Parameter Estimation Research Articles

Modeling and Optimal Control of Infectious Diseases

We propose a stochastic model of infectious disease transmission that is more realistic than those found in the literature. The model is based on jump-diffusion processes. However, it is defined in such a way that the number of people susceptible to be infected decreases over time, which is the case for a population of fixed size. Next, we consider the problem of finding the optimal control of the proposed model. The dynamic programming equation satisfied by the value function is derived. Estimators of the various model parameters are obtained.

Modeling and Optimal Control of Infectious Diseases

We propose a stochastic model of infectious disease transmission that is more realistic than those found in the literature. The model is based on jump-diffusion processes. However, it is defined in such a way that the number of people susceptible to be infected decreases over time, which is the case for a population of fixed size. Next, we consider the problem of finding the optimal control of the proposed model. The dynamic programming equation satisfied by the value function is derived. Estimators of the various model parameters are obtained.

Adaptive field-oriented control of current-fed induction motors: a discrete-time implementation

Indirect field oriented control (IFOC) is the industry standard for high performance current-fed induction motors. It is well-known that this controller preserves stability in the face of large rotor resistance variations – which are inevitable in practical scenarios – but its performance is significantly degraded. To overcome this drawback many adaptation schemes have been reported, both in drives and control-oriented publications, with the overwhelming majority of these developments done in continuous-time. This, in spite of the fact that the IFOC, as well as their proposed adaptive versions, involve the solution of highly nonlinear differential equations, whose discretization is far from obvious. In this paper we try to reverse this trend and present a discrete-time estimator of the motor parameters and an adaptive IFOC based on it. It is shown that the proposed estimator and adaptive controller are globally convergent under very weak prior knowledge and excitation assumptions. Simulations, that validate the theoretical claims and illustrate the robustness of the schemes, are also presented.

A Study on the Object-Based High-Resolution Remote Sensing Image Classification of Crop Planting Structures in the Loess Plateau of Eastern Gansu Province

The timely and accurate acquisition of information on the distribution of the crop planting structure in the Loess Plateau of eastern Gansu Province, one of the most important agricultural areas in Western China, is crucial for promoting fine management of agriculture and ensuring food security. This study uses multi-temporal high-resolution remote sensing images to determine optimal segmentation scales for various crops, employing the estimation of scale parameter 2 (ESP2) tool and the Ratio of Mean Absolute Deviation to Standard Deviation (RMAS) model. The Canny edge detection algorithm is then applied for multi-scale image segmentation. By incorporating crop phenological factors and using the L1-regularized logistic regression model, we optimized 39 spatial feature factors—including spectral, textural, geometric, and index features. Within a multi-level classification framework, the Random Forest (RF) classifier and Convolutional Neural Network (CNN) model are used to classify the cropping patterns in four test areas based on the multi-scale segmented images. The results indicate that integrating the Canny edge detection algorithm with the optimal segmentation scales calculated using the ESP2 tool and RMAS model produces crop parcels with more complete boundaries and better separability. Additionally, optimizing spatial features using the L1-regularized logistic regression model, combined with phenological information, enhances classification accuracy. Within the OBIC framework, the RF classifier achieves higher accuracy in classifying cropping patterns. The overall classification accuracies for the four test areas are 91.93%, 94.92%, 89.37%, and 90.68%, respectively. This paper introduced crop phenological factors, effectively improving the extraction precision of the shattered agricultural planting structure in the Loess Plateau of eastern Gansu Province. Its findings have important application value in crop monitoring, management, food security and other related fields.

A noncommutative Bianchi I model with radiation

In this work, we study the dynamical evolution of a homogeneous and anisotropic, noncommutative (NC) Bianchi I (BI) model coupled to a radiation perfect fluid. Our first motivation is determining if the present model tends to a homogeneous and isotropic NC Friedmann–Robertson–Walker (FRW) model, during its evolution. In order to simplify our task, we use the Misner parametrization of the BI metric. In terms of that parametrization the BI metric has three metric functions: the scale factor [Formula: see text] and the two parameters [Formula: see text], which measure the spatial anisotropy of the model. Our second motivation is trying to describe the present accelerated expansion of the universe using noncommutativity (NCTY). The NCTY is introduced by two nontrivial Poisson brackets between some geometrical as well as matter variables of the model. We recover the description in terms of commutative variables by introducing some variables transformations that depend on the NC parameter. Using those variables’ transformations, we rewrite the total NC Hamiltonian of the model in terms of commutative variables. From the resulting Hamiltonian, we obtain the dynamical equations for a generic perfect fluid. In order to solve these equations, we restrict our attention to a model in which the perfect fluid is radiation. We solve, numerically, these equations and compare the NC solutions to the corresponding commutative ones. The comparison shows that the NC model may be considered as a possible candidate for describing the accelerated expansion of the universe. Finally, we obtain estimates for the NC parameter and compare the main results of the NC BI model coupled to radiation with the same NC BI model coupled to other perfect fluids. As our main result, we show that the solutions, after some time, produce an isotropic universe. Based on that result, we can speculate that the solutions may represent an initial anisotropic stage of our Universe, that later, due to the expansion, became isotropic.

Population dynamics of spotted scat, Scatophagus argus (Linnaeus 1766) from the Sundarbans of Bangladesh

In Bangladesh, the mangrove areas of the Sundarbans are a hotspot for the spotted scat (Scatophagus argus), as the estuarine tributeries are significant zones for fishing, local marketing and foreign trading. Due to the widespread capture of spotted scats in the wild for a variety of uses, parameters of population were estimated using length-frequency and body weight data gathered from various sites in the southwestern forest areas of the country to comprehend the stock's status. Between May 2022 and April 2023, 1280 S. argus individuals were sampled from Khulna, Bagerhat and Satkhira. To gain insight into the population dynamics of S. argus, estimations of the growth parameters, recruitment pattern, mortalities, and level of exploitation (E) were made. For S. argus, a bit of negetive allomentric growth (b = 2.95) was observed. Moreover, the growth performance index (φ′), the asymptotic length (TL∝) and the growth coefficient (K) were 2.83, 34.65, cm and 0.56 yr−1, respectively. Natural mortality (1.08 yr−1) was lower than fishing mortality (1.12 yr−1). In the wild, this species recruits throughout the year, reaching its peak between October and January. By fishing gear, 50 % of the S. argus were caught between 7.71 cm and 9.43 cm. Moreover, high fishing mortality was assumed in both the small group (11–15 cm) and the large group (29–33 cm) of S. argus by virtual population analysis (VPA). It was discovered that the E for S. argus was 0.51, over the maximum permissible limit (Emax = 0.48). In summary, it is discovered that the S. argus population is overexploited. Therefore, in order to preserve this popular fish species and avoid local extinction, spotted scat resources should be taken into account under appropriate fisheries management guidelines. These findings will guide policymakers to manage the species accordingly.

Locally sparse and robust partial least squares in scalar-on-function regression

We present a novel approach for estimating a scalar-on-function regression model, leveraging a functional partial least squares methodology. Our proposed method involves computing the functional partial least squares components through sparse partial robust M regression, facilitating robust and locally sparse estimations of the regression coefficient function. This strategy delivers a robust decomposition for the functional predictor and regression coefficient functions. After the decomposition, model parameters are estimated using a weighted loss function, incorporating robustness through iterative reweighting of the partial least squares components. The robust decomposition feature of our proposed method enables the robust estimation of model parameters in the scalar-on-function regression model, ensuring reliable predictions in the presence of outliers and leverage points. Moreover, it accurately identifies zero and nonzero sub-regions where the slope function is estimated, even in the presence of outliers and leverage points. We assess our proposed method’s estimation and predictive performance through a series of Monte Carlo experiments and an empirical dataset—that is, data collected in relation to oriented strand board. Compared to existing methods our proposed method performs favorably. Notably, our robust procedure exhibits superior performance in the presence of outliers while maintaining competitiveness in their absence. Our method has been implemented in the robsfplsr package in .

The filtering-based recursive least squares identification and convergence analysis for nonlinear feedback control systems with coloured noises

The coloured noise is ubiquitous in industrial processes. This paper addresses the identification problem for the nonlinear feedback systems with coloured noise. Firstly, a direct identification scheme based on the least squares principle is developed to estimate the whole parameters of the nonlinear feedback systems and the convergence analysis is carried out through the stochastic stability theory. Secondly, for the purpose of improving the estimation accuracy, a filtering-based identification framework is proposed by constructing a linear filter for filtering the input data, output data and the coloured noise, and the coloured noise is transformed into a white noise. This identification scheme based on the filtering technique can effectively reduce the adverse effects caused by coloured noise and parameter estimation accuracy is enhanced compared with the direct least squares algorithm. Meanwhile, the convergence analysis of the filtering-based identification algorithm is given to provide a theoretical analysis. Finally, the simulation example is carried out by performance test and comparison analysis and simulation results show the effectiveness of the proposed identification methods.

Pharmacokinetic-pharmacodynamic modelling and simulation of methotrexate dosing in patients with rheumatoid arthritis.

To develop a non-linear mixed-effects population pharmacokinetic and pharmacodynamic (PK-PD) model describing the change in the concentration of methotrexate polyglutamates in erythrocytes (ery-MTX-PGn with "n" number of glutamate, representing PK component) and how this relates to modified 28-joint Disease Activity Score incorporating erythrocyte sedimentation rate (DAS-28-3) for rheumatoid arthritis (RA), representing PD component. An existing PK model was fitted to data from a study consisting of 117 RA patients. The estimation of population PK-PD parameters was performed using stochastic approximation expectation maximisation algorithm in Monolix 2021R2. The model was used to perform Monte Carlo simulations of a loading dose regimen (50mg subcutaneous methotrexate as loading doses, then 20mg weekly oral methotrexate) compared to a standard dosing regimen (10mg weekly oral methotrexate for 2 weeks, then 20mg weekly oral methotrexate). Every 40 nmol/L increase in ery-MTX-PG3-5 total concentration correlated with 1-unit reduction in DAS-28-3. Significant covariate effects on the therapeutic response of methotrexate included the use of prednisolone in the first 4 weeks (positive use correlated with 25% reduction in DAS-28-3 when other variables were constant) and patient age (every 10-year increase in age correlated with 3.4% increase in DAS-28-3 when other variables were constant). 4 methotrexate loading doses led to a higher percentage of patients achieving a good/moderate response compared to the standard regimen (Week 4: 87.6% vs. 39.8%; Week 10: 64.7% vs. 57.0%). A loading dose regimen was more likely to achieve higher ery-MTX-PG concentration and better therapeutic response after 4 weeks of methotrexate treatment.

An active learning enhanced data programming (ActDP) framework for ECG time series

Abstract Supervised machine learning requires the estimation of multiple parameters by using large amounts of labelled data. Getting labelled data generally requires a substantial allocation of resources in terms of both cost and time. In such scenarios, weak supervised learning techniques like data programming (DP) and active learning (AL) can be advantageous for time-series classification tasks. These paradigms can be used to assign data labels in an automated manner, and time-series classification can subsequently be carried out on the labelled data. 

This work proposes a novel framework titled active learning enhanced data programming (ActDP). It uses DP and AL for ECG classification using single-lead data. ECG classification is pivotal in cardiology and healthcare for diagnosing a broad spectrum of heart conditions and arrhythmias. To establish the usefulness of this proposed ActDP framework, the experiments have been conducted using the MIT-BIH dataset with 94,224 ECG beats. DP assigns a probabilistic label to each ECG beat using nine novel polar labelling functions and a generative model in this work. Further, AL improves the result of DP by replacing the labels for sampled ECG beats of a generative model with ground truth. Subsequently, a discriminative model is trained on these labels for each iteration. 

The experimental results show that by incorporating AL to DP in the ActDP framework, the accuracy of ECG classification strictly increases from 85.7 % to 97.34 % in 58 iterations. Comparatively, the proposed framework (ActDP) has demonstrated a higher classification accuracy of 97.34 % In contrast, DP with data augmentation (DA) achieves an accuracy of 92.2 %, while DP without DA results in an accuracy of 85.7 %, majority vote yields an accuracy of 50.2 %, and the generative model achieves an accuracy of only 66.5 %.

Estimating error rates for single molecule protein sequencing experiments.

The practical application of new single molecule protein sequencing (SMPS) technologies requires accurate estimates of their associated sequencing error rates. Here, we describe the development and application of two distinct parameter estimation methods for analyzing SMPS reads produced by fluorosequencing. A Hidden Markov Model (HMM) based approach, extends whatprot, where we previously used HMMs for SMPS peptide-read matching. This extension offers a principled approach for estimating key parameters for fluorosequencing experiments, including missed amino acid cleavages, dye loss, and peptide detachment. Specifically, we adapted the Baum-Welch algorithm, a standard technique to estimate transition probabilities for an HMM using expectation maximization, but modified here to estimate a small number of parameter values directly rather than estimating every transition probability independently. We demonstrate a high degree of accuracy on simulated data, but on experimental datasets, we observed that the model needed to be augmented with an additional error type, N-terminal blocking. This, in combination with data pre-processing, results in reasonable parameterizations of experimental datasets that agree with controlled experimental perturbations. A second independent implementation using a hybrid of DIRECT and Powell's method to reduce the root mean squared error (RMSE) between simulations and the real dataset was also developed. We compare these methods on both simulated and real data, finding that our Baum-Welch based approach outperforms DIRECT and Powell's method by most, but not all, criteria. Although some discrepancies between the results exist, we also find that both approaches provide similar error rate estimates from experimental single molecule fluorosequencing datasets.

Design analysis and development of a gantry robot for multi-layer 3-D concrete printing with simulation and experimental validation

The present study investigates the design analysis and development of a framed gantry robot (FGR) for 3-D concrete printing. Finite element simulations are performed for design validation and parameter estimation to conduct detailed analysis of the concrete printer. The kinematic and dynamic models of FGR are used for workspace analysis, motion control study, and motor sizing for printer development. The motion trajectory of the printer nozzle tip is evaluated using MATLAB simulations. An integrated programmable logic controller (PLC) with an in-built human-machine interface (HMI) through control algorithms has been implemented for interactive, user-friendly, and precise motion control. A separate control system is developed to monitor the pumping power and feed rate of mortar mix during printing. The simulated control scheme is validated experimentally using trajectory tracking for various input printing paths through HMI, with a maximum tracking error of 1 mm. Successful concrete printing trials have been conducted using various mix-design, following extensive evaluation of different printer and material parameters at the laboratory scale. The proposed design concept, mechanisms, interactive control interface, and tracking accuracy have been validated through simulation and experimental results, for ensuring quality concrete prints by the developed printer. Material testing parameters have been evaluated experimentally for modeling various mix-designs. To further evaluate printing performance, simulations of layer deformation in printed structures are conducted using finite element analysis, incorporating non-linear material models and various contact layer interactions. The simulated results of printed layers of different shapes of structures show a close resemblance with the experimental concrete printing trials.

The impact of diffusion tensor imaging tractography settings on muscle fascicle architecture and diffusion parameter estimates: Tract length, completion, and curvature are most sensitive to tractography settings.

Diffusion-tensor (DT)-MRI tractography provides information about properties relevant to muscle health and function, including estimates of architectural properties such as fascicle length, pennation angle, and curvature and diffusion properties such as mean diffusivity (MD) and fractional anisotropy (FA). Tractography settings, including integration algorithms, thresholds for early tract termination, and tract smoothing approaches, impact the accuracy of the muscle property estimates. However, muscle DT-MRI tractography is performed using a variety of these settings, complicating comparisons between different studies. The effects of different tractography settings on muscle architecture estimates have not been fully explored, and optimized settings for muscle tractography have not yet been determined. We examined the influence of integration algorithm and termination check settings combined with a range of step sizes, termination criteria, and smoothing polynomial orders on tract characteristics, completion/reason for termination, and goodness of fit between fiber tracts and smoothing polynomials using 3-T DT-MR images of the lower leg muscles of seven healthy adults. We found that tract length and completion were highly sensitive to strict FA and intersegment angle thresholds (25%-69% reduction in complete fiber tracts from lowest to highest minimum FA threshold and 11%-36% reduction from highest to lowest intersegment angle threshold). Higher order polynomials (third and fourth order vs. second order) better fit the muscle fiber trajectories, but curvature estimates were highly sensitive to smoothing polynomial order (3.9-6.6 m-1 increase for second- vs. fourth-order fitting polynomials). Step size impacted curvature estimates, albeit to a lesser degree. Integration algorithm had little impact, and mean pennation angle, and tract-based FA and MD, were relatively insensitive to all parameters. The results demonstrate which muscle diffusion measures and architectural estimates are most sensitive to varying tractography settings and support the need for consistent reporting of tractography details to aid interpretation and comparison of results between studies.

A comprehensive forecast for cosmological parameter estimation using joint observations of gravitational waves and short γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\gamma $$\\end{document}-ray bursts

In the third-generation (3G) gravitational-wave (GW) detector era, multi-messenger GW observation for binary neutron star (BNS) merger events can exert great impacts on exploring cosmic expansion history. In this work, we comprehensively explore the potential of 3G GW standard siren observations in cosmological parameter estimations by considering 3G GW detectors and the future short γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\gamma $$\\end{document}-ray burst (GRB) detector THESEUS-like telescope joint observations. Based on a 10-year observation of different detection strategies, we predict that the numbers of detectable GW-GRB events are 334–674 with the redshifts z<3.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$z<3.5$$\\end{document} and the inclination angles ι<15∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\iota <15^{\\circ }$$\\end{document}. For the cosmological analysis, we consider the Λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Lambda $$\\end{document}CDM, wCDM, w0wa\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$w_0w_a$$\\end{document}CDM, and interacting dark energy (IDE) models. We find that GW can tightly constrain the Hubble constant with precision of 0.345–0.065%, but does not perform well in constraining other cosmological parameters. Fortunately, GW detection could effectively break the cosmological parameter degeneracies generated by the mainstream EM observations, CMB + BAO + SN (CBS). When combining mock GW data with CBS data, CBS + GW can tightly constrain the equation of state of dark energy w with a precision of 1.26%, close to the standard of precision cosmology. Meanwhile, the addition of GW to CBS could improve constraints on cosmological parameters by 34.2–94.9%. In conclusion, GW standard siren observations from 3G GW detectors could play a crucial role in helping solve the Hubble tension and probe the fundamental nature of dark energy.

Advanced extraction of PV parameters’ models based on electric field impacts on semiconductor conductivity using QIO algorithm

This article presents a novel approach for parameters estimation of photovoltaic cells/modules using a recent optimization algorithm called quadratic interpolation optimization algorithm (QIOA). The proposed formula is dependent on variable voltage resistances (VVR) implementation of the series and shunt resistances. The variable resistances reduced from the effect of the electric field on the semiconductor conductivity should be included to get more accurate representation. Minimizing the mean root square error (MRSE) between the measured (I–V) dataset and the extracted (V–I) curve from the proposed electrical model is the main goal of the current optimization problem. The unknown parameters of the proposed PV models under the considered operating conditions are identified and optimally extracted using the proposed QIOA. Two distinct PV types are employed with normal and low radiation conditions. The VVR TDM is proposed for (R.T.C. France) silicon PV operating at normal radiation, and eleven unknown parameters are optimized. Additionally, twelve unknown parameters are optimized for a Q6-1380 multi-crystalline silicon (MCS) (area 7.7 cm2) operating under low radiation. The efficacy of the QIOA is demonstrated through comparison with four established optimizers: Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA), and Sine Cosine Algorithm (SCA). The proposed QIO method achieves the lowest absolute current error values in both cases, highlighting its superiority and efficiency in extracting optimal parameters for both Single-Crystalline Silicon (SCS) and MCS cells under varying irradiance levels. Furthermore, simulation results emphasize the effectiveness of QIO compared to other algorithms in terms of convergence speed and robustness, making it a promising tool for accurate and efficient PV parameter estimation.

Influence of Apparatus Scale on Geogrid Monotonic and Cyclic/Post-Cyclic Pullout Behavior in Cohesive Soils

Geosynthetics have increasingly been applied to geotechnical engineering works due to their numerous advantages, including cost-effectiveness and their significant role in sustainable development. When geosynthetics are used as reinforcement in earth structures, such as embankments, retaining walls and bridge abutments, soil–geosynthetic interface shear behavior is a critical parameter involved in the design. This paper presents a series of monotonic and cyclic/post-cyclic pullout tests carried out to examine the apparatus scale effect on the pullout response of a geogrid embedded in two different soils. To assess the small-scale equipment feasibility, comparisons were made between pullout test parameters derived from small- and large-scale equipment. The test results indicate that, under a low confining stress of 25 kPa, using a smaller-sized apparatus results in lower values of geogrid pullout resistance and maximum mobilized shear stress, but higher values of confined tensile stiffness at low strains. On the other hand, as the confining stress increases (i.e., 50 kPa and 100 kPa), the difference between the results becomes less significant and similar trends are observed regardless of the equipment type. Adopting small-scale equipment enables obtaining soil–reinforcement interaction parameters using test procedures that are less time-consuming than those associated with large-scale pullout tests. However, proper scale effect correction factors may be considered for more consistent estimates of the interface strength parameters under low normal stress values.

Semi-Analytical Modeling of Transient Stream Drawdown and Depletion in Response to Aquifer Pumping.

Analytical and semi-analytical models for stream depletion with transient stream stage drawdown induced by groundwater pumping are developed to address a deficiency in existing models, namely, the use of a fixed stream stage condition at the stream-aquifer interface. Field data are presented to demonstrate that stream stage drawdown does indeed occur in response to groundwater pumping near aquifer-connected streams. A model that predicts stream depletion with transient stream drawdown is developed based on stream channel mass conservation and finite stream channel storage. The resulting models are shown to reduce to existing fixed-stage models in the limit as stream channel storage becomes infinitely large, and to the confined aquifer flow with a no-flow boundary at the streambed in the limit as stream storage becomes vanishingly small. The model is applied to field measurements of aquifer and stream drawdown, giving estimates of aquifer hydraulic parameters, streambed conductance, and a measure of stream channel storage. The results of the modeling and data analysis presented herein have implications for sustainable groundwater management.

Adventitious Error and Its Implications for Testing Relations Between Variables and for Composite Measurement Outcomes.

Wu and Browne (Psychometrika 80(3):571-600, 2015. https://doi.org/10.1007/s11336-015-9451-3 ; henceforth W &B) introduced the notion of adventitious error to explicitly take into account approximate goodness of fit of covariance structure models (CSMs). Adventitious error supposes that observed covariance matrices are not directly sampled from a theoretical population covariance matrix but from an operational population covariance matrix. This operational matrix is randomly distorted from the theoretical matrix due to differences in study implementations. W &B showed how adventitious error is linked to the root mean square error of approximation (RMSEA) and how the standard errors (SEs) of parameter estimates are augmented. Our contribution is to consider adventitious error as a general phenomenon and to illustrate its consequences. Using simulations, we illustrate that its impact on SEs can be generalized to pairwise relations between variables beyond the CSM context. Using derivations, we conjecture that heterogeneity of effect sizes across studies and overestimation of statistical power can both be interpreted as stemming from adventitious error. We also show that adventitious error, if it occurs, has an impact on the uncertainty of composite measurement outcomes such as factor scores and summed scores. The results of a simulation study show that the impact on measurement uncertainty is rather small although larger for factor scores than for summed scores. Adventitious error is an assumption about the data generating mechanism; the notion offers a statistical framework for understanding a broad range of phenomena, including approximate fit, varying research findings, heterogeneity of effects, and overestimates of power.

Parameter estimation of uncertain stock model using residual method optimized by genetic algorithm: valuation of vulnerable European and barrier options

Parameter estimation of uncertain stock model using residual method optimized by genetic algorithm: valuation of vulnerable European and barrier options

Mixed effect estimation in deep compartment models: Variational methods outperform first-order approximations.

This work focusses on extending the deep compartment model (DCM) framework to the estimation of mixed-effects. By introducing random effects, model predictions can be personalized based on drug measurements, enabling the testing of different treatment schedules on an individual basis. The performance of classical first-order (FO and FOCE) and machine learning based variational inference (VI) algorithms were compared in a simulation study. In VI, posterior distributions of the random variables are approximated using variational distributions whose parameters can be directly optimized. We found that variational approximations estimated using the path derivative gradient estimator version of VI were highly accurate. Models fit on the simulated data set using the FO and VI objective functions gave similar results, with accurate predictions of both the population parameters and covariate effects. Contrastingly, models fit using FOCE depicted erratic behaviour during optimization, and resulting parameter estimates were inaccurate. Finally, we compared the performance of the methods on two real-world data sets of haemophilia A patients who received standard half-life factor VIII concentrates during prophylactic and perioperative settings. Again, models fit using FO and VI depicted similar results, although some models fit using FO presented divergent results. Again, models fit using FOCE were unstable. In conclusion, we show that mixed-effects estimation using the DCM is feasible. VI performs conditional estimation, which might lead to more accurate results in more complex models compared to the FO method.