Monte Carlo Simulation Study Research Articles

Competitive Adsorption of Trace Gases on Ice at Tropospheric Temperatures: A Grand Canonical Monte Carlo Simulation Study.

The coadsorption of two atmospheric trace gases on ice is characterized by using, for the first time, grand canonical Monte Carlo (GCMC) simulations performed in conditions similar to those of the corresponding experiments. Adsorption isotherms are simulated at tropospheric temperatures by considering two different gas mixtures of 1-butanol and acetic acid molecules, and selectivity of the ice surface with respect to these species is interpreted at the molecular scale as resulting from a competition process between these molecules for being adsorbed at the ice surface. It is thus shown that the trapping of acetic acid molecules on ice is always favored with respect to that of 1-butanol at low pressures, corresponding to low coverage of the surface, whereas the adsorption of the acid species is significantly modified by the presence of the alcohol molecules in the saturated portion of the adsorption isotherm, in accordance with the experimental observations. The present GCMC simulations thus confirm that competitive adsorption effects have to be taken into consideration in real situations when gas mixtures present in the troposphere interact with the surface of ice particles.

Hydrogen adsorption in Si-LTA and LTA-4A zeolites: A Gibbs Ensemble Monte Carlo simulation study

Storing a targeted amount of hydrogen is one of the most challenging tasks. Several porous materials are being tested for the physisorption of hydrogen. We perform, NPT-Gibbs Ensemble Monte Carlo Simulation of hydrogen storage inside two zeolites: Si-LTA and LTA-4A. Classical three-site Silvera-Goldman potential is used to model hydrogen molecules. Gravimetric densities, radial distribution functions, adsorption enthalpies and specific regions for hydrogen are analyzed in detail in this study. Although number of hydrogen molecules stored in LTA-4A is more compared to Si-LTA, their gravimetric densities are comparable. At 77 K and 200 bar, almost 3 wt% of hydrogen can be stored in these zeolites. Introduction of sodium ions enhance the number of hydrogen molecules adsorbed in LTA-4A. Because of the presence of sodium ions at the windows of the cages, it can store these extra hydrogens inside it. It is also observed that for both these zeolites, preferred adsorption sites are always α-cages compared to the channels or β-cages. Replacement of silicon atoms with aluminium does not have any change on the uptake, but the presence of sodium counter ions increase the uptake. At low loading, enthalpies of adsorption are enhanced by 2 kJ/mol, because of the counter ions. Even though hydrogen uptake is less in these zeolites, we find important aspects of hydrogen adsorption in general which will help to devise porous materials with higher storing capabilities.

A Monte-Carlo simulation study for design of brain dedicated TOF-PET

Brain positron emission tomography (PET) has several advantages, including improved sensitivity and spatial resolution compared to whole-body PET, which provides a more accurate diagnosis and observation of metabolic activity in the brain. However, the small diameter and design parameter result in a high parallax error and a random count rate, leading to a deteriorated imaging performance. We designed and evaluated brain PET with various design parameters and applied time-of-flight (TOF) technology to overcome the above problems. The aim of the study was to design an optimal structure of the brain dedicated TOF-PET that provides excellent image quality. This study investigated effects of ring diameter, crystal length, and TOF capability on the performance of brain TOF-PET using a Monte-Carlo simulation. Two brain PET were designed and evaluated, consisting of a commercially available brain PET ring diameter (357 mm) and a smaller ring diameter (257 mm) to verify the effect of ring diameter on the performance of the brain PET. Three brain TOF-PET with different crystal thicknesses (10 mm, 15 mm, and 20 mm) and TOF capacities (180 ps, 220 ps, and 260 ps) were assessed and their performances were compared. An optimization study was also performed to design the optimal structure based on the two simulation results. This study determined the optimal structure of brain-dedicated TOF-PET based on trade-offs and performance differences depending on the parameters of brain PET. As a result, we propose a brain-dedicated TOF-PET with a 15 mm crystal length and 257 mm diameter as the optimal structure. The results of this paper could be used to design PET systems for the brain and other specific organs.

Managing delayed or missed pregabalin doses in patients with focal epilepsy: a Monte Carlo simulation study.

Delayed or missed doses are inevitable in epilepsy pharmacotherapy. The current remedial measures recommended by the United States Food and Drug Administration (FDA) for non-adherence are generic and lack clinical evidence. To assess remedial strategies for delayed or missed pregabalin doses in patients with epilepsy using Monte Carlo simulations. Monte Carlo simulations were performed using a published population pharmacokinetic model for pregabalin. The applicability of five proposed remedial regimens as well as FDA recommendations was evaluated by simulating various poor adherence scenarios in eight populations, including those with renal dysfunction. All proposed remedial strategies were associated with delay duration and renal function. When delays are relatively short, an immediate regular dose is advised. The cut-off time points for taking the regular dose as a remedial regimen were 1, 2, 4, and 12h for patients with mild renal impairment and normal renal function, moderate renal impairment, severe renal impairment, and end-stage renal disease, respectively. However, when delay aligns closely with a dosing interval, a regular dose combined with a partial dose proves effective. Generally, supplementing 1.3-fold the regular dose at the next scheduled time adequately compensates for the missed dose. Model-based simulations provided quantitative evidence for the effectiveness and feasibility of remedial strategies for missed or delayed pregabalin doses.

The Logistic Burr XII Distribution: Properties and Applications to Income Data

We define and study the four-parameter logistic Burr XII distribution. It is obtained by inserting the three-parameter Burr XII distribution as the baseline in the logistic-X family and may be a useful alternative method to model income distribution and could be applied to other areas. We illustrate that the new distribution can have decreasing and upside-down-bathtub hazard functions and that its density function is an infinite linear combination of Burr XII densities. Some mathematical properties of the proposed model are determined, such as the quantile function, ordinary and incomplete moments, and generating function. We also obtain the maximum likelihood estimators of the model parameters and perform a Monte Carlo simulation study. Further, we present a parametric regression model based on the introduced distribution as an alternative to the location-scale regression model. The potentiality of the new distribution is illustrated by means of two applications to income data sets.

Cox regression with linked data.

Record linkage is increasingly used, especially in medical studies, to combine data from different databases that refer to the same entities. The linked data can bring analysts novel and valuable knowledge that is impossible to obtain from a single database. However, linkage errors are usually unavoidable, regardless of record linkage methods, and ignoring these errors may lead to biased estimates. While different methods have been developed to deal with the linkage errors in the generalized linear model, there is not much interest on Cox regression model, although this is one of the most important statistical models in clinical and epidemiological research. In this work, we propose an adjusted estimating equation for secondary Cox regression analysis, where linked data have been prepared by a third-party operator, and no information on matching variables is available to the analyst. Through a Monte Carlo simulation study, the proposed method is shown to lead to substantial bias reductions in the estimation of the parameters of the Cox model caused by false links. An asymptotically unbiased variance estimator for the adjusted estimators of Cox regression coefficients is also proposed. Finally, the proposed method is applied to a linked database from the Brest stroke registry in France.

A versatile model for lifetime of a component under stress

In this study, a versatile model, called alpha-monotone inverse Weibull distribution (alphaIW), for lifetime of a component under stress is introduced by using the alpha-monotone concept. The alphaIW distribution is also expressed as a scale-mixture between the inverse Weibull distribution and uniform distribution on (0, 1). The alphaIW distribution includes alpha-monotone inverse exponential and alpha-monotone inverse Rayleigh distributions as submodels and converenges to the inverse Weibull, inverse exponential, and inverse Rayleigh distributions as limiting cases. Also, slash Weibull, slash Rayleigh, and slash exponential distribuitons can be obtained under certain variable transformation and parameter settings. The alphaIW distribution is characterized by its hazard rate function and characterizing conditions are provided as well. Maximum likelihood, maximum product of spacing, and least squares methods are used to estimate distribution parameters. A Monte-Carlo simulation study is conducted to compare the efficiencies of the considered estimation methods. In the application part, two practical data sets, Kevlar 49/epoxy and Kevlar 373/epoxy, are modeled via the alphaIW distribution. Modeling performance of the alphaIW distribution is compared with its rivals by means of some well-known goodness-of-fit statistics and results show that alphaIW distribution performs better modeling than them. Results of comparison also indicate that obtaining the alphaIW distribution by using the alpha-monotone concept is cost effective since the new shape parameter added to the distribution by using the alpha-monotone concept significantly increases the modeling capability of the IW distribution. As a result of this study, it is shown that the alphaIW distribution can be an alternative to the well-known and recently-introduced distributions for modeling purposes.

Generalized Fiducial Inference for the Generalized Rayleigh Distribution

This article focuses on the interval estimation of the generalized Rayleigh distribution with scale and shape parameters. The generalized fiducial method is used to construct the fiducial point estimators as well as the fiducial confidence intervals, and then their performance is compared with other methods such as the maximum likelihood estimation, Bayesian estimation and parametric bootstrap method. Monte Carlo simulation studies are carried out to examine the efficiency of the methods in terms of the mean square error, coverage probability and average length. Finally, two real data sets are presented to demonstrate the applicability of the proposed method.

Simulation Study for Parametric EWMA and NPWEWPA-SR Control Charts Against Non-Normality Assumptions

Common control chart types such as EWMA require assumptions to have valid information. The study compares IC robustness and OOC performance for parametric EWMA and NPEWMA-SR control charts in violation of symmetrical assumption. The Monte Carlo simulation study held scale parameters with various shape parameters in Weibull distribution. First finding in this paper was both parametric EWMA and NPEWMA-SR control charts were not suitable for the application in asymmetrical distribution due to weak IC robustness and frequent false alarm will be occurred. Although EWMA-X ̅ The control chart showed a most stable OOC performance; the weak IC robustness made the control chart unacceptable. Whereas, NPEWMA-SR control chart lost the ability in small shift detection when symmetrical assumption violated. Moreover, two different weightage of current sample for both parametric EWMA and NPEWMA-SR control charts were also investigated. The results showed that weightage of current sample for both parametric EWMA and NPEWMA-SR control charts did not affect the ARL value trend in different skewness of Weibull distribution.

A new discrete distribution with applications to radiation, smoking, and health data

In this study, a flexible discrete distribution is introduced by compounding Epanechnikov-exponential and Poisson distributions. Many mathematical features of the distribution are obtained. Several point estimators of the unknown parameter are proposed. The Monte Carlo simulation study is carried out to compare these estimators. Three methods are proposed to construct the approximate confidence intervals for the distribution parameter. A novel zero-inflated regression model is given based on the proposed discrete distribution as an alternative to zero-inflated geometric and zero-inflated Poisson models. The usability of the distribution and the count regression model is demonstrated by some practical data analyses, such as the number of dicentric chromosomes observed after exposure to radiation at doses of 0.600, survey data on health, and smoking habits.

Detecting Cohort Effects in Accelerated Longitudinal Designs Using Multilevel Models

Accelerated longitudinal designs allow researchers to efficiently collect longitudinal data covering a time span much longer than the study duration. One important assumption of these designs is that each cohort (a group defined by their age of entry into the study) shares the same longitudinal trajectory. Although previous research has examined the impact of violating this assumption when each cohort is defined by a single age of entry, it is possible that each cohort is instead defined by a range of ages, such as groups that experience a particular historical event. In this paper we examined how including cohort membership in linear and quadratic multilevel models performed in detecting and controlling for cohort effects in this scenario. Using a Monte Carlo simulation study, we assessed the performance of this approach under conditions related to the number of cohorts, the overlap between cohorts, the strength of the cohort effect, the number of affected parameters, and the sample size. Our results indicate that models including a proxy variable for cohort membership based on age at study entry performed comparably to using true cohort membership in detecting cohort effects accurately and returning unbiased parameter estimates. This indicates that researchers can control for cohort effects even when true cohort membership is unknown.

Multiple imputation of missing data in multilevel ecological momentary assessments: an example using smoking cessation study data.

Advances in digital technology have greatly increased the ease of collecting intensive longitudinal data (ILD) such as ecological momentary assessments (EMAs) in studies of behavior changes. Such data are typically multilevel (e.g., with repeated measures nested within individuals), and are inevitably characterized by some degrees of missingness. Previous studies have validated the utility of multiple imputation as a way to handle missing observations in ILD when the imputation model is properly specified to reflect time dependencies. In this study, we illustrate the importance of proper accommodation of multilevel ILD structures in performing multiple imputations, and compare the performance of a multilevel multiple imputation (multilevel MI) approach relative to other approaches that do not account for such structures in a Monte Carlo simulation study. Empirical EMA data from a tobacco cessation study are used to demonstrate the utility of the multilevel MI approach, and the implications of separating participant- and study-initiated EMAs in evaluating individuals' affective dynamics and urge.

Statistical inference for the partial area under ROC curve for the lower truncated proportional hazard rate models based on progressive Type-II censoring

This paper considers inference on the partial area under the receiver operating characteristic curve based on two independent progressively Type-II censored samples from the populations that are belonging to the lower truncated proportional hazard rate models with the same baseline distributions. The maximum likelihood estimator, a generalized pivotal estimator and some Bayes estimators are obtained for three structures of prior distributions. The percentile bootstrap confidence interval, a generalized pivotal confidence interval and some Bayesian credible intervals are also presented. A Monte-Carlo simulation study is used to evaluate the performances of the obtained point estimators and confidence and credible intervals. Finally, a real data set is applied for illustrative purposes.

Nickel effects on carbon dioxide and methane adsorptions on porous glass: experimental and monte carlo simulation studies

Nickel effects on carbon dioxide and methane adsorptions on porous glass: experimental and monte carlo simulation studies

Testing the correct specification of a system of spatial dependence models for stock returns

This paper provides two specification tests for the system of spatial autoregressive model of order m. We derive the theoretical limit distributions and show in a detailed Monte Carlo simulation study that the tests result in reasonable sized testing procedures with large power. In the empirical application, we analyze Euro Stoxx 50 returns in two different time spans, looking for insights how well models with different specifications of the spatial weighting matrices (local, country, industry and country-industry specific dependencies including interaction effects) fit to the data. The analyzes also demonstrate the ability of the tests to detect inaccurate Value-at-Risk forecasts.

Uncertainty quantification of the diffuse sound field assumption in sound insulation predictions

The transmission loss of a partition depends on the sound fields in the adjoining rooms, which are often taken to be diffuse. A diffuse sound field is a random field, representing a conceptual ensemble of rooms with the same volume and reverberation time, but otherwise any possible arrangement of boundaries and small objects that have a wave scattering effect. Uncertainty due to random wave scattering is therefore inherently present in diffuse transmission loss values. In this work, closed-form expressions are presented for quantifying this uncertainty, such that it becomes possible to estimate by how much the transmission loss of a particular room-wall-room system may deviate from the nominal, ensemble average value which corresponds to omnidirectional incidence and halfspace radiation. The first expression is exact yet its evaluation requires knowledge of the dynamic stiffness of the partitioning structure and of the radiation impedances of the surrounding fluids. The second expression is approximate and depends on energetic quantities only, so it is also applicable in an experimental setting. The expressions are validated in a Monte-Carlo simulation study involving a single wall.

Extropy based inaccuracy measure in order statistics

In this paper, we provide a measure based on inaccuracy between distributions of the ith order statistic and the parent random variable. This measure characterizes the distribution function of parent random variable uniquely. We demonstrate that the extropy of the parent random variable is the average of the accuracy measure. It is also shown that the measure of inaccuracy defined is invariant under scale but not under location transformation. Nonparametric estimators for the proposed measures are also obtained. A Monte Carlo simulation study is performed to verify the performance of the suggested estimators. Simulation results show that the estimator based on the reflection boundary technique for probability density function estimation and the empirical method for cumulative distribution function estimation has the best performance among estimators. Also, a real dataset is considered to show an application of the proposed estimators on model selection.

The Generalized Thurstonian Unfolding Model (GTUM): Advancing the Modeling of Forced-Choice Data

Forced-choice (FC) measurement has become increasingly popular due to its robustness to various response biases and reduced susceptibility to faking. Although several current Item Response Theory (IRT) models can extract normative person scores from FC responses, each has its limitations. This study proposes the Generalized Thurstonian Unfolding Model (GTUM) as a more flexible IRT model for FC measures to overcome these limitations. The GTUM (1) adheres to the unfolding response process, (2) accommodates FC scales of any block size, and (3) manages both dichotomous and graded responses. Monte Carlo simulation studies consistently demonstrated that the GTUM exhibited good statistical properties under most realistic conditions. Particularly noteworthy findings include (1) the GTUM's ability to handle FC scales with or without intermediate statements, (2) the consistently superior performance of graded responses over dichotomous responses in person score recovery, and (3) the sufficiency of 10 mixed pairs to ensure robust psychometric performance. Two empirical examples, one with 1,033 responses to a static version of the Tailored Adaptive Personality Assessment System and the other with 759 responses to a graded version of the Forced-Choice Five-Factor Markers, demonstrated the feasibility of the GTUM to handle different types of FC scales. To aid in the practical use of the GTUM, we also developed the R package “ fcscoring.”

Estimation of stress-strength reliability for generalized Gompertz distribution under progressive type-II censoring

In this study, the stress-strength reliability, $R=P(Y

Comparison of Propensity Score Weighting Methods to Remove Selection Bias in Average Treatment Effect Estimates

In this Monte Carlo simulation study, the performance of six different propensity score methods implemented through weighting cases was investigated: inverse probability of treatment weighting, truncated inverse probability of treatment weighting, propensity score stratification, marginal mean weighting through propensity score stratification, optimal full propensity score matching, and marginal mean weighting through optimal full propensity score matching. These methods aim to reduce selection bias in estimates of the average treatment effect (ATE) in observational studies. For the estimation of standard errors of the ATE with weights, three methods were compared: weighted least squares (WLS), Taylor series linearization (TSL), and jackknife (JK). Results indicated that covariance adjustment extensions of the investigated propensity score methods, in combination with TSL and JK standard error estimation methods, remove the selection bias appropriately and provide the most accurate standard errors under the simulated conditions.