Model Estimation Research Articles

Validation and Comparison of Non-stationary Cognitive Models: A Diffusion Model Application

AbstractCognitive processes undergo various fluctuations and transient states across different temporal scales. Superstatistics are emerging as a flexible framework for incorporating such non-stationary dynamics into existing cognitive model classes. In this work, we provide the first experimental validation of superstatistics and formal comparison of four non-stationary diffusion decision models in a specifically designed perceptual decision-making task. Task difficulty and speed-accuracy trade-off were systematically manipulated to induce expected changes in model parameters. To validate our models, we assess whether the inferred parameter trajectories align with the patterns and sequences of the experimental manipulations. To address computational challenges, we present novel deep learning techniques for amortized Bayesian estimation and comparison of models with time-varying parameters. Our findings indicate that transition models incorporating both gradual and abrupt parameter shifts provide the best fit to the empirical data. Moreover, we find that the inferred parameter trajectories closely mirror the sequence of experimental manipulations. Posterior re-simulations further underscore the ability of the models to faithfully reproduce critical data patterns. Accordingly, our results suggest that the inferred non-stationary dynamics may reflect actual changes in the targeted psychological constructs. We argue that our initial experimental validation paves the way for the widespread application of superstatistics in cognitive modeling and beyond.

Estimation of simultaneous equation models by backpropagation method using stochastic gradient descent

Simultaneous equation model (SEM) is an econometric technique traditionally used in economics but with many applications in other sciences. This model allows the bidirectional relationship between variables and a simultaneous relationship between the equation set. There are many estimators used for solving an SEM. Two-steps least squares (2SLS), three-steps least squares (3SLS), indirect least squares (ILS), etc. are some of the most used of them. These estimators let us obtain a value of the coefficient of an SEM showing the relationship between the variables. There are different works to study and compare the estimators of an SEM comparing the error in the prediction of the data, the computational cost, etc. Some of these works study the estimators from different paradigms such as classical statistics, Bayesian statistics, non-linear regression models, etc. This work proposes to assume an SEM as a particular case of an artificial neural networks (ANN), considering the neurons of the ANN as the variables of the SEM and the weight of the connections of the neurons the coefficients of the SEM. Thus, backpropagation method using stochastic gradient descent (SGD) is proposed and studied as a new method to obtain the coefficient of an SEM.

Data-driven Approximation of Cumulative Distribution Function Using Particle Swarm Optimization based Finite Mixtures of Logistic Distribution

This paper proposes a data-driven approximation of the Cumulative Distribution Function using the Finite Mixtures of the Cumulative Distribution Function of Logistic distribution. Since it is not possible to solve the logistic mixture model using the Maximum likelihood method, the mixture model is modeled to approximate the empirical cumulative distribution function using the computational intelligence algorithms. The Probability Density Function is obtained by differentiating the estimate of the Cumulative Distribution Function. The proposed technique estimates the Cumulative Distribution Function of different benchmark distributions. Also, the performance of the proposed technique is compared with the state-of-the-art kernel density estimator and the Gaussian Mixture Model. Experimental results on κ−μ distribution show that the proposed technique performs equally well in estimating the probability density function. In contrast, the proposed technique outperforms in estimating the cumulative distribution function. Also, it is evident from the experimental results that the proposed technique outperforms the state-of-the-art Gaussian Mixture model and kernel density estimation techniques with less training data.

Impact of local sports policies in the prevalence of cardiovascular disease: An assessment with spatial data analysis

According to the World Health Organization, 17 million people die every year of cardiovascular diseases (CVD), particularly heart attacks and strokes. More than half of all deaths across the Europe are caused by CVDs. 80 % of premature deaths from these causes could be avoided by controlling the main risk factors: tobacco, unhealthy diet and physical inactivity.Local authorities provide sport and recreation facilities to their communities. They also have a central role in forging partnerships with public and private sector to enhance the local sport delivery system.The present paper consists of an empirical exercise about the statistical relationship between the financial effort of Portuguese municipalities in the implementation of sports policies and the prevalence of CVDs. The estimation of a Spatial Autocorrelation Model confirms the strong spatial dependence between neighbouring municipalities and validates the existence of a positive impact of local sports policies on mortality rate from cardiovascular diseases. The results raise the need to reinforce local policies to promote active living based on holistic approaches to combating heart disease and promoting healthy lifestyles in partnerships with health professionals, educators, and community leaders.

Semiparametric estimation of a principal functional coefficient panel data model with cross-sectional dependence and its application to cigarette demand

In this paper, we consider the estimation of functional coefficient panel data models with cross-sectional dependence. Borrowing the principal component structure, the functional coefficient panel data models can be transformed into a semiparametric panel data model. Combining the local linear dummy variable technique and profile least squares method, we develop a semiparametric profile method to estimate the coefficient functions. A gradient-descent iterative algorithm is employed to enhance computation speed and estimation accuracy. The main results show that the resulting parameter estimator enjoys asymptotic normality with a NT convergence rate and the nonparametric estimator is asymptotically normal with a nonparametric convergence rate NTh when both the number of cross-sectional units N and the length of time series T go to infinity, under some regularity conditions. Monte Carlo simulations are carried out to evaluate the proposed methods, and an application to cigarette demand is investigated for illustration.

Reliability modeling for perception systems in autonomous vehicles: A recursive event-triggering point process approach

Ensuring the reliability of sensor-fusion-based perception systems is crucial for the safe deployment of autonomous vehicles. Such systems function through a sequence of interconnected stages, where errors in upstream stages may propagate to downstream stages and trigger additional errors. The cross-stage error propagation conceptually exists and makes errors in different stages, not independent, posing model challenges, estimation challenges, and data challenges for reliability modeling. The existing methods cannot be applied to address all these challenges. Thus, this paper presents a recursive event-triggering point process to explicitly consider the error propagation based on the simulated data. The data are simulated from a proposed error injection framework, which can generate various errors from a sequence of interconnected stages in a perception system. The latent and probabilistic error propagation information is incorporated into a modified expectation–maximization (EM) algorithm for parameter estimation. The numerical and physics-based simulation case studies demonstrate the prediction accuracy and interpretability of the proposed modeling methodology.

Spatiotemporal evolution and driving factors of the coupling coordination of the population‒land‒water‒industry system in the lower Yellow River

Exploring the interaction and coupling effects within the population‒land‒water‒industry (PLWI) system is conducive to promoting high-quality regional sustainable development. Taking the lower Yellow River during the period from 2000 to 2020 as a research sample, this study used the entropy weight TOPSIS method, the coupling coordination degree (CCD) model and kernel density estimation to synthetically evaluate the CCD of the PLWI system. The GeoDetector model was applied to explore the factors influencing the CCD of the PLWI system considering the nonlinear relationship. The major results can be summarized as follows: (1) From 2000 to 2020, the comprehensive development index (CDI) of the population, land, water and industry subsystems followed a gradual upward trend in the lower Yellow River, increasing by 0.293, 0.033, 0.111 and 0.369, respectively. However, the CDI of the land subsystem varied greatly between regions. Some cities, such as Jinan, Jining and Binzhou, experienced large declines in the CDI of the land subsystem, from 0.433, 0.534 and 0.572 to 0.358, 0.481 and 0.522, respectively. (2) The CCD of the PLWI system in the lower Yellow River showed an upward trend, increasing from 0.481 to 0.678, and became more concentrated during 2000–2020. Most of the region transitioned from near disorder to primary coordination. (3) Factors such as number of health technicians per 10,000 people, average salary, number of college students per 10,000 people, per capita GDP and per capita education expenditure were critical to the coordinated development of the PLWI system, the explanatory powers were 0.644, 0.639, 0.610, 0.498 and 0.455, respectively. Finally, this study proposed three policy recommendations to improve coupling coordination in the lower Yellow River Basin: Improving population quality, promoting green technology and rational land planning.

Robust model averaging approach by Mallows-type criterion.

Model averaging is an important tool for treating uncertainty from model selection process and fusing information from different models, and has been widely used in various fields. However, the most existing model averaging criteria are proposed based on the methods of ordinary least squares or maximum likelihood, which possess high sensitivity to outliers or violation of certain model assumption. For the mean regression, no optimal robust methods are developed. To fill this gap, in our paper, we propose an outlier-robust model averaging approach by Mallows-type criterion. The idea is that we first construct a generalized M (GM) estimator for each candidate model, and then build robust weighting schemes by the asymptotic expansion of the final prediction error based on the GM-type loss function. So, we can still achieve a trustworthy result even if the dataset is contaminated by outliers in response and/or covariates. Asymptotic properties of the proposed robust model averaging estimators are established under some regularity conditions. The consistency of our weight estimators tending to the theoretically optimal weight vectors is also derived. We prove that our model averaging estimator is robust in terms of having bounded influence function. Further, we define the empirical prediction influence function to evaluate the quantitative robustness of the model averaging estimator. A simulation study and a real data analysis are conducted to demonstrate the finite sample performance of our estimators and compare them with other commonly used model selection and averaging methods.

A new robust approach for the polytomous logistic regression model based on Rényi's pseudodistances.

This paper presents a robust alternative to the maximum likelihood estimator (MLE) for the polytomous logistic regression model, known as the family of minimum Rènyi Pseudodistance (RP) estimators. The proposed minimum RP estimators are parametrized by a tuning parameter $\alpha \ge 0$, and include the MLE as a special case when $\alpha =0$. These estimators, along with a family of RP-based Wald-type tests, are shown to exhibit superior performance in the presence of misclassification errors. The paper includes an extensive simulation study and a real data example to illustrate the robustness of these proposed statistics.

Pengaruh Family Ownership, CEO duality, dan Political Connection terhadap Firm Performance

This study aims to examine the effect of family ownership and CEO duality moderated by political connection on firm performance. Using purposive sampling method, the research observation uses data from 34 companies in the mining industry in the mining sector listed on the Indonesia Stock Exchange (IDX) during 2018-2023 totaling 200 data samples. The data was tested using software Eviews 12 with moderation regression models with the most suitable model estimation is Common Effect Model (CEM). To measure the dependent variable from this study, which is firm performance, this study uses Return on Assets (ROA) as a parameter. As a result of the moderation regression, family ownership has a positive significant effect on firm performance. CEO Duality has no effect on firm performance. Family ownership moderated by political connection has no effect on firm performance. Lastly, CEO Duality moderated by political connection has no effect on firm performance.

The fly connectome reveals a path to the effectome

A goal of neuroscience is to obtain a causal model of the nervous system. The recently reported whole-brain fly connectome1–3 specifies the synaptic paths by which neurons can affect each other, but not how strongly they do affect each other in vivo. To overcome this limitation, we introduce a combined experimental and statistical strategy for efficiently learning a causal model of the fly brain, which we refer to as the ‘effectome’. Specifically, we propose an estimator for a linear dynamical model of the fly brain that uses stochastic optogenetic perturbation data to estimate causal effects and the connectome as a prior to greatly improve estimation efficiency. We validate our estimator in connectome-based linear simulations and show that it recovers a linear approximation to the nonlinear dynamics of more biophysically realistic simulations. We then analyse the connectome to propose circuits that dominate the dynamics of the fly nervous system. We discover that the dominant circuits involve only relatively small populations of neurons—thus, neuron-level imaging, stimulation and identification are feasible. This approach also re-discovers known circuits and generates testable hypotheses about their dynamics. Overall, we provide evidence that fly whole-brain dynamics are generated by a large collection of small circuits that operate largely independently of each other. This implies that a causal model of a brain can be feasibly obtained in the fly.

Canopy height uniformity: a new 3D phenotypic indicator linking individual plant to canopy

Canopy height uniformity (CHU) is a key indicator linking individual plants to populations. Determining CHU by the manual measurement of the height of individual plants is inefficient and subjective, making meeting the demand for a high-throughput assessment of CHU in high-yield crop management and variety selection challenging. Therefore, a high-throughput CHU estimation approach using unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) data is proposed. First, individual plant point clouds were segmented by incorporating planting density, and the CHU was estimated based on the extracted plant heights (PHs). The CHU was then applied to quantify the effects of different cropping practices on maize canopy structure. In addition, 18 canopy structural parameters (SPs) were extracted from the canopy point cloud, and aboveground biomass (AGB) was estimated by combining these SPs with Pelican Optimization Algorithm (POA) machine learning models (POA-MLs). Finally, as an indicator of individual variability within the canopy, CHU was integrated into the training process to evaluate the accuracy of AGB estimation for different models and datasets. The results showed that the plant height extracted by the canopy population-individual plant segmentation was accurate, with R2 ranging from 0.85 to 0.93. The CHU was able to accurately quantify the effects of different cropping practices on canopy structure. An increase in applied nitrogen fertilizer and irrigation could significantly contribute to an increase in CHU and the formation of a clean and homogeneous canopy structure. Meanwhile, marginal effects can be accurately quantified through PHs estimation to further quantify the intra-canopy differences. In addition, the accuracy of the AGB estimation can be effectively improved by merging SPs, PH, and CHU. In this study, we demonstrated the efficacy of CHU as a phenotypic indicator for representing differences in canopy structure, thus providing practical phenotypic identification information for breeders and field managers.

Improving global gross primary productivity estimation using two-leaf light use efficiency model by considering various environmental factors via machine learning

Distinguishing gross primary productivity (GPP) into sunlit (GPPsu) and shaded (GPPsh) components is critical for understanding the carbon exchange between the atmosphere and terrestrial ecosystems under climate change. Recently, the two-leaf light use efficiency (TL-LUE) model has proven effective for simulating global GPPsu and GPPsh. However, no known physical method has focused on integrating the overall constraint of intricate environmental factors on photosynthetic capability, and seasonal differences in the foliage clumping index (CI), which most likely influences GPP estimation in LUE models. Here, we propose the TL-CRF model, which uses the random forest technique to integrate various environmental variables, particularly for terrestrial water storage (TWS), into the TL-LUE model. Moreover, we consider seasonal differences in CI at a global scale. Based on 267 global eddy covariance flux sites, we explored the functional response of vegetation photosynthesis to key environmental factors, and trained and evaluated the TL-CRF model. The TL-CRF model was then used to simulate global eight-day GPP, GPPsu, and GPPsh from 2002 to 2020. The results show that the relative prediction error of environmental stress factors on the maximum LUE is reduced by approximately 52 % when these factors are integrated via the RF model. Thus the accuracy of global GPP estimation (R2 = 0.87, RMSE = 0.94 g C m−2 d−1, MAE = 0.61 g C m−2 d−1) in the TL-CRF model is greater than that (R2 = 0.76, RMSE = 2.18 g C m−2 d−1, MAE = 1.50 g C m−2 d−1) in the TL-LUE model, although this accuracy awaits further investigation among the released GPP products. TWS exerts the greatest control over ecosystem photosynthesis intensity, making it a suitable water indicator. Furthermore, the results confirm an optimal minimum air temperature for photosynthesis. Overall, these findings indicate a promising method for producing a new global GPP dataset, advancing our understanding of the dynamics and interactions between photosynthesis and environmental factors.

Performance and stability analysis of interacting multiple model estimator under unobservable packet loss

For a system with packet loss, if the estimator cannot observe the packet-loss status (PLS), the system is called an unobservable-packet-loss (UPL) system. Otherwise, it is called an observable-packet-loss (OPL) system. This paper studies the interacting multiple model (IMM) estimator for UPL systems, and the main contributions are twofold. (i) Estimation accuracy of the unobservable PLS. For an unstable UPL system, we prove that the UPL system will become an OPL one with time, since the PLS can be exactly estimated with time. For a stable UPL system, there exists an accuracy threshold such that the estimation accuracy of the PLS cannot be better than this threshold. (ii) Stability of the IMM estimator. For an unstable UPL system, we establish a necessary and sufficient condition: there exists a threshold such that the IMM estimator is stable almost everywhere if and only if the packet-arrival rate is greater than this threshold. For a stable UPL system, we show that the IMM estimator is stable, no matter what value the packet-arrival rate is.

The Moderating Effect of Competition on The Leverage-Performance Relation: Evidence from Vietnam

This paper examines the moderating effect of competition on the relationship between financial leverage and firm performance in Vietnam. Using a dataset created out of 352 firms listed on Vietnam’s stock exchanges in 2015–2019, this paper estimates both the leverage-performance relation and the dependence of this nexus on market competition. The two-step system generalised method of moments is used to tackle the endogeneity, unobserved heterogeneity, and autocorrelation problems in our model estimation. The findings reveal a negative leverage-performance nexus, and increased competition hurts this relationship. In highly competitive markets, debts become more expensive, thus leading to increased financial pressures on leveraged firms. The fiercer competition also creates a risky business environment, so leveraged firms may fail when operating inefficiently and failing to satisfy their debt obligations. Given the results, this paper proposes recommendations on using financial leverage to improve firm performance.

An Efficient Filtering Approach for Model Estimation in Sparse Regression

ABSTRACTAs technology advances, the scale of data generated is growing exponentially, bringing huge challenges to data storage and computation. To facilitate the computational cost while maintaining model estimation accuracy, subdata selection become important. Conventional methods, such as LASSO and ridge regression, often focus on feature selection. In contrast, methods of subsampling aim at specifying data points to be extracted. However, these subsampling methods often overlook the full consideration of the role of the response variable and its relationship with predictor variables. In this work, we propose a so‐called filtering approach for model estimation (FAME) method to perform subsampling in combination with feature screening. Compared with existing methods, the proposed method can result in the subdata being smaller in size both in terms of the number of features and observations, and also the computational complexity does not increase. The proposed method can be extended to situations when the predictor is binary, the response is binary, or both are binary. The performance of FAME is evaluated in both numerical studies and real data examples.

Factors affecting farmers' choice to adopt risk management strategies: The application of multivariate and multinomial probit models

This study investigates the factors that impact farmers' adoption of risk management strategies (RMS) in Pakistan during times of uncertainty. The study examines farmers' RMS adoption choices using both multinomial probit (MNP) and multivariate probit (MVP). Data were collected from 382 sampled farmers from four districts in Khyber-Pakhtunkhwa (KP) province of Pakistan via a multistage sampling technique. This study utilizes the MNP model, considering the assumption of Independence of Irrelevant Alternatives (IIA) and incorporating correlated error terms. The objective is to understand farmers' behavior in risky situations and determine if there is heterogeneity. Results are compared with the MVP model to assess robustness and gain insights into farmers' decision-making processes.The research findings reveal that our results are robust, and farmers behave homogeneously in different settings of the provision of risk management strategies. Farmers adopt RMS individually or in combination to mitigate the adverse effects of natural calamities on their livelihood. The risk-averse farmers, who perceive weather-related risks as a threat, access credits and information, and have farms close to a river are more likely to adopt RMS, irrespective of the format of the strategies available. Moreover, the predicted probabilities and correlation of the RMS and RM categories further strengthened our model estimation. These findings provide insights into the behavior of farmers in adopting RMS which are helpful for policymakers and stakeholders in developing strategies to mitigate the impacts of natural calamities on farmers.

Estimation and research of the temperature field models of large space spherical mesh shell structures under localized fire

This paper firstly investigated fire dynamics characteristics and the temperature field distributions of large space spherical mesh shell (SMS) structures under localized fire in FDS, and divided the temperature fields into plume region, smoke accumulation region and ceiling jet region. Based on the classical axisymmetric plume model, the predictive models for the steady-state and transient temperature fields in different fire regions for large space SMS structures were proposed in this paper. Thereafter, the proposed temperature models were compared with the simulated results and experimental test results by other scholars, and they were in good agreement. In order to further discover the realistic fire development progress and temperature distribution of large space fire, and validate the proposed temperature field models, this paper designed and conducted three full-scale large space fire tests (Test-1 ∼ Test-3) with different fire source powers in a large space building. Results showed that temperature fields of large space fires could be consisted of near-fire region (Flame region) and far-fire region (Plume region and Ceiling jet region). The steady-state fire source powers were tested for 443 kW, 886 kW, 1090 kW for Test-1, Test-2 and Test-3, respectively, while their maximum temperatures of the roof reached 75 °C, 105 °C and 120 °C. Based on McCaffrey plume model, we further proposed the transient temperature model in flame region for large space structures, and its accuracy was verified by the test results. Moreover, the proposed steady-state and transient temperature fields for large space SMS structures were compared with the tested temperature field data, and the tested and predicted temperature curves were generally in good agreement in plume and ceiling jet regions. Finally, the test results aimed to provide a scientific basis and reference for the fire safety and structural fire-resistant design of large space buildings.

Quasi Maximum Likelihood Estimation and Inference of Large Approximate Dynamic Factor Models via the EM algorithm

We study estimation of large Dynamic Factor models implemented through the Expectation Maximization (EM) algorithm, jointly with the Kalman smoother. We prove that as both n and T diverge to infinity: (i) the estimated loadings are \sqrt{T}-consistent and asymptotically normal and equivalent to their Quasi Maximum Likelihood estimates; (ii) the estimated factors are \sqrt{n}-consistent and asymptotically normal and equivalent to their Weighted Least Squares estimates. Moreover, the estimated loadings are asymptotically as efficient as those obtained by Principal Components analysis, while the estimated factors are more efficient if the idiosyncratic covariance is sparse enough.

A multiscale hybrid modelling methodology for cell cultures enabled by enzyme-constrained dynamic metabolic flux analysis under uncertainty

Mammalian cell cultures make a significant contribution to the pharmaceutical industry. They produce many of the biopharmaceuticals obtaining FDA-approval each year. Motivated by quality-by-design principles, various modelling methodologies are frequently trialled to gain insight into these bioprocesses. However, these systems are highly complex and uncertain, involving dynamics at different scales, both in time and space, making them challenging to model in a comprehensive and fully mechanistic manner. This study develops a machine-learning-supported multiscale modelling framework of cell cultures, linking the macroscale bioprocess dynamics to the microscale metabolic flux distribution. As a relevant biopharmaceutical case study, we consider the production of Trastuzumab by Chinese Hamster Ovary (CHO) cells in batch. A macroscale hybrid model is constructed by integrating macro-kinetic and machine-learning approaches. Enzyme-constrained Dynamic Metabolic Flux Analysis (ecDMFA) is adopted to calculate flux distributions based on the dynamic predictions of the hybrid model. Uncertainty estimation of the multiscale model is conducted through bootstrapping. Judging from experimental data, our hybrid model can reduce the modelling error of the macroscale dynamics to 8.0%; a 70% reduction from the purely mechanistic model. In addition, the predicted dynamic flux distribution aligns with observations seen in literature, highlighting important metabolic changes throughout the process. Model uncertainty is maintained at a low level, demonstrating the trustworthiness of the predictions. Overall, our comprehensive modelling framework has the potential to facilitate the development of digital twins in the biopharmaceutical industry.