Series Of Monte-Carlo Simulations Research Articles

Lessons From Pinocchio: Cues to Deception May Be Highly Exaggerated

Deception researchers widely acknowledge that cues to deception—observable behaviors that may differ between truthful and deceptive messages—tend to be weak. Nevertheless, several deception cues have been reported with unusually large effect sizes, and some researchers have advocated the use of such cues as tools for detecting deceit and assessing credibility in practical contexts. By examining data from empirical deception-cue research and using a series of Monte Carlo simulations, I demonstrate that many estimated effect sizes of deception cues may be greatly inflated by publication bias, small numbers of estimates, and low power. Indeed, simulations indicate the informational value of the present deception literature is quite low, such that it is not possible to determine whether any given effect is real or a false positive. I warn against the hazards of relying on potentially illusory cues to deception and offer some recommendations for improving the state of the science of deception.

A Novel FEM Based T-S Fuzzy Particle Filtering for Bearings-Only Maneuvering Target Tracking.

In this paper, we propose a novel fuzzy expectation maximization (FEM) based Takagi-Sugeno (T-S) fuzzy particle filtering (FEMTS-PF) algorithm for a passive sensor system. In order to incorporate target spatial-temporal information into particle filtering, we introduce a T-S fuzzy modeling algorithm, in which an improved FEM approach is proposed to adaptively identify the premise parameters, and the model probability is adjusted by the premise membership functions. In the proposed FEM, the fuzzy parameter is derived by the fuzzy C-regressive model clustering method based on entropy and spatial-temporal characteristics, which can avoid the subjective influence caused by the artificial setting of the initial value when compared to the traditional FEM. Furthermore, using the proposed T-S fuzzy model, the algorithm samples particles, which can effectively reduce the particle degradation phenomenon and the parallel filtering, can realize the real-time performance of the algorithm. Finally, the results of the proposed algorithm are evaluated and compared to several existing filtering algorithms through a series of Monte Carlo simulations. The simulation results demonstrate that the proposed algorithm is more precise, robust and that it even has a faster convergence rate than the interacting multiple model unscented Kalman filter (IMMUKF), interacting multiple model extended Kalman filter (IMMEKF) and interacting multiple model Rao-Blackwellized particle filter (IMMRBPF).

Statistical Inference for Alpha-Series Process with the Generalized Rayleigh Distribution.

In the modeling of successive arrival times with a monotone trend, the alpha-series process provides quite successful results. Both selecting the distribution of the first arrival time and making an optimal statistical inference play a crucial role in the modeling performance of the alpha-series process. In this study, when the distribution of the first arrival time is the generalized Rayleigh, the problem of statistical inference for the , , and parameters of the alpha-series process is considered. Further, in order to obtain optimal modeling performance from the mentioned alpha-series process, various estimators for the model parameters are obtained by employing different estimation methodologies such as maximum likelihood, modified maximum spacing, modified least-squares, modified moments, and modified L-moments. By a series of Monte Carlo simulations, the estimation efficiencies of the obtained estimators are evaluated through the different sample sizes. Finally, two real datasets are analyzed to illustrate the importance of modeling with the alpha-series process.

Optimized design for time-of-flight total neutron scattering instrumentation using Monte Carlo simulations with application for the characterization of disordered macromolecules

A time-of-flight neutron total scattering instrument for the China Spallation Neutron Source (CSNS) was designed. The instrument is designed for disordered macromolecular systems with characteristic lengths below 10 nm. The coupled hydrogen moderator of CSNS was selected to ensure the favorable statistics at the smallest scattering vector of 0.01 Å−1. The optics and geometry of the instrument were optimized to cover a wide range of scattering vectors from 0.01 to 70 Å−1. The performance of the instrument was examined using a series of Monte Carlo simulations, which demonstrate that the flux on the sample, the resolution, and the range of the scattering vectors are comparable to those of other advanced neutron total instruments. Finally, a virtual experiment was performed with silica powder. The resulting scattering pattern is consistent with both the numeric calculation and the estimated resolution of the instrument. The designed total scattering instrument at CSNS allows the simultaneous observation of both atomic and nanometer scale structures, having the potential to become a powerful tool for studying the structure of disordered materials in the fields of polymers, biology, and condensed matter physics.

Coping with the black swan in psychophysics

Aberrant responses that are associated with lapses in attention and microsleeps during a behavioral experiment violate basic assumptions underlying signal detection theory. These responses (i.e., the black swan events) occur infrequently but could lead to severe biases in the estimated perceptual sensitivity (d’) and misinterpretations of the data. When adaptive procedures are used to estimate behavioral thresholds, performance lapses occurring early in an adaptive track may cause convergence failures. Moreover, microsleeps may become more frequent as the task difficulty decreases, leading to potential instabilities in common adaptive procedures. Through a series of Monte-Carlo simulations, the potential effects of performance lapses on typical psychophysical tasks (e.g., Yes/No, 2-alternative forced choice) were demonstrated. Several possible experimental and computational techniques to cope with performance lapses were investigated. These included (1) introducing additional parameters representing performance lapses into the psychometric model, (2) allowing a “Not Sure” response category, and (3) screening and removing the likely aberrant responses. These simulation studies provide useful guidelines to design adaptive psychophysical procedures for subject populations from which performance lapses are expected (e.g., young children and behaving animals).

A panel quantile approach to attrition bias in Big Data: Evidence from a randomized experiment

This paper introduces a quantile regression estimator for panel data models with individual heterogeneity and attrition. The method is motivated by the fact that attrition bias is often encountered in Big Data applications. For example, many users sign-up for the latest program but few remain active users several months later, making the evaluation of such interventions inherently very challenging. Building on earlier work by Hausman and Wise (1979), we provide a simple identification strategy that leads to a two-step estimation procedure. In the first step, the coefficients of interest in the selection equation are consistently estimated using parametric or nonparametric methods. In the second step, standard panel quantile methods are employed on a subset of weighted observations. The estimator is computationally easy to implement in Big Data applications with a large number of subjects. We investigate the conditions under which the parameter estimator is asymptotically Gaussian and we carry out a series of Monte Carlo simulations to investigate the finite sample properties of the estimator. Lastly, using a simulation exercise, we apply the method to the evaluation of a recent Time-of-Day electricity pricing experiment inspired by the work of Aigner and Hausman (1980).

Propensity-score matching with competing risks in survival analysis.

Propensity‐score matching is a popular analytic method to remove the effects of confounding due to measured baseline covariates when using observational data to estimate the effects of treatment. Time‐to‐event outcomes are common in medical research. Competing risks are outcomes whose occurrence precludes the occurrence of the primary time‐to‐event outcome of interest. All non‐fatal outcomes and all cause‐specific mortality outcomes are potentially subject to competing risks. There is a paucity of guidance on the conduct of propensity‐score matching in the presence of competing risks. We describe how both relative and absolute measures of treatment effect can be obtained when using propensity‐score matching with competing risks data. Estimates of the relative effect of treatment can be obtained by using cause‐specific hazard models in the matched sample. Estimates of absolute treatment effects can be obtained by comparing cumulative incidence functions (CIFs) between matched treated and matched control subjects. We conducted a series of Monte Carlo simulations to compare the empirical type I error rate of different statistical methods for testing the equality of CIFs estimated in the matched sample. We also examined the performance of different methods to estimate the marginal subdistribution hazard ratio. We recommend that a marginal subdistribution hazard model that accounts for the within‐pair clustering of outcomes be used to test the equality of CIFs and to estimate subdistribution hazard ratios. We illustrate the described methods by using data on patients discharged from hospital with acute myocardial infarction to estimate the effect of discharge prescribing of statins on cardiovascular death.

Recoil-proton track imaging as a new way for neutron spectrometry measurements

Recoil-proton track imaging (RPTI) is an attractive technique to optically record the tracks of recoil protons in scintillation gas by using realtime imaging devices. For the first time, its use as an online nuclear track detector for neutron spectrometry measurements (NSM) is explored. Based on the RPTI methodology for NSM, a very basic detector system is designed, consisting of the neutron-to-proton recoil system and proton track imaging system. Satisfactory performance of the RPTI neutron spectrometer has been examined with a series of Monte Carlo simulations. Moreover, using well-defined line-proton sources from a tandem accelerator, the capability of the detector for imaging proton tracks at the single-particle level in real time has been validated in preliminary experiments. From the clear single proton tracks in the images, the proton ranges were easily distinguished, and precise proton energy spectra were unfolded, laying a solid experimental foundation for the future implementation of NSM.

Importance-Weighted Density: A Shared Leadership Illustration of the Case for Moving Beyond Density and Decentralization in Particularistic Resource Networks

Social network analysis has been increasingly used by researchers to operationalize team processes and emergent states. Despite their advantages over aggregate measures, the most frequently used network measures such as density and centrality are agnostic to potentially meaningful elements reflecting the patterns of ties between team members. Specifically, intangible resources transmitted within team networks are often more particularistic, such that the value of the shared resource is dependent upon who gives it. We use shared leadership as an exemplar case for explaining this issue and proposing a solution in the form of a new network measure, importance-weighted density (IWD). Combining logic from the principles of density, decentralization, and eigenvector centralization, IWD provides a more detailed understanding of network tie patterns by taking into account the degree to which ties emerge from members who are themselves well connected. We test the measure’s validity in a series of Monte Carlo simulations and laboratory and field studies. We find that IWD has high convergent, discriminant, and criterion validities and discuss how this statistic might help to enhance the study of several other team constructs. We provide access to a downloadable tool for the calculation of IWD and other network statistics discussed within this article.

The effect of number of clusters and cluster size on statistical power and Type I error rates when testing random effects variance components in multilevel linear and logistic regression models

When using multilevel regression models that incorporate cluster-specific random effects, the Wald and the likelihood ratio (LR) tests are used for testing the null hypothesis that the variance of the random effects distribution is equal to zero. We conducted a series of Monte Carlo simulations to examine the effect of the number of clusters and the number of subjects per cluster on the statistical power to detect a non-null random effects variance and to compare the empirical type I error rates of the Wald and LR tests. Statistical power increased with increasing number of clusters and number of subjects per cluster. Statistical power was greater for the LR test than for the Wald test. These results applied to both the linear and logistic regressions, but were more pronounced for the latter. The use of the LR test is preferable to the use of the Wald test.

Improved Back-Projection Cortical Potential Imaging by Multi-resolution Optimization Technique.

Electroencephalogram (EEG) has evolved to be a well-established tool for imaging brain activity. This progress is mainly due to the development of high-resolution (HR) EEG methods. One class of HR-EEG is the cortical potential imaging (CPI), which aims to estimate the potential distribution on the cortical surface, which is much more informative than EEG. Even though these methods exhibit good performance, most of them have inherent inaccuracies that originate from their operating principles that constrain the solution or require a complex calculation process. The back-projection CPI (BP-CPI) method is relatively new and has the advantage of being constraint-free and computation inexpensive. The method has shown relatively good accuracy, which is necessary to become a clinical tool. However, better performance must be achieved. In the present study, two improvements are proposed. Both are embedded as adjacent stages to the BP-CPI and are based on the multi-resolution optimization approach (MR-CPI). A series of Monte-Carlo simulations were performed to examine the characteristics of the proposed improvements. Additional tests were done, including different EEG noise levels and variation in electrode-numbers. The results showed highly accurate cortical potential estimations, with a reduction in estimation error by a factor of 3.75 relative to the simple BP-CPI estimation error. We also validated these results with true EEG data. Analyzing these EEGs, we have demonstrated the MR-CPI competence to correctly localize cortical activations in a real environment. The MR-CPI methods were shown to be reliable for estimating cortical potentials, enabling researchers to obtain fast and robust high-resolution EEGs.

Safety Assessment of a UAV CD&R System in High Density Airspace Using Monte Carlo Simulations

This paper presents a safety assessment method for unmanned aerial vehicle (UAV) operations, including the effect of a distributed conflict detection and resolution (CD&R) system, in a high density airspace environment. Here, the expected conflicts occurrence and chances for each CD&R system to perform are sufficiently high to extract two parameters of safety, i.e., the frequency of near mid-air collision (NMAC) and the frequency of mid-air collision (MAC), by series of Monte Carlo simulations. The results are then used to derive the safety parameters in a more realistic, less dense airspace. Two cases of distributed CD&R protocols are assessed and compared, i.e., 1) uncoordinated protocol, where each vehicle has its own avoidance preferences, and 2) implicitly coordinated protocol where each vehicle, while still independent from each other, applies simple common rules. Using those CD&R protocols, the result shows a reduction of more than 94% of possible NMAC. Moreover, while maintaining the target level of safety in the airspace, the maximum number of UAVs under an implicitly coordinated CD&R protocol can be at least ten times more than cases when no CD&R is applied.

Effect of suspended solids on peracetic acid decay and bacterial inactivation kinetics: Experimental assessment and definition of predictive models

The work addresses the effect of total suspended solids (TSS) on disinfection by peracetic acid (PAA) concerning both PAA decay and bacterial inactivation kinetics. The effect of TSS on PAA decay was evaluated at five TSS concentrations (5, 40, 80, 120 and 160 mg/L), obtained from stock TSS solutions prepared from activated sludge samples. The influence of the soluble matter associated to the suspended solids on PAA decay was evaluated separately, using the same stock TSS solution after the removal of solids by filtration. The contributions of suspended and soluble fractions were found to be independent, and a predictive model formed by two additive sub-models was proposed to describe the overall PAA decay kinetics. Moreover, an uncertainty analysis was performed by a series of Monte Carlo simulations to propagate the uncertainties associated to the coefficients of the model. Then, the disinfectant dose (mg/L min) was highlighted as the main parameter determining disinfection efficiency on a pure culture of E. coli and an inactivation kinetic model was developed based on the response of E. coli to various PAA doses. Finally, the effect of TSS (40 and 160 mg/L) on the inactivation of free-swimming E. coli was investigated at two PAA doses (5 and 20 mg/L min). TSS reduced inactivation extent an average of 0.4 logs at 5 mg/L min and 1.5 logs at 20 mg/L min. It was hypothesized that this might be due to the formation of bacteria aggregates as defense mechanism against disinfection, enhanced by the presence of solids.

Model-Based Measures for Detecting and Quantifying Response Bias

This paper proposes a model-based family of detection and quantification statistics to evaluate response bias in item bundles of any size. Compensatory (CDRF) and non-compensatory (NCDRF) response bias measures are proposed, along with their sample realizations and large-sample variability when models are fitted using multiple-group estimation. Based on the underlying connection to item response theory estimation methodology, it is argued that these new statistics provide a powerful and flexible approach to studying response bias for categorical response data over and above methods that have previously appeared in the literature. To evaluate their practical utility, CDRF and NCDRF are compared to the closely related SIBTEST family of statistics and likelihood-based detection methods through a series of Monte Carlo simulations. Results indicate that the new statistics are more optimal effect size estimates of marginal response bias than the SIBTEST family, are competitive with a selection of likelihood-based methods when studying item-level bias, and are the most optimal when studying differential bundle and test bias.

Least squares support vector machine regression combined with Monte Carlo simulation based on the spatial frequency domain imaging for the detection of optical properties of pear

A spatial frequency domain imaging system to nondestructively measure wide-field optical properties of biological tissue has been developed. Optical parameters of absorption (μa) and reduced scattering coefficients (μ’s) of ‘Crown’ pears were estimated based on spatial frequency dependent diffuse reflectance (Rd). Because of the limitation of diffusion approximation (DA), least squares support vector machine (LSSVM) regression was introduced to model the relationship between the μa, μ’s and Rd that were generated by Monte Carlo (MC) simulation. To accelerate the detection speed, two spatial frequencies of fx = 0 m−1 and nonzero fx were chosen according to the correlation coefficients between the measured and simulated results of Rd of 60 liquid phantoms. The results showed that the fx of 250 m−1 presents the highest correlation. The forward LSSVR models at the two fx were trained based on a series of MC simulations with a wide range of μa, μ’s and albedo, and then validated by the phantoms by comparing predicted Rd and MC simulated Rd. The validation results showed that two models were capable of describing the forward relationship with little deviation, and that both the determination coefficients were close to 1 at two fx. The μa and μ’s of phantoms were predicted after an optimal searching of models. The inverse validation results showed that the mean relative errors for μa and μ’s were 5.27% and 2.65% respectively. Compared with the calculation results by DA, the proposed method was capable of measuring the optical properties of tissue simulation phantoms accurately, especially when the albedo is less than 50. A case study on the inspection of pear bruises was carried out to verify the application prospect of the proposed method. Results indicated that the fresh bruise which is not obviously distinguishable by naked eyes, can be identified in μ’s maps.

Using beta coefficients to impute missing correlations in meta-analysis research: Reasons for caution.

Meta-analysis has become a well-accepted method for synthesizing empirical research about a given phenomenon. Many meta-analyses focus on synthesizing correlations across primary studies, but some primary studies do not report correlations. Peterson and Brown (2005) suggested that researchers could use standardized regression weights (i.e., beta coefficients) to impute missing correlations. Indeed, their beta estimation procedures (BEPs) have been used in meta-analyses in a wide variety of fields. In this study, the authors evaluated the accuracy of BEPs in meta-analysis. We first examined how use of BEPs might affect results from a published meta-analysis. We then developed a series of Monte Carlo simulations that systematically compared the use of existing correlations (that were not missing) to data sets that incorporated BEPs (that impute missing correlations from corresponding beta coefficients). These simulations estimated ρ̄ (mean population correlation) and SDρ (true standard deviation) across a variety of meta-analytic conditions. Results from both the existing meta-analysis and the Monte Carlo simulations revealed that BEPs were associated with potentially large biases when estimating ρ̄ and even larger biases when estimating SDρ. Using only existing correlations often substantially outperformed use of BEPs and virtually never performed worse than BEPs. Overall, the authors urge a return to the standard practice of using only existing correlations in meta-analysis. (PsycINFO Database Record

The perils of standardizing infant weight to assess weight change differences across exposure groups

PurposeWhen conducting analyses of child weight growth trajectories, researchers commonly use Z-scores from a standard instead of the observed weights. However, these Z-scores, calculated from cross-sectional data, may introduce methodological limitations when used in the context of longitudinal analyses. We assessed analytic limitations when analyzing infant growth data with three anthropometric measures: weight and the corresponding Z-scores and percentiles from a standard. MethodsWe undertook a series of Monte Carlo simulations and compared tests of differences in postnatal weight change across time (growth velocity) between two exposure groups. Models with the observed weight outcome were compared to the corresponding weight World Health Organization (WHO) Z-score or weight percentile outcomes. We calculated power, type I error, and median product term coefficient estimates to assess differences between the models. ResultsThere was lower power to detect velocity differences across exposure groups for WHO Z-scores and percentiles as outcomes compared to the use of observed weight values. We also noted instances in which velocity differences between exposed and unexposed groups were in the opposite direction in analyses with WHO Z-score outcomes. ConclusionsIn our simulations of infant weight velocity differences across exposure groups, we observed lower power and effect inconsistencies when applying a standard-derived Z-score transformation. These results emphasize the need for careful consideration of the appropriate scale when assessing infant growth trajectories across categorical groups.

The effect of microscopic cracks on chloride diffusivity of recycled aggregate concrete

This study proposes a novel stochastic numerical method to investigate the effect of microscopic cracks on chloride diffusivity of recycled aggregate concrete. First of all, two-dimensional fictitious multi-phase recycled aggregate concrete (RAC) models are randomly generated. The fictitious RAC models consist of microscopic cracks in addition to natural aggregate, old interfacial transition zones (ITZs), old mortar, new ITZs, and new mortar. Next, effective homogenized ion diffusion coefficients of RAC models are computed using representative volume element (RVE) approach, after the size of RVE is determined by convergence analysis. Then a series of Monte Carlo simulations are made to explore the link between randomly distribution of microscopic cracks and the effective homogenized ion diffusivity. Finally, the numerical experimental results indicate that microscopic defect number, direction, and length might have significant influence on the effective homogenized ion diffusion parameter of RAC. In addition, this study investigates material property uncertainties of ITZs and mortar on the global chloride diffusivity of RAC. Final results demonstrate that effects from material property uncertainties of ITZs and mortar might be negligible.

Dynamic Online Pricing with Incomplete Information Using Multi-Armed Bandit Experiments

Pricing managers at online retailers face a unique challenge. They must decide on real-time prices for a large number of products with incomplete demand information. The manager runs price experiments to learn about each product's demand curve and the profit-maximizing price. Balanced field price experiments, in practice can create high opportunity costs since a large number of customers are presented with sub-optimal prices. In this paper, we propose an alternative dynamic price experimentation policy. The proposed approach extends multi-armed bandit (MAB) algorithms, from statistical machine learning, to include microeconomic choice theory. Our automated pricing policy solves this MAB problem using a scalable distribution-free algorithm. We prove analytically that our method is asymptotically optimal for any weakly downward sloping demand curve. In a series of Monte Carlo simulations, we show that the proposed approach perform favorably compared to balanced field experiments and standard methods in dynamic pricing from computer science. In a calibrated simulation based on an existing pricing field experiment, we find that our algorithm can increase profits by 43% profits during the month of testing and 4% annually.

Assessing covariate balance when using the generalized propensity score with quantitative or continuous exposures.

Propensity score methods are increasingly being used to estimate the effects of treatments and exposures when using observational data. The propensity score was initially developed for use with binary exposures (e.g., active treatment vs. control). The generalized propensity score is an extension of the propensity score for use with quantitative exposures (e.g., dose or quantity of medication, income, years of education). A crucial component of any propensity score analysis is that of balance assessment. This entails assessing the degree to which conditioning on the propensity score (via matching, weighting, or stratification) has balanced measured baseline covariates between exposure groups. Methods for balance assessment have been well described and are frequently implemented when using the propensity score with binary exposures. However, there is a paucity of information on how to assess baseline covariate balance when using the generalized propensity score. We describe how methods based on the standardized difference can be adapted for use with quantitative exposures when using the generalized propensity score. We also describe a method based on assessing the correlation between the quantitative exposure and each covariate in the sample when weighted using generalized propensity score -based weights. We conducted a series of Monte Carlo simulations to evaluate the performance of these methods. We also compared two different methods of estimating the generalized propensity score: ordinary least squared regression and the covariate balancing propensity score method. We illustrate the application of these methods using data on patients hospitalized with a heart attack with the quantitative exposure being creatinine level.