Monte Carlo Simulation Study Research Articles

Robust M Estimation for Poisson Panel Data Model with Fixed Effects: Method, Algorithm, Simulation, and Application

The fixed effects Poisson (FEP) model is one of the most important for the count data when the data containperiods and cross-sectional units. The maximum likelihood (ML) estimation method for the FEP model provides good results in the absence of outliers, but it is affected by outliers. So, we introduce in this paper robust estimators for the FEP model. These estimators yield stable and good results in case of the presence of outliers. The Monte Carlo simulation study and empirical application were conducted to assess the performance of the non-robust fixed Poisson maximum likelihood (FPML) estimator and the robust estimators: fixed Poisson Huber (FPHR), fixed Poisson Hampel (FPHM) and fixed Poisson Tukey (FPTK). The results of simulation and application show that robust estimators are better than FPML estimator when the count panel data contains outliers. In addition, FPTK is more efficient than other robust estimators.

Predictive Modeling of COVID-19 Intensive Care Unit Patient Flows and Nursing Complexity: A Monte Carlo Simulation Study.

This study aimed to develop a Monte Carlo simulation model to forecast the number of ICU beds needed for COVID-19 patients and the subsequent nursing complexity in a French teaching hospital during the first and second pandemic outbreaks. The model used patient data from March 2020 to September 2021, including age, sex, ICU length of stay, and number of patients on mechanical ventilation or extracorporeal membrane oxygenation. Nursing complexity was assessed using a simple scale with three levels based on patient status. The simulation was performed 1000 times to generate a scenario, and the mean outcome was compared with the observed outcome. The model also allowed for a 7-day forecast of ICU occupancy. The simulation output had a good fit with the actual data, with an R2 of 0.998 and a root mean square error of 0.22. The study demonstrated the usefulness of the Monte Carlo simulation model for predicting the demand for ICU beds and could help optimize resource allocation during a pandemic. The model's extrinsic validity was confirmed using open data from the French Public Health Authority. This study provides a valuable tool for healthcare systems to anticipate and manage surges in ICU demand during pandemics.

On Modi-Exponential Distribution under Conventional Type-I and Type-II Censoring

In this article, a new Modi-Exponential distribution is considered under Type-I and Type-II censoring. Some statistical and reliability characteristics of the distribution are specified. Statistical inferences of the Modi-Exponential parameters based on Type-I and Type-II censored data are discussed. The maximum likelihood estimation method is used for estimation of unknown parameters along with reliability characteristics. Asymptotic confidence intervals are also obtained for interval estimation. Monte-Carlo Simulation study is performed to study the performances of estimators. Further, two real data sets are provided to illustrate the results.. KEYWORDS :Censoring, Maximum likelihood estimation, Confidence bounds, Simulation.

Experimental, theoretical, and Monte Carlo simulation study of radiation shielding properties of La2O3-Added lithium borate glasses

The La2O3-added lithium borate glasses in the composition of (80-x)B2O3: xLa2O3: 10Li2O: 10Al2O3 (x = 10, 15, 20, 25, and 30 mol%) were prepared by the melt quenching process. The gamma-ray shielding properties of glasses were measured experimentally at various gamma-ray energies acquired from sources of 133Ba, 137Cs, and 60Co and compared with the FLUKA Monte Carlo simulation. FLUKA was also utilized for investigation and compared with the theoretical values obtained using the WinXCom program across the energy range from 0.015 MeV to 15 MeV. The mass attenuation coefficient (μm), effective atomic number (Zeff), effective electron density (Neff), mean free path (MFP) and half-value layer (HVL) were reported. From the results, it was found that adding La2O3 to the glass structure increased μm, Zeff, and Neff, while MFP and HVL decreased. The glasses exhibited the lowest HVL value, indicating improved shielding ability when compared with some shielding materials. PHITS has been utilized to simulate the variations in the absorbed dose resulting from interaction of protons within the modelled phantom. These findings demonstrate the suitability of La2O3-added lithium borate glasses as radiation shielding materials.

An extended Rayleigh Weibull model with actuarial measures and applications

In this article, we discuss a new extension of the Rayleigh-Weibull model using the Marshall-Olkin family of distributions. The proposed model is called the Marshall-Olkin-Rayleigh-Weibull (MORW) model. Various statistical properties of the MORW distribution are discussed, including explicit expressions for quantiles, moments, incomplete and conditional moments, some inequality measures, moment generating function, moments of the residual and reversed residual life, the Rényi entropy, and order statistics. Six different estimation methods are considered to investigate the behavior of the model parameters within the MORW model. A Monte Carlo simulation study is conducted to evaluate the performance of these different estimators. In addition, some actuarial measures, such as the Value-at-Risk, the expected shortfall, the tail Value-at-Risk, the tail variance, the tail variance premium, the tail conditional expectation, and the tail standard deviation premium, are calculated. Finally, applying the model to real data sets illustrates the applicability and usefulness of the MORW distribution.

Enhancing the Detection of Social Desirability Bias Using Machine Learning: A Novel Application of Person-Fit Indices.

Social desirability bias (SDB) is a common threat to the validity of conclusions from responses to a scale or survey. There is a wide range of person-fit statistics in the literature that can be employed to detect SDB. In addition, machine learning classifiers, such as logistic regression and random forest, have the potential to distinguish between biased and unbiased responses. This study proposes a new application of these classifiers to detect SDB by considering several person-fit indices as features or predictors in the machine learning methods. The results of a Monte Carlo simulation study showed that for a single feature, applying person-fit indices directly and logistic regression led to similar classification results. However, the random forest classifier improved the classification of biased and unbiased responses substantially. Classification was improved in both logistic regression and random forest by considering multiple features simultaneously. Moreover, cross-validation indicated stable area under the curves (AUCs) across machine learning classifiers. A didactical illustration of applying random forest to detect SDB is presented.

A new extended Pham distribution for modelling cancer data

In this study, a novel extended Pham model is offered and examined. This model can fit positive, negative, and approximately symmetric data. In addition, it features four alternative hazard rate shapes, namely increasing, decreasing, bathtub, and upside-down bathtub shapes. The new model's main properties, such as quantiles, moments, and mean deviations, are derived. To evaluate the estimation of distribution parameters, two approaches are studied. From a classical standpoint, the maximum likelihood technique is used to determine the point and approximate confidence intervals of all unknown parameters. The Bayesian estimation method, on the other hand, is considered via the Markov chain Monte Carlo technique for obtaining Bayes point estimates alongside Bayes credible intervals. We present various Monte Carlo simulation studies to investigate the efficacy of maximum likelihood and Bayes estimation findings for the model's parameters. Numerical investigations showed that the offered Bayes estimates outperformed those derived using the conventional method. We also analyzed two sets of real data taken from the medical sector; the first represents survival rates for cancer patients who received a combination of radiotherapy and chemotherapy, and the second consists of remission times for bladder cancer patients. These clinical scenarios reveal that the new model outperforms other important competing models based on specific statistical criteria. Ultimately, for both clinical data applications, the proposed extended Pham distribution is the best choice compared to the traditional Pham and the other well-known eight lifetime models.

Are the Signs of Factor Loadings Arbitrary in Confirmatory Factor Analysis? Problems and Solutions

The replication crisis in social and behavioral sciences has raised concerns about the reliability and validity of empirical studies. While research in the literature has explored contributing factors to this crisis, the issues related to analytical tools have received less attention. This study focuses on a widely used analytical tool - confirmatory factor analysis (CFA) - and investigates one issue that is typically overlooked in practice: accurately estimating factor-loading signs. Incorrect loading signs can distort the relationship between observed variables and latent factors, leading to unreliable or invalid results in subsequent analyses. Our study aims to investigate and address the estimation problem of factor-loading signs in CFA models. Based on an empirical demonstration and Monte Carlo simulation studies, we found current methods have drawbacks in estimating loading signs. To address this problem, three solutions are proposed and proven to work effectively. The applications of these solutions are discussed and elaborated.

Monte Carlo simulation study of the structure of calcium and potassium chlorides ternary solutions

ABSTRACT Monte Carlo simulations were conducted on pure water and aqueous calciumand potassium chloride ternary solutions at different salts concentrations. To assess the local order within the solutions, the Monte Carlo simulations utilised the Madrid-2019 force field, usingtheTIP4P/2005 water model and scaled charges. The results demonstrate that as the salt concentrations increase, there is a notable decrease in the contribution of the structured tetrahedral arrangement of water molecules, favouring an unstructured configuration instead. The use of the nearest neighbour approach, provided unambiguous information regarding the radial and orientation characteristics of the unstructured configuration of water. Structures of the solvation shells around ions are also studied. The Monte Carlo simulations indicate that as the salt concentration increases, the hydration number around the cation decreases. This effect is likely due to the occasional substitution of some water molecules in the first hydration shell by chlorine ions.

On the Stress-Strength Reliability of Transmuted GEV Random Variables with Applications to Financial Assets Selection.

In reliability contexts, probabilities of the type R=P(X<Y), where X and Y are random variables, have shown to be useful tools to compare the performance of these stochastic entities. By considering that both X and Y follow a transmuted generalized extreme-value (TGEV) distribution, new analytical relationships were derived for R in terms of special functions. The results hereby obtained are more flexible when compared to similar results found in the literature. To highlight the applicability and correctness of our results, we conducted a Monte-Carlo simulation study and investigated the use of the reliability measure P(X<Y) to select among financial assets whose returns were characterized by the random variables X and Y. Our results highlight that R is an interesting alternative to modern portfolio theory, which usually relies on the contrast of involved random variables by a simple comparison of their means and standard deviations.

Exploring the efficacy of novel thiohydantoin derivatives with arylacetamide core as corrosion inhibitors for mild steel in acidic medium: insights from electrochemical analysis, comprehensive characterization, and quantum studies

Two new compounds of Thiohydantoin Derivatives N-(4-nitrophenyl)-2-((5-oxo-4,4-diphenyl-4,5-dihydro-1H-imidazole-2-yl)thio) acetamide and N-(4-fluorophenyl)-2-((5-oxo-4,4-diphenyl-4,5-dihydro-1H-imidazole-2-yl)thio) acetamide symbolized by NNOITAcetamide and N-FOITAcetamide respectively, were synthesized and characterized by NMR spectroscopy. Their effectiveness in inhibiting mild steel (M-S) corrosion in 1.0 M HCl at 298 K was investigated by potentiodynamic polarisation (PDP), electrochemical impedance spectroscopy (EIS), electrochemical frequency modulation (EFM), and surface examinations via energy dispersive spectroscopy (EDS), and scanning electron microscopy (SEM) as well as X-ray photoelectron spectroscopy (XPS). Experimental results have been supported by theoretical studies such as Density Functional Theory (DFT) calculations and Monte Carlo (MC) simulation studies. All findings indicate that inhibitors have a considerable impact on lowering the rate of corrosion, and that the efficiency of the inhibitors rises with the concentration of the inhibitor. Experimental results revealed that the efficacy of the new thiohydantoin derivatives with an arylacetamide nucleus increased with increasing inhibitor concentration, where their inhibition efficiencies reached maximum values of 92.1 %, 94.1 %, for NNOITAcetamide and N-FOITAcetamide, at 10−3 M, respectively. In addition, the icorr decreased with increasing concentration up to 85 µA cm−2 and 58 for NNOITAcetamide and N-FOITAcetamide respectively, with a blank as a reference of 1045 µA cm−2, while the efficacy reduces as the temperature increases. The activation and thermodynamic parameters indicated that these products act via chemical adsorption on the mild steel surface. Furthermore, the surface analysis (SEM, EDS, and XPS) confirmed the formation of a protective layer adsorbed on the mild steel surface. Polarization curves indicated that NNOITAcetamide and N-FOITAcetamide are mixed-type inhibitors. Moreover, the quantum chemical calculation well supports the experimental results.

Parameter Estimation in Spatial Autoregressive Models with Missing Data and Measurement Errors

This study addresses the problem of parameter estimation in spatial autoregressive models with missing data and measurement errors in covariates. Specifically, a corrected likelihood estimation approach is employed to rectify the bias in the log-maximum likelihood function induced by measurement errors. Additionally, a combination of inverse probability weighting (IPW) and mean imputation is utilized to mitigate the bias caused by missing data. Under several mild conditions, it is demonstrated that the proposed estimators are consistent and possess oracle properties. The efficacy of the proposed parameter estimation process is assessed through Monte Carlo simulation studies. Finally, the applicability of the proposed method is further substantiated using the Boston Housing Dataset.

Effect of particle size on liver MRI relaxometry: Monte Carlo simulation and phantom studies.

To investigate the effect of particle size on liver by Monte Carlo simulation and phantom studies at both 1.5 T and 3.0 T. Two kinds of particles (i.e., iron sphere and fat droplet) with varying sizes were considered separately in simulation and phantom studies. MRI signals were synthesized and analyzed for predicting , based on simulations by incorporating virtual liver model, particle distribution, magnetic field generation, and proton movement into phase accrual. In the phantom study, iron-water and fat-water phantoms were constructed, and each phantom contained 15 separate vials with combinations of five particle concentrations and three particle sizes. measurements in the phantom were made at both 1.5 T and 3.0 T. Finally, differences in predictions or measurements were evaluated across varying particle sizes. In the simulation study, strong linear and positively correlated relationships were observed between predictions and particle concentrations across varying particle sizes and magnetic field strengths ( ). The relationships were affected by iron sphere size ( ), where smaller iron sphere size yielded higher predicted , whereas fat droplet size had no effect on predictions ( ) for constant total fat concentration. Similarly, the phantom study showed that measurements were relatively sensitive to iron sphere size ( ) unlike fat droplet size ( ). Liver is affected by iron sphere size, but is relatively unaffected by fat droplet size. These findings may lead to an improved understanding of the underlying mechanisms of relaxometry in vivo, and enable improved quantitative MRI phantom design.

Multiscale SUR Estimation of Systematic Risk

Abstract We propose a multiscale version of the seemingly unrelated regressions model, based on wavelet transform-based time series observations. Each regression equation refers to a different time scale, which enables the use of across-scale error covariances in the feasible GLS estimation procedure for efficiency gains. We demonstrate the advantages of the proposed method over OLS with two studies: an empirical study using stock market returns for the main US industrial sectors and a detailed Monte Carlo simulation study with alternative wavelet filters. We also provide explanations for the suitability of the proposed method for estimating long-term systematic risk.

Parametric estimations of the mean value and variance functions in geometric process

The paper introduces novel parametric estimators for the mean value function (MVF) and variance function (VF) in geometric processes (GPs) based on a random sample which belongs to a GP. Specifically, it addresses scenarios where the distribution function of the first interarrival time of GP is analytically known, but its parameters, along with the ratio parameter of the GP, are unknown. It establishes a solid theoretical foundation for the estimators proposed and investigates some statistical properties such as strong consistency and asymptotic unbiasedness of the estimators. These results generalize some of the findings acquired in previous studies. To evaluate the performance of the proposed estimators, we conduct a comprehensive Monte Carlo simulation study. This involves using various lifetime distributions, such as gamma, Weibull, and lognormal considering different sample sizes and parameter values. A discussion of an application to software engineering is also provided to illustrate the applicability of the methods developed in this study.

A new kernel two-parameter prediction under multicollinearity in partially linear mixed measurement error model

A Partially linear mixed effects model relating a response Y to predictors ( X , Z , T ) with the mean function X T β + Zb + g ( T ) is considered in this paper. When the parametric parts' variable X are measured with additive error and there is ill-conditioned data suffering from multicollinearity, a new kernel two-parameter prediction method using the kernel ridge and Liu regression approach is suggested. The kernel two parameter estimator of β and the predictor of b are derived by modifying the likelihood and Henderson methods. Matrix mean square error comparisons are calculated. We also demonstrate that under suitable conditions, the resulting estimator of β is asymptotically normal. The situation with an unknown measurement error covariance matrix is handled. A Monte Carlo simulation study, together with an earthquake data example, is compiled to evaluate the effectiveness of the proposed approach at the end of the paper.

Unraveling the influence of surface functionalities on gas Physisorption: A comprehensive study on SBA-15 nanoporous material from Monte Carlo simulation for improved Textural-Energetic characterization

In this study, we conducted experimental and Monte Carlo simulation studies in the grand canonical ensemble (GCMC) to investigate the role of molecular orientation and surface heterogeneity on the adsorption of N2 at 77 K. Our research focused on a series of ordered nanoporous materials (SBA-15) with varying degrees of oxygen functionalities. Specifically, we examined the effects of surface heterogeneity on the calculation of pore size distribution (PSD) and the Brunauer-Emmett-Teller (BET) area of porous materials. To provide a comprehensive perspective, we compared our results with three levels of surface oxidation, including a pristine case without any surface oxidation.The results from both our experimental and simulation data reveal the importance of chemical heterogeneity in determining equilibrium properties such as molecular packing within the pores, differential enthalpies of adsorption, and N2 orientation distribution. Our findings suggest that accurate characterization of surface heterogeneity is crucial for understanding gas adsorption in nanoporous materials and for developing better models for predicting their performance in various applications. Moreover, our simulations revealed substantial changes in the molecular orientation of adsorbate particles with increasing surface heterogeneity. This insight provides valuable information about the behavior of molecules within the nanoporous materials, further enhancing our understanding of the complex adsorption processes in these systems.

Monte Carlo simulation study on the reliability of concrete-filled HSS beam-column design provisions

Revisions were recently proposed to the way in which concrete-filled hollow structural section members are handled in CSA S16. These revisions were based on previous research, comparisons to experiments, and a first-order reliability method analysis of existing provisions for compression and flexural members. In this paper, this topic is further expanded by using Monte Carlo Simulations (MCS) to determine the inherent reliability of the previous and new design rules for concrete-filled rectangular hollow section (RHS) and circular hollow section (CHS) beam-columns. A representative set of concrete-filled RHS and CHS members with variations in concrete strength, wall slenderness, effective length, and loading eccentricity are analyzed. Using MCS, the reliability index ( β+ ) of each is determined over a range of live-to-dead load ratios. Inherent β+ values are compared to the code-specified target (i.e., β+ = 3.0 per Annex B of CSA S16) and the CSA S16:19 and AISC 360-16 provisions.

Linear Mixed-Effects Models for Dependent Data: Power and Accuracy in Parameter Estimation

Linear mixed-effects models have been increasingly used to analyze dependent data in psychological research. Despite their many advantages over ANOVA, critical issues in their analyses remain. Due to increasing random effects and model complexity, estimation computation is demanding, and convergence becomes challenging. Applied users need help choosing appropriate methods to estimate random effects. The present Monte Carlo simulation study investigated the impacts when the restricted maximum likelihood (REML) and Bayesian estimation models were misspecified in the estimation. We also compared the performance of Akaike information criterion (AIC) and deviance information criterion (DIC) in model selection. Results showed that models neglecting the existing random effects had inflated Type I errors, unacceptable coverage, and inaccurate R-squared measures of fixed and random effects variation. Furthermore, models with redundant random effects had convergence problems, lower statistical power, and inaccurate R-squared measures for Bayesian estimation. The convergence problem is more severe for REML, while reduced power and inaccurate R-squared measures were more severe for Bayesian estimation. Notably, DIC was better than AIC in identifying the true models (especially for models including person random intercept only), improving convergence rates, and providing more accurate effect size estimates, despite AIC having higher power than DIC with 10 items and the most complicated true model.

Comparing the Clique Percolation algorithm to other overlapping community detection algorithms in psychological networks: A Monte Carlo simulation study

In psychological networks, one limitation of the most used community detection algorithms is that they can only assign each node (symptom) to a unique community, without being able to identify overlapping symptoms. The clique percolation (CP) is an algorithm that identifies overlapping symptoms but its performance has not been evaluated in psychological networks. In this study, we compare the CP with model parameters chosen based on fuzzy modularity (CPMod) with two other alternatives, the ratio of the two largest communities (CPRat), and entropy (CPEnt). We evaluate their performance to: (1) identify the correct number of latent factors (i.e., communities); and (2) identify the observed variables with substantive (and equally sized) cross-loadings (i.e., overlapping symptoms). We carried out simulations under 972 conditions (3x2x2x3x3x3x3): (1) data categories (continuous, polytomous and dichotomous); (2) number of factors (two and four); (3) number of observed variables per factor (four and eight); (4) factor correlations (0.0, 0.5, and 0.7); (5) size of primary factor loadings (0.40, 0.55, and 0.70); (6) proportion of observed variables with substantive cross-loadings (0.0%, 12.5%, and 25.0%); and (7) sample size (300, 500, and 1000). Performance was evaluated through the Omega index, Mean Bias Error (MBE), Mean Absolute Error (MAE), sensitivity, specificity, and mean number of isolated nodes. We also evaluated two other methods, Exploratory Factor Analysis and the Walktrap algorithm modified to consider overlap (EFA-Ov and Walk-Ov, respectively). The Walk-Ov displayed the best performance across most conditions and is the recommended option to identify communities with overlapping symptoms in psychological networks.