CES are estimated with the aid of extreme value theory. In the asymptotic theory, we consider two scenarios: (i) the extreme scenario that allows for extrapolation beyond the range of the available data and (ii) the intermediate scenario that works exclusively in the case where the available data are adequate relative to the tail level. For finite-sample implementation, we propose a novel bootstrap procedure to circumvent the slow convergence rates of the SCBs as well as infeasibility of approximating the limiting distributions. A series of Monte Carlo simulations confirm that our approach works well in finite samples.