Resulting Parameter Estimates Research Articles

A Stochastic Harmonic Oscillator Temperature Model for the Valuation of Weather Derivatives

Stochastic processes are employed in this paper to capture the evolution of daily mean temperatures, with the goal of pricing temperature-based weather options. A stochastic harmonic oscillator model is proposed for the temperature dynamics and results of numerical simulations and parameter estimation are presented. The temperature model is used to price a one-month call option and a sensitivity analysis is undertaken to examine how call option prices are affected when the model parameters are varied.

A Hybrid Algorithm for Parameter Estimation (HAPE) for Dynamic Constant Power Loads

Low-inertia microgrids may easily have a single load that can make up most of the total load, thereby greatly affecting stability and power quality. Instead of a static load model, a dynamic constant power load (DCPL) model is considered here. Next, a hybrid algorithm for parameter estimation (HAPE) is introduced. In order to verify the load model and the HAPE, two experiments are conducted with different DCPLs using a power-hardware-in-the-loop (PHiL) testbed. The PHiL testbed consists of a real-time computer working with a programmable power amplifier in order to perturb the input voltage's amplitude and frequency. Each connected DCPL in two separate experiments serves as the device under test. Using the captured experimental data as a reference, the HAPE is then invoked. The resulting parameter estimates are used to define simulation models. Both resulting DCPL models are simulated to produce waveforms that closely resemble experimental waveforms. Finally, the HAPE's resulting parameter estimates are presented, and the performance of the HAPE is discussed.

Application of millimeter wave radar in safety obstacle avoidance of crane in electric power construction

When the traditional electric construction crane safely avoids obstacles, obstacles are only detected according to the parameter estimation results, which is greatly affected by the external environment and takes a long time to avoid obstacles. Aiming at the above problems, the safety obstacle avoidance method of electric construction crane based on millimeter wave radar is studied. The millimeter wave radar is used to obtain the millimeter wave radar detection signal for crane safety obstacle avoidance. According to the impulse response of millimeter wave radar detection echo, the obstacle location mark and distance estimation are carried out, and the distance and azimuth information between the crane and the obstacle are calculated. By optimizing the safe obstacle avoidance path of electric construction cranes, the safe obstacle avoidance of electric construction cranes can be realized. Through comparative simulation experiments, it is verified that the design method takes less time to avoid obstacles, has higher efficiency, and does not collide with obstacles in the workspace in the process of movement, which verifies the effectiveness of the method.

Dynamical Analysis and Parameter Estimation of Hepatitis B Disease Model in Malang

In this article, a model representing the spread of Hepatitis B disease is constructed as a nonlinear autonomous system. The model divides the considered human population into three classes, namely susceptible, infected, and recovered class. The dynamical analysis shows that there are two equilibrium points in the model, namely a disease-free equilibrium point and an endemic equilibrium point. The existence and stability of the equilibrium points depend on the basic reproduction number (R_0). The disease-free equilibrium point is local asymptotically stable when R_0<1Â while the endemic equilibrium point exists and is local asymptotically stable if R_0>1. The five parameters of the model are estimated by applying Downhill Simplex (Nelder-Mead) Algorithm and by using the infected data cases taken from such a hospital in Malang. The estimated parameters are the transmission of infection rate, the saturation rate, the vaccination rate, the recovery rate, and the immunity loss rate. The resulting parameter estimation supports the analytical result and is used to illustrate the analytical results numerically. Based on the considered model and the result of the parameters estimation, it can be concluded that the Hepatitis B spread in Malang is controllable.Keywords: downhill simplex (Nelder-mead) algorithm, dynamical analysis, hepatitis B model, parameter estimation.Â

Gaussian process model selection for parameter estimation in ocean acoustics

The dynamic nature of the ocean environment presents numerous challenges to parameter estimation in ocean acoustics. We present a variational Bayesian method which selects an optimal model for parameter estimation in ocean acoustics using Gaussian process (GP) regression. GP model selection is based on Bayesian model selection, which treats the parameters and kernel selection of the model as hyperparameters and uses Bayesian inference to compute a model’s likelihood with respect to its hyperparameters. However, in Bayesian model selection, the integrals over parameter space are often intractable and require approximation or sampling methods such as Markov chain Monte Carlo. GP renders these integrals tractable by treating hyperparameters as multivariate Gaussian distributions, which greatly speeds up the optimization. Furthermore, model selection using GPs allows for straightforward evaluation of uncertainty in the selected model’s likelihood. In this study, we compare results of acoustic parameter estimation from Bayesian and GP model selection using an ocean acoustic propagation model.

Comparison of Testlet Effect on Parameter Estimates Using Different Item Response Theory Models

In this study, the testlet effect was calculated for each testlet in the PISA 2018 reading literacy test, and it was examined whether this effect caused a difference in item and ability parameters. The data set was analyzed with a two-parameter logistic item response theory model and a two-parameter logistic testlet model. The results show that variances of testlet effects range from .100 to .432. When the item and ability parameter estimation results of the models were compared, it was determined that the item and ability parameters estimated from the two approaches were highly correlated with each other. It can be said that the item slope and item intercept parameters estimated from different models remained unaffected. However, when the local dependency assumption is not met, it was observed that the standard error values of the two-parameter model for the ability parameter were underestimated. The implications for the analysis and evaluation of the tests based on testlet are discussed. In conclusion, in this study, it was concluded that the testlet effect caused a difference in parameter estimates, but the local dependence among the items was negligible because of the small testlet effects.

Detecting Epileptic Seizures Based on Parameter Estimation of a Neural Mass Model

This paper presents two adaptive Kalman filters (KFs) for nonlinear model updating where, in addition to nonlinear model parameters, the covariance matrix of measurement noise is estimated recursively in a near online manner. Two adaptive KF approaches are formulated based on the forgetting factor and the moving window covariance-matching techniques using residuals. Although the proposed adaptive methods are integrated with the unscented KF (UKF) for nonlinear model updating in this paper, they can be alternatively combined with other types of nonlinear KFs such as the extended KF (EKF) or the ensemble KF (EnKF). The performance of the proposed methods is investigated through two numerical applications and compared to that of a non-adaptive UKF and an existing dual adaptive filter. The first application considers a nonlinear steel pier where nonlinear material properties are selected as updating parameters. Significant improvements in parameter estimation results are observed when using adaptive filters compared to the non-adaptive approach. Furthermore, the covariance matrix of simulated measurement noise is estimated from the adaptive approaches with acceptable accuracy. Effects of different types of modeling errors are studied in the second numerical application of a nonlinear 3-story 3-bay steel frame structure. Similarly, more accurate and robust parameter estimations and response predictions are obtained from the adaptive approaches compared to the non-adaptive approach. The results verify the effectiveness and robustness of the proposed adaptive filters. The forgetting factor and moving window methods are shown to have a simpler tuning process compared to the dual adaptive method while providing similar performance.

Forecasting the volatility of crude oil futures: The role of oil investor attention and its regime switching characteristics under a high-frequency framework

The purpose of this article is to investigate whether oil investor attention (OA), measured by Google search volume, contains incremental information content to predict crude oil futures volatility under high-frequency heterogeneous autoregressive (HAR) model specifications. Moreover, to account for possible structural breaks and nonlinearity in the relation between OA and crude oil volatility, this article extends HAR-type models with regime switching considerations. The results of parameter estimation and out-of-sample prediction show that the in-sample and out-of-sample performance of HAR-type and Markov switching (MS)-HAR-type models with OA is significantly better than that of their corresponding HAR-type and MS-HAR-type models without OA. Furthermore, our findings suggest that (i) HAR-type-OA models tend to produce better forecasts for the volatility of the crude oil market at short horizons (1-day) compared to HAR-type, MS-HAR-type and MS-HAR-type-OA models. (ii) MS-HAR-type-OA models have the best forecasting performance at relatively long prediction horizons (1-week and 1-month). Therefore, the result suggests that the OA and regime switching specifications have a significant positive impact on volatility predictions and can be useful for improving the performance of HAR-type models.

Inferring within-flock transmission dynamics of highly pathogenic avian influenza H5N8 virus in France, 2020.

Following the emergence of highly pathogenic avian influenza (H5N8) in France in early December 2020, we used duck mortality data from the index farm to investigate within‐flock transmission dynamics. A stochastic epidemic model was fitted to the daily mortality data and model parameters were estimated using an approximate Bayesian computation sequential Monte Carlo (ABC‐SMC) algorithm. The model predicted that the first bird in the flock was infected 5 days (95% credible interval, CI: 3–6) prior to the day of suspicion and that the transmission rate was 4.1 new infections per day (95% CI: 2.8–5.8). On average, ducks became infectious 4.1 h (95% CI: 0.7–9.1) after infection and remained infectious for 4.3 days (95% CI: 2.8–5.7). The model also predicted that 34% (50% prediction interval: 8%–76%) of birds would already be infectious by the day of suspicion, emphasizing the substantial latent threat this virus could pose to other poultry farms and to neighbouring wild birds. This study illustrates how mechanistic models can help provide rapid relevant insights that contribute to the management of infectious disease outbreaks of farmed animals. These methods can be applied to future outbreaks and the resulting parameter estimates made available to veterinary services within a few hours.

Improved estimation of myelin water fractions with learned parameter distributions.

To improve estimation of myelin water fraction (MWF) in the brain from multi-echo gradient-echo imaging data. A systematic sensitivity analysis was first conducted to characterize the conventional exponential models used for MWF estimation. A new estimation method was then proposed for improved estimation of MWF from practical gradient-echo imaging data. The proposed method uses an extended signal model that includes a finite impulse response filter to compensate for practical signal variations. This new model also enables the use of prelearned parameter distributions as well as low-rank signal structures to improve parameter estimation. The resulting parameter estimation problem was solved optimally in the Bayesian sense. Our sensitivity analysis results showed that the conventional exponential models were very sensitive to measurement noise and modeling errors. Our simulation and experimental results showed that our proposed method provided a substantial improvement in reliability, reproducibility, and robustness of MWF estimates over the conventional methods. Clinical results obtained from stroke patients indicated that the proposed method, with its improved capability, could reveal the loss of myelin in lesions, demonstrating its translational potentials. This paper addressed the problem of robust MWF estimation from gradient-echo imaging data. A new method was proposed to provide improved MWF estimation in the presence of significant noise and modeling errors. The performance of the proposed method has been evaluated using both simulated and experimental data, showing significantly improved robustness over the existing methods. The proposed method may prove useful for quantitative myelin imaging in clinical applications.

Robust Estimation for Proportional Odds Model through Monte Carlo Simulation

Ordinal regression is used to model the ordinal response variable as functions of several explanatory variables. The most commonly used model for ordinal regression is the proportional odds model (POM). The classical technique for estimating the unknown parameters of this model is the maximum likelihood (ML) estimator. However, this method is not suitable for solving problems with extreme observations. A robust regression method is needed to handle the problem of extreme points in the data. This study proposes Huber M-estimator as a robust method to estimate the parameters of the POM with a logistic link function and polytomous explanatory variables. This study assesses ML estimator performance and the robust method proposed through an extensive Monte Carlo simulation study conducted using statistical software, R. Measurement for comparisons are bias, RMSE, and Lipsitzs' goodness of fit test. Various sample sizes, percentages of contamination, and residual standard deviations are considered in the simulation study. Preliminary results show that Huber estimates provide the best results for parameter estimation and overall model fitting. Huber's estimator has reached a 50% breakdown point for data containing extreme points that are quite far from most points. In addition, the presence of extreme points that have only a distance of two times far from most points has no major impact on ML estimates. This means that the estimates for ML and Huber may yield the same results if the model's residual values are between -2 and 2. This situation may also occur for data with a percentage of contamination below 5%.

Analysis of Entrepreneurship Interest on Business Productivity

This study was conducted to determine the interest in entrepreneurship and business productivity of Semarang State Polytechnic students during the covid-19 pandemic. Respondents in this study were Semarang State Polytechnic students who had a business in the middle of their learning on campus totaling 100 students. This study uses confirmatory factor analysis and maximum likelihood estimation techniques on SEM (Structural Equations Modeling) from the AMOS 24.0 statistical package (Analysis of Moment Structure). The data collection technique used a questionnaire with a differential semantic scale. The parameter estimation results are in accordance with the hypothesis that entrepreneurial understanding significantly affects entrepreneurial interest with an estimated standardized coefficient value of 0.534 and P-Value ***, as well as entrepreneurship training has a positive effect on entrepreneurial interest with an estimated standardized coefficient value of 0.503 and P-Value *** and interest in entrepreneurship significantly affects business productivity with an estimated standardized coefficient of 0.361 and P-Value ***. From this research, it can be concluded that in the midst of the current covid-19 pandemic, students who are engaged in the world of entrepreneurship need to increase their training and understanding related to entrepreneurship, especially with the current digitalization era which is growing rapidly, there needs to be innovations that can make them survive in a better direction according to the times.

Extrinsic factors, endocrine mechanisms, and behavioral indicators of migratory restlessness in wintering whooper swans (Cygnus cygnus)

Extrinsic factors, endocrine mechanisms, and behavioral indicators of migratory restlessness were studied in wintering whooper swans (Cygnus cygnus) in the Sanmenxia Swan National Wetland Park in western Henan Province, central China. First, the fecal glucocorticoid metabolite (FGM) concentration was established and related to mean air temperature or photo period (day length) using simple linear or non-linear regression models. After a model selection procedure, the best fitted model revealed that an increase of FGM concentration was associated with an increase in the squared mean air temperature (R2 = 0.88). Other models showed an increasing FGM concentration to correspond with increasing values of day length, squared day length, and mean air temperature—however without statistical support. In a second step, behavioral frequencies of seven behaviors were condensed into three behavioral principal components (PCs) using principal components analysis. Behavioral PCs largely corresponded to three activity phases described for wintering whooper swans in central China and were correlated with the FGM concentration using Spearman's rank-order correlations. Results revealed a significant correlation between FGM and behavioral PC2 (positive factor loadings from vigilance and preening, negative loading from foraging). Finally, we tested for an effect of behavioral PCs on changes in winter home range size using a set of multiple linear regression models. Results of averaged model parameter estimates showed only the behavioral PC3 (positive factor loadings from fighting and calling, negative loading from locomotion) had a marginal significant effect on home range size. Results confirmed findings of previous studies on migratory restlessness in whooper swans. However, due to the small sample size (N = 15 weeks) the effect of PC3 on home range size was weak and should be viewed with caution.

Prediction of Reservoir Storage in Ungauged Reservoirs by Grouping Reservoir Characteristics

The reservoir in Korea is managed by the local government and Korea Rural Community Corporation. About 97% of agricultural reservoirs managed by local governments are ungauged reservoirs, and the water supply amount from the reservoirs is estimated by an indirect method. Reliable and accurate water supply amount a calculation of ungauged reservoirs is required. In this study, 200 reservoirs with observation data were selected for the prediction of the water supply amount in the ungauged reservoir. To evaluate the reliability of the parameters of the TANK model for each reservoir, 101 reservoirs that were judged as good or more than among the NSE and ROV analysis results were selected. The physical properties of 101 reservoirs and the parameter estimation results of the TANK model were grouped. The 101 reservoirs were estimated into a total of 6 groups, and representative values were presented for each group. After applying the parameters of the TANK model for each group, the gauged reservoir was assumed to be an ungauged reservoir, and then the observed and simulated inflows were compared and analyzed.

An alternative closed-form crash severity model with the non-identical, heavy-tailed, and asymmetric properties

Crash severity model is a classical topic in road safety research. The multinomial logit (MNL) model, as a basic discrete outcome method, is widely applied to measure the association between crash severity and possible risk factors. However, the MNL model has several assumptions and properties that are possibly not consistent with the actual crash mechanism, and therefore with the association measure for crash severity. One significant attribute is the variation in drivers’ safety perception. Risk-taking drivers tends to drive at a higher speed, which increases the likelihood of severe crashes. However, the variations in speed and other driving performance lead to the error in the utility function more profound. This violates the assumption of identical error distributions between different crash severity outcomes. In this paper, we propose a multinomial multiplicative (MNM) model, as an alternative for crash severity model. There are two possible formulations for the proposed MNM model: (1) Weibull and (2) Fréchet, according to the distributions of random propensities and subject to the signs of the systematic parts of the regression equation. The two heavy-tailed distributions can capture the effect of unobserved contributory factors on crash injury severity. Additionally, the MNM model can incorporate the effects of the non-identical, heavy-tailed, and asymmetric properties of the distribution, whereas the conventional MNL model cannot. Several operational considerations are also attempted in this study, including the specifications of the systematic parts and the interpretations of the parameters. The MNM model is further extended to the mixed MNM (MMNM) model by considering unobserved heterogeneities using random coefficients, while the mixed MNL (MMNL) model is used as the benchmark model. The proposed MMNM model is calibrated using the crash dataset obtained from the Guangdong Province, China. Results indicated that the proposed MMNM model outperformed the MMNL model in this case. Also, the results of parameter estimates are indicative to impact factors on crash severity as well as the design and implementation of policies. This justified the use of MMNM model as an alternative for crash severity model in practice. This is the first application of MMNM model in the traffic safety literature, it is worth exploring the application of other advanced multiplicative models for safety analysis in the future.

Diffusivity Estimation for Activator\u2013Inhibitor Models: Theory and Application to Intracellular Dynamics of the Actin Cytoskeleton

A theory for diffusivity estimation for spatially extended activator–inhibitor dynamics modeling the evolution of intracellular signaling networks is developed in the mathematical framework of stochastic reaction–diffusion systems. In order to account for model uncertainties, we extend the results for parameter estimation for semilinear stochastic partial differential equations, as developed in Pasemann and Stannat (Electron J Stat 14(1):547–579, 2020), to the problem of joint estimation of diffusivity and parametrized reaction terms. Our theoretical findings are applied to the estimation of effective diffusivity of signaling components contributing to intracellular dynamics of the actin cytoskeleton in the model organism Dictyostelium discoideum.

The Discovery and Interpretation of Evidence Accumulation Stages

To improve the understanding of cognitive processing stages, we combined two prominent traditions in cognitive science: evidence accumulation models and stage discovery methods. While evidence accumulation models have been applied to a wide variety of tasks, they are limited to tasks in which decision-making effects can be attributed to a single processing stage. Here, we propose a new method that first uses machine learning to discover processing stages in EEG data and then applies evidence accumulation models to characterize the duration effects in the identified stages. To evaluate this method, we applied it to a previously published associative recognition task (Application 1) and a previously published random dot motion task with a speed-accuracy trade-off manipulation (Application 2). In both applications, the evidence accumulation models accounted better for the data when we first applied the stage-discovery method, and the resulting parameter estimates where generally in line with psychological theories. In addition, in Application 1 the results shed new light on target-foil effects in associative recognition, while in Application 2 the stage discovery method identified an additional stage in the accuracy-focused condition — challenging standard evidence accumulation accounts. We conclude that the new framework provides a powerful new tool to investigate processing stages.

A meta-analysis of tumour response and relapse kinetics based on 34,881 patients: A question of cancer type, treatment and line of treatment

PurposeCancer disease burden is commonly assessed radiologically in solid tumours in support of response assessment via the RECIST criteria. These longitudinal data are amenable to mathematical modelling and these models characterise the initial tumour size, initial tumour shrinkage in responding patients and rate of regrowth as patient's disease progresses. Knowing how these parameters vary between patient populations and treatments would inform translational modelling approaches from non-clinical data as well as clinical trial design. Experimental designHere a meta-analysis of reported model parameter values is reported. Appropriate literature was identified via a PubMed search and the application of text-based clustering approaches. The resulting parameter estimates are examined graphically and with ANOVA. ResultsParameter values from a total of 80 treatment arms were identified based on 80 trial arms containing a total of 34,881 patients. Parameter estimates are generally consistent. It is found that a significant proportion of the variation in rates of tumour shrinkage and regrowth are explained by differing cancer and treatment: cancer type accounts for 66% of the variation in shrinkage rate and 71% of the variation in reported regrowth rates. Mean average parameter values by cancer and treatment are also reported. ConclusionsMathematical modelling of longitudinal data is most often reported on a per clinical trial basis. However, the results reported here suggest that a more integrative approach would benefit the development of new treatments as well as the further optimisation of those currently used.

A random parameter bivariate probit model for injury severities of riders and pillion passengers in motorcycle crashes

This study proposes a random parameter bivariate probit model to analyze risk factors on the crash injury severity of both motorcycle riders and passengers in a single modeling framework. The proposed model can not only account for the underlying correlation of common factors affecting the rider and its pillion passenger simultaneously, but also can capture the unobserved heterogeneity across crash samples. The case analysis is based on 3665 motorcycle-carrying-passenger crashes in Hunan province of China. Model comparisons show that the proposed random parameter bivariate probit model outperforms two conventional models in the goodness-of-fit. The results of parameter estimations show that, age and gender differences in passengers pose significant effects on injury severity of the rider in crashes. Specifically, when carrying a vulnerable passenger including women, children and elders, the rider is less likely to sustain severe injuries. But for injury severity of the passenger, these vulnerable passengers are more likely to suffer from severe injuries. Apart form age and gender attributes, factors including collision objects, helmet use, drunk riding, night without lights, peak periods, high-speed roads have significant effects on rider injury and/or passenger injury. Relevant suggestions to alleviate the injury severity for motorcycle-carrying-passengers crashes are recommended.

Research on Industry Index Fluctuation of Stock Market Based on SV-T Model

In a complex market environment, it is important to describe and predict the volatility of returns in various industries. In this paper, after ADF stability test is conducted on the return rate sequence of building materials, real estate, medicine biological, non-bank finance, and communication industry, SV-T modeling is carried out, and the parameter estimation range of SV-T model is obtained by using MCMC method and Winbugs software. After ten thousand iterations, under the condition that the Markov chain of parameters obtained by Gibbs sampling method converges, the parameter estimation results of SV-T model are obtained. According to the parameter estimation results of SV-T model in each industry, the difference of investment risk and volatility predictability among five target industries is compared and analyzed.