Estimation Of Model Parameters Research Articles

Joint extented Kalman filter-based parameter estimation in networked SEIR models with stochastic variability: analysis using COVID-19 daily data from southern India

Graph Laplacian diffusion is utilised to augment the susceptible-infected-removed (SEIR) epidemic model by integrating a weighted network and introducing variability in the transmission rate parameter to simulate population movement across network nodes. Employing the Joint Extended Kalman filter (JEKF), our objective is to derive three critical parameters: the transmission rate (β), recovery rate (γ), and latent period rate (α). The JEKF's iterative approach continuously anticipates and updates the system state based on current parameter estimations, making it adaptable for parameter estimation in dynamic systems. Integration of model predictions with observed data further enhances accuracy. Subsequently, we offer visual insights and demonstrate our methodology through experiments conducted on a simple tree network and a small-world Watts-Strogatz graph. Our ultimate aim is to precisely determine optimal parameter values to formulate effective COVID-19 management plans tailored for South India.

Identification and application of dynamic parameters of manipulator based on improved IRLS algorithm

Purpose To overcome the shortcomings of traditional dynamic parameter identification methods in accuracy and efficiency, this paper aims to propose a dynamic parameter identification method based on improved iterative reweighted least squares (IIRLS) algorithm. Design/methodology/approach First, Newton–Euler method is used to establish the dynamic model of the robot, which is linearized and reorganized. Then, taking the improved Fourier series as the excitation trajectory, the optimization model with objective function is established and optimized. Then, the manipulator runs the optimized trajectory and collects the running state of the joint. Finally, the iterative process of iterative reweighted least squares (IRLS) algorithm is improved by combining clustering algorithm and matrix inversion operation rules, and the dynamic model of robot is identified by using the processed collected data. Findings Experimental results show that, compared with the IRLS algorithm, the root mean square of the proposed IIRLS algorithm is reduced by 4.18% and the identification time is reduced by 94.92% when the sampling point is 1001. This shows that IIRLS algorithm can identify the dynamic model more accurately and efficiently. Originality/value It effectively solves the problem of low accuracy and efficiency of parameter identification in robot dynamic model and can be used as an effective method for parameter estimation of robot dynamic model, which is of great significance to the research of control method based on robot model.

Hybrid Brown-Bear and Hippopotamus Algorithms with Fractional Order Chaos Maps for Precise Solar PV Model Parameter Estimation

The rise in photovoltaic (PV) energy utilization has led to increased research on its functioning, as its accurate modeling is crucial for system simulations. However, capturing nonlinear current–voltage traits is challenging due to limited data from cells’ datasheets. This paper presents a novel enhanced version of the Brown-Bear Optimization Algorithm (EBOA) for determining the ideal parameters for the circuit model. The presented EBOA incorporates several modifications aimed at improving its searching capabilities. It combines Fractional-order Chaos maps (FC maps), which support the BOA settings to be adjusted in an adaptive manner. Additionally, it integrates key mechanisms from the Hippopotamus Optimization (HO) to strengthen the algorithm’s exploitation potential by leveraging surrounding knowledge for more effective position updates while also improving the balance between global and local search processes. The EBOA was subjected to extensive mathematical validation through the application of benchmark functions to rigorously assess its performance. Also, PV parameter estimation was achieved by combining the EBOA with a Newton–Raphson approach. Numerous module and cell varieties, including RTC France, STP6-120/36, and Photowatt-PWP201, were assessed using double-diode and single-diode PV models. The higher performance of the EBOA was shown by a statistical comparison with many well-known metaheuristic techniques. To illustrate this, the root mean-squared error values achieved by our scheme using (SDM, DDM) for RTC France, STP6-120/36, and PWP201 are as follows: (8.183847 × 10−4, 7.478488 × 10−4), (1.430320 × 10−2, 1.427010 × 10−2), and (2.220075 × 10−3, 2.061273 × 10−3), respectively. The experimental results show that the EBOA works better than alternative techniques in terms of accuracy, consistency, and convergence.

Stochastic behavior of green bond premiums

This paper aims to examine the stochastic behavior of green bond premiums that can characterize the benefits of green bonds. We propose a novel affine model of green bond pricing with mean-reverting interest rates and green bond premiums and a new model parameter estimation method using conventional and green bond prices to capture the stochastic behavior. Then, the model parameter estimation results demonstrate mean-reverting stochastic behavior for conventional bond yield and green bond premium using the US and EU green bond indexes for Corporate and three corporate bond indexes for intermediate, total, and long-term periods of the Bloomberg Fixed Income Indices from November 3, 2014 to December 11, 2020. Comparative statics using simulated green bond premiums show that green bond premiums orient toward negative values in nature. Moreover, the stochastic behavior of green bond premiums demonstrates that the greenness of green bonds has a downward effect on interest rates in COVID-19 and has a mitigating impact on liquidity risk in corporate bond markets. These results confirm the benefits of green bonds. Finally, the discussions secure the validity of the green bond pricing model by conducting econometric analyses of regime switching, principal component ones, and the GARCH (1,1) model.

Cross-Validatory Z-Residual for Diagnosing Shared Frailty Models

Accurate model performance assessment in survival analysis is imperative for robust predictions and informed decision-making. Traditional residual diagnostic tools like martingale and deviance residuals lack a well-characterized reference distribution for censored regression, making numerical statistical tests based on these residuals challenging. Recently, the introduction of Z-residuals for diagnosing survival models addresses this limitation. However, concerns arise from conventional methods that use the entire dataset for both model parameter estimation and residual assessment, which may cause optimistic biases. This article introduces cross-validatory Z-residuals as an innovative approach to address these limitations. Employing a cross-validation (CV) framework, the method systematically partitions the dataset into training and testing sets to reduce the optimistic bias. Our simulation studies demonstrate that, for goodness-of-fit tests and outlier detection, cross-validatory Z-residuals are significantly more powerful (e.g., power increased from 0.2 to 0.6). and more discriminative (e.g., AUC increased from 0.58 to 0.85) than Z-residuals without CV. We also compare the performance of Z-residuals with and without CV in identifying outliers in a real application that models the recurrence time of kidney infection patients. Our findings suggest that cross-validatory Z-residuals can identify outliers, which Z-residuals without CV fail to identify. The CV Z-residual is a more powerful tool than the No-CV Z-residual for checking survival models, particularly in goodness-of-fit tests and outlier detection. We have published a generic function, which is collected in an R package called Zresidual, for computing CV Z-residual for the output of the widely used survival R package.

A novel structure adaptive new information priority grey Bernoulli model and its application in China's renewable energy production

At present, the global energy structure is undergoing major changes. China is in the transition period of energy structure. Accurately anticipating future energy trends is critical for China's energy structure and modernization. Considering the uncertainty and sparsity characteristics of China's energy system sequence, this study examines China's renewable energy scenario using the grey model with limited sample and uncertain system modelling features. Renewable energy is affected by a variety of uncertain factors, exhibiting a range of complicated traits including nonlinearity, periodicity and random volatility. The traditional grey model has been difficult to appropriately predict its future evolution. This paper focuses on the adaptability of the model, optimizes and improves the accumulation operator and model structure, and establishes a fractional-order structural self-adaptation grey Bernoulli model based on new information priority. Firstly, based on the new information priority accumulation operator, it is extended to the fractional order. In terms of model structure, combined with NGBM(1,1) model, SADGM(1,1) model and FPGM(1,1) model, periodic fluctuation term and nonlinear power term are included to improve the model's capacity to capture nonlinear, fluctuating and periodic features, and enhance the adaptability and flexibility of the model. The backward difference technique yields the model's parameter estimation and temporal response sequence. Based on the results of the algorithm comparison experiment, the Improved Grey Wolf Optimization Algorithm is chosen to optimize the structural parameters of the model in an effort to enhance its performance. The performance comparison experiment of the model was designed, and three cases of China's hydropower generation, China's renewable energy power generation installed capacity and China's solar energy quarterly power generation were selected to compare the performance with a variety of grey prediction models. Monte-Carlo simulation and probability density analysis were utilized to confirm the stability and accuracy of the proposed model. The results show that the proposed FSANGBM(1,1) model can handle data series of renewable energy with nonlinear, volatile, and periodic features with high prediction ability. Finally, the model is applied to forecast three cases' future development trends.

M-estimation for linear models with exchangeable errors

This paper focuses on the estimation of parameters in linear models with fixed regressors and exchangeable errors, especially, these errors are assumed to be a subset of an infinite exchangeable sequence. We propose a modified M-estimation to estimate the slope parameter, which outperforms the traditional M-estimation when the random errors are exchangeable. The modified M-estimation utilizes a design point centralization technique, leading to a more suitable estimator with improved theoretical properties. To establish the asymptotic properties of the modified M-estimator, we also develop a weighted central limit theorem for exchangeable sequences, which is of independent interest. Regarding the intercept, we prove that there is no consistent estimator for the intercept when the exchangeable errors have non-zero covariance, and we propose an unbiased estimator for the intercept as a viable compromise. Based on our theoretical findings, we develop a simulation-based approach to approximate the confidence set of the slope, and we establish a prediction model specifically designed for linear models with exchangeable errors. Additionally, we illustrate the finite sample performance of the proposed method through various numerical simulations and a real data application.

Tillage Depth Detection and Control Based on Attitude Estimation and Online Calibration of Model Parameters

Aiming at solving the problems of inaccurate tillage depth detection and unstable tillage depth control, this study proposes a tillage depth detection and control method based on attitude estimation and online calibration of model parameters. First, a tillage depth detection model based on the attitude measurement of the plow is established. The attitude estimation method is designed to measure the horizontal attitude of the plow. An adaptive Kalman filter is utilized to perform an online calibration of the model parameters between the rotation angle of the lifting arm and the pitch angle of the plow. The dynamic and accurate detection of tillage depth is achieved using the tillage depth detection model. Second, a tillage depth control device using an STM32 microcontroller is developed in this study. The PID controller controls the tractor’s suspension system to adjust the plow through the solenoid valve, thus achieving stable control of the tillage depth. Finally, the experimental results obtained from simulation and field tests show that the tillage depth can be stably controlled within the set target interval during the tractor plowing operation, proving the effectiveness and feasibility of the proposed tillage depth detection and control method.

Illumination-guided dual-branch fusion network for partition-based image exposure correction

Images captured in the wild often suffer from issues such as under-exposure, over-exposure, or sometimes a combination of both. These images tend to lose details and texture due to uneven exposure. The majority of image enhancement methods currently focus on correcting either under-exposure or over-exposure, but there are only a few methods available that can effectively handle these two problems simultaneously. In order to address these issues, a novel partition-based exposure correction method is proposed. Firstly, our method calculates the illumination map to generate a partition mask that divides the original image into under-exposed and over-exposed areas. Then, we propose a Transformer-based parameter estimation module to estimate the dual gamma values for partition-based exposure correction. Finally, we introduce a dual-branch fusion module to merge the original image with the exposure-corrected image to obtain the final result. It is worth noting that the illumination map plays a guiding role in both the dual gamma model parameters estimation and the dual-branch fusion. Extensive experiments demonstrate that the proposed method consistently achieves superior performance over state-of-the-art (SOTA) methods on 9 datasets with paired or unpaired samples. Our codes are available at https://github.com/csust7zhangjm/ExposureCorrectionWMS.

Modeling the Impact of Human Awareness and Insecticide Use on Malaria Control: A Fractional-Order Approach

In this research work, we developed a fractional-order model for the transmission dynamics of malaria, incorporating two control strategies: health education campaigns and the use of insecticides. The theoretical analysis of the model is presented, including the computation of disease-free equilibrium and basic reproduction number. We analyzed the stability of the proposed model using a well-formulated Lyapunov function. Furthermore, model parameter estimation was carried out using real data from malaria cases reported in Zimbabwe. We found that the fractional-order model provided a better fit to the real data compared to the classical integer-order model. Sensitivity analysis of the basic reproduction number was performed using computed partial rank correlation coefficients to assess the effect of each parameter on malaria transmission. Additionally, we conducted numerical simulations to evaluate the impact of memory effects on the spread of malaria. The simulation results indicated that the order of derivatives significantly influences the dynamics of malaria transmission. Moreover, we simulated the model to assess the effectiveness of the proposed control strategies. Overall, the interventions were found to have the potential to significantly reduce the spread of malaria within the population.

Modern contraceptive utilization among street beggar women in the Sidama region of Ethiopia using bayesian model approach

IntroductionContraceptive methods are techniques used to prevent pregnancy during sexual intercourse. The primary goal of contraception is to inhibit the fertilization of an egg by sperm or to prevent the implantation of a fertilized egg in the uterus. Unwanted pregnancy is a major challenge for street beggar women. Due to limited use of modern contraception, unwanted pregnancy and STDs are key issues faced by this population. This study aims to assess modern contraceptive utilization and explore factors influencing contraceptive choices among street beggar women in the Sidama region, Ethiopia by using Bayesian logistic approaches.MethodsA cross-sectional quantitative study was conducted on reproductive-aged (15–49) street beggar women in the Sidama Region. Data was collected using a pre-tested, structured interview questionnaire in the local language from each town in the Sidama region. Descriptive and inferential statistics were used to analyze the data using STATA version 17. A Bayesian logistic approach, with Markov Chain Monte Carlo methods, was used to model the association between modern contraceptive utilization and independent variables, allowing for estimation of model parameters and credible intervals.ResultThe study found that 38.9% of street women in the Sidama region used modern contraceptives, primarily implants and injections. Bayesian logistic regression identified key factors influencing contraceptive use including, women aged 25–34 (AOR: 2.69 (95% CI: 1.57, 4.26)), married women (AOR: 4.91 (95% CI: 2.67, 8.51)), Widowed women (AOR: 2.81 (95% CI: 1.49)) and educated women (AOR: 2.16 (95% CI: 1.48, 3.01)) were more likely to use contraceptives. In contrast, those living on the street (AOR: 0.12 (95% CI: 0.05, 0.27)), with no history of pregnancy (AOR: 0.07 (95% CI: 0.02, 0.16)), and no sexual activity in the last six months (AOR: 0.11 (95% CI: 0.01, 0.03)), had lower usage rates. Additionally, women whose only job was begging (AOR: 0.43 (95% CI: 0.13, 0.92)) and those lacking health advice (AOR: 0.25 (95% CI: 0.07, 0.59)) also showed reduced utilization. Lack of discussion about contraceptives with partners (AOR: 0.18 (95% CI: 0.08, 0.33)) and no women's approval (AOR: 0.18 (95% CI: 0.08, 0.33)) further contributed to lower usage. Conversely, good knowledge of modern contraceptives was positively associated with utilization (AOR: 2.33 (95% CI: 1.44, 3.64)).ConclusionThe findings of this study identified several factors significantly associated with modern contraceptive use among street beggar women in the Sidama region, including age, marital status, Education level, disability, Job in addition to begging, Current living situation, history of sexual assault/rape, History of pregnancy after street life, discussion of modern contraception with a sexual partner, Advice from a health professional, Sexual activity in the last six months, time taken to arrive at a health facility on foot, approval of modern contraception, and knowledge. The main barriers reported were fear of side effects and lack of knowledge about contraceptive methods. The study suggests that concerned stakeholders should intervene to address these factors and improve modern contraceptive uptake in this vulnerable population.

Development of the Aslam-Ahmad estimator for the Cox proportional and hazards regression model with multicollinearity

In the current article, we develop the Aslam-Ahmad estimator i.e. the modified Liu-ridge-type estimator as proposed by Aslam and Ahmad [Aslam M, Ahmad S. The modified Liu-ridge-type estimator: a new class of biased estimators to address multicollinearity. Commun Statist Simul Comput. 2022] for the Cox proportional hazards regression model. It is common to use the maximum partial likelihood approach for the parameter estimation of the Cox proportional hazards model. However, when there is considerable multicollinearity among the regressors, this estimator is unable to give stable estimates for the unknown regression coefficients. To vanquish the problem, the literature suggests many alternative biased estimators. Following this, we propose to tailor the Aslam-Ahmad estimator for the Cox proportional hazards model and define its properties. The performance of the proposed estimator with the available estimators has been evaluated by the Monte Carlo simulations using the scalar mean squared error criterion. A numerical example has also been provided for the illustration.

Modeling Zika Virus Disease Dynamics with Control Strategies

In this research, we formulated a fractional-order model for the transmission dynamics of Zika virus, incorporating three control strategies: health education campaigns, the use of insecticides, and preventive measures. We conducted a theoretical analysis of the model, obtaining the disease-free equilibrium and the basic reproduction number, and analyzing the existence and uniqueness of the model. Additionally, we performed model parameter estimation using real data on Zika virus cases reported in Colombia. We found that the fractional-order model provided a better fit to the real data compared to the classical integer-order model. A sensitivity analysis of the basic reproduction number was conducted using computed partial rank correlation coefficients to assess the impact of each parameter on Zika virus transmission. Furthermore, we performed numerical simulations to determine the effect of memory on the spread of Zika virus. The simulation results showed that the order of derivatives significantly impacts the dynamics of the disease. We also assessed the effect of the control strategies through simulations, concluding that the proposed interventions have the potential to significantly reduce the spread of Zika virus in the population.

A systematic approach for estimating colloidal particle adsorption model parameters

The estimation of ion-exchange chromatography model parameters is crucial to enable efficient model-assisted biopharmaceutical downstream process development. Model calibration methods can be hindered by model limitations combined with parameter correlations, leading to time-consuming repeated parameter estimations. While Steric Mass Action isotherm estimation methods exist, there is a need for a systematic approach to estimate model parameters for an emerging Colloidal Particle Adsorption (CPA) model proposed by Briskot et al. This study presents a novel strategy that addresses this challenge, offering significant improvements.Through a parameter sensitivity analysis, we identified key levers for improved CPA parameter estimation, enabling the prediction of elution behavior for low and high load densities in gradient and step elution mode. This analysis also revealed the correlation structure of parameters, allowing the establishment of a minimalized experimental data set for parameter estimation, by using one breakthrough, a high load and three low load density gradient elution experiments. Our workflow leverages a surrogate-assisted global-optimization tool, minimizing computationally expensive function evaluations during parameter fitting. Furthermore, we employed a customized objective function, specifically adapted to the model structure and sensitivity results, to enhance the solver's performance.Our strategy was tested on three model proteins with molecular weights of approximately 50, 150 and 200 kDa using a strong cation exchange Poros 50 HS resin. Our final approach enabled high throughput CPA model calibration for single components. The resulting CPA models were able to describe non-binding protein-pulses, low and high-loaded gradient elution, break through, as well as isocratic elution experiments.

Functional connectotomy of a whole-brain model reveals tumor-induced alterations to neuronal dynamics in glioma patients

Abstract Brain tumors can induce pathological changes in neuronal dynamics that are reflected in functional connectivity measures. Here, we use a whole-brain modeling approach to investigate pathological alterations to neuronal activity in glioma patients. By fitting a Hopf whole-brain model to empirical functional connectivity, we investigate glioma-induced changes in optimal model parameters. We observe considerable differences in neuronal dynamics between glioma patients and healthy controls, both on an individual and population-based level. In particular, model parameter estimation suggests that local tumor pathology causes changes in brain dynamics by increasing the influence of interregional interactions on global neuronal activity. Our approach demonstrates that whole-brain models provide valuable insights for understanding glioma-associated alterations in functional connectivity.

Parameter estimation and hypothesis tests in logistic model for complex correlated data

Observations are frequently generated in clinical trials from correlated multiple organs (or parts) of individuals. The statistical inference is little about conducting regression analysis based on such data. This paper first develops a logistic regression for correlated multiple responses using a stable correlation binomial (SCB) model. Then, we obtain maximum likelihood estimators (MLEs) of unknown parameters through a fast quadratic lower bound (QLB) algorithm. Further, likelihood ratio, score and Wald statistics are used to test the effect of covariates based on the MLEs. Finally, the QLB algorithm and asymptotic tests are evaluated through simulations and applied to real dental data.

Caged Markov process – A continuous-time framework for modeling a constrained Markov process within a freely-evolving Markov process

We develop a continuous-time modeling framework for describing a bivariate Markov system in which the transition mechanism of one Markov process is constrained by another freely-evolving Markov process. The notion of constraint parameters is introduced to explicitly quantify the degree of constraints toward the constrained process within the coupling scheme. A complete construction of the resulting bivariate Markov model is provided with a closed-form formulation of its transition rate matrix. Several attributes of the model are examined, in particular, the behavior of the bivariate model when the two underlying Markov processes are independent from each other. This enables us to properly define a measurement of the overall strength of constraints based on the proposed constraint parameters. Model parameter estimation is discussed and demonstrated with two simulations, including a coupling of two independent Markov processes and a strongly coupled bivariate Markov process. The proposed model is further applied to real-world datasets in the context of stock index time series analysis.

Optimizing parameter estimation in hydrological models with convolutional neural network guided dynamically dimensioned search approach

Hydrological model calibration plays a crucial role in estimating optimal parameters for accurate simulation. Estimation of parameters is inevitable in hydrological modeling due to the challenge of directly measuring them, as most parameters are conceptual descriptions of physical processes. Modelers commonly employ optimization algorithms for calibrating hydrological models. However, these algorithms often pose computational challenges, especially when dealing with complex physics-based and distributed models. To address these challenges, our study introduces a novel approach called hydroCNN+DDS. By leveraging the strengths of Convolutional Neural Networks (CNN) and the Dynamically Dimensioned Search (DDS) algorithm, hydroCNN+DDS simplifies the model calibration process in complex physics-based models. This approach enables to capture the general patterns and relationships between discharge time series and parameters without compromising the underlying physics. We use hydroCNN+DDS to estimate parameters in the highly parameterized hydrological model, Structure for Unifying Multiple Modeling Alternatives (SUMMA) using hourly observed discharge. Notably, hydroCNN quickly generates sub-optimal parameters, serving as a good initial solution for DDS. This initialization aids DDS in converging faster towards an optimal solution. One of the notable advantages of the hydroCNN+DDS approach is its potential for spatial and temporal transferability. This feature proves valuable in dynamic systems and regions with limited historical data, expanding the applicability of the methodology. Furthermore, our proposed methodology is versatile and can be applied to any simple or complex models, accommodating any variables of interest. The best practices of good model calibration are followed in our approach.

Fast Generalized Spatial Multilevel blockNNGP Modeling

Typical geostatistical models only consider the case where we observe one response at each location. However, the situation with multiple replicates at spatial locations is seldom discussed. Moreover, the generalized spatial Gaussian process models encounter computational difficulties when the size of the spatial domain becomes massive. Thus, fast generalized spatial multilevel models that use block nearest neighbor Gaussian process to scale to large datasets are introduced. The proposed method uses integrated nested Laplace approximation (INLA) to avoid long sequential updates of the Markov chain Monte Carlo (MCMC) methods. A simulation study is performed under different response distributions to show the model parameter estimation capacity, computational efficiency, and prediction performance. Finally, the proposed models are fitted to the data of Beijing housing transactions to predict the sales price of houses at unobserved locations. The studies demonstrate that the proposed models have advantages in fitting and prediction, making the interpretation better substantiated.

Performance Evaluation of QT-RR Adaptation Time Lag Estimation in Exercise Stress Testing.

Slower adaptation of the QT interval to sudden changes in heart rate has been identified as a risk marker of ventricular arrhythmia. The gradual changes observed in exercise stress testing facilitates the estimation of the QT-RR adaptation time lag. The time lag estimation is based on the delay between the observed QT intervals and the QT intervals derived from the observed RR intervals using a memoryless transformation. Assuming that the two types of QT interval are corrupted with either Gaussian or Laplacian noise, the respective maximum likelihood time lag estimators are derived. Estimation performance is evaluated using an ECG simulator which models change in RR and QT intervals with a known time lag, muscle noise level, respiratory rate, and more. The accuracy of T-wave end delineation and the influence of the learning window positioning for model parameter estimation are also investigated. Using simulated datasets, the results show that the proposed approach to estimation can be applied to any changes in heart rate trend as long as the frequency content of the trend is below a certain frequency. Moreover, using a proper position of the learning window for exercise so that data compensation reduces the effect of nonstationarity, a lower mean estimation error results for a wide range of time lags. Using a clinical dataset, the Laplacian-based estimator shows a better discrimination between patients grouped according to the risk of suffering from coronary artery disease. Using simulated ECGs, the performance evaluation of the proposed method shows that the estimated time lag agrees well with the true time lag.