Model Estimation Research Articles

Markerless tracking of tumor and tissues: A motion model approach.

Respiratory motion management is essential in order to achieve high-precision radiotherapy. Markerless motion tracking of tumor can provide a non-invasive way to manage respiratory motion, thereby enhancing treatment accuracy. However, the low contrast in real-time x-ray images for image guidance limits the application of markerless tracking. We present a novel approach based on a motion model to perform markerless tracking of tumor and surrounding tissues even when they have low contrast in real-time x-ray images. A proof-of-concept validation of the method has been performed using digital and physical phantoms at breathing conditions that are significantly different than the planning stage. A motion model is first constructed by performing principal component analysis (PCA) on the planning 4DCT. During treatment, the motion of a surrogate is tracked and used as the input of the motion model, which generates a 3D real-time volume estimation. Such 3D estimation is then projected to 2D to create digitally reconstructed radiographs (DRRs). The relationships between the real-time DRRs, reference DRRs, and reference x-ray images are first established to simulate 2D real-time images from the real-time volume. The registration between the simulated 2D real-time images and real-time x-ray images corrects the initial motion model estimation to ensure the estimated volume matches the real-time condition. In digital phantom, the Dice index of pancreas was improved from 0.74 to 0.78 after correction using real-time DRRs in fully inhaled phase. Validation on lung and pancreas is performed in physical phantom with two motion traces. The surrogate-tumor relationships were intentionally altered to generate large target localization errors due to the differences in body condition between treatment planning stage and during treatment. The real-time correction for the estimated 3D real-time volume was performed using a pair of 2D x-ray images. For the deep breathing motion trace, the tumor localization mean absolute error (MAE) throughout the tracking decreases from around 3mm to less than 1mm after correction. For the shallow breathing motion trace with a 1.7mm baseline shift, the tumor localization MAE throughout the tracking decreases from around 1.5mm to less than 1mm after correction. The method combines the detailed structural information from planning 4DCT and real-time information from real-time x-ray images through a motion model. The matching between the real-time model estimation and 2D real-time images is performed in the same modality so that it can be applied to regions with low contrast in the images. The real-time images successfully corrected the initial motion model estimations in our proof-of-concept validation. This suggests the potential to perform markerless tracking in low-contrast region using a motion model.

Investors’ reaction to banning IFRS use by domestic firms in alternative market

AbstractThis paper analyses the regulatory context, i.e., how IFRS preparers investing in Poland react to banning IFRS use by domestic firms in the alternative trading market (NewConnect) after six years of regulatory arbitrage. In other words, it studies their portfolio investment and foreign direct investment (FDI) outward sensitivity to the end of regulatory arbitrage opportunity given by the alternative market’s regulation in the country where accounting law limits IFRS use to regulated markets and business groups where the parent company uses IFRS. The contribution is built on the natural experiment of prohibition of domestic IFRS preparers issuing shares on the alternative (not-regulated and cheaper) trading market after 2012. The originality is delivered by implementing a de facto measure of IFRS adoption by foreign investors to identify a 69% growth rate of IFRS preparers investing in Polish firms in 2012 (317% growth of Swedish investors and 219% of Portuguese) that dropped to 8% in 2013 and 3% in 2018. Thus, we check whether investing decisions consider opportunities to gain funds or attract other investors and build trust through IPOs on the unregulated market dedicated to start-ups and engaged in R&D activity. We use difference-in-differences, GMM dynamic panel-data analysis with Arellano-Bond and Arellano-Bover/Blundel-Bond estimators for the Knowledge-Capital model on portfolio investment, outward FDI overall, and three FDI components based on debt, equity, and earning reinvestment in 2003–2019. We found that banning IFRS use by domestic firms in the alternative market discourages portfolio investments, debt-based FDI flows and FDI earnings reinvestment. Also, total FDI and equity-based FDI from countries with more IFRS preparers have been reduced since 2013 due to the IFRS ban for domestic firms.

Characterisation and calibration of multiversal methods

AbstractMultiverse Analysis is a heuristic for robust multiple models estimation where data fit many connected specifications of the same abstract model, instead of a singular or a small selection of specifications. Differently from the canonical application of multimodels, in Multiverse Analysis the probabilities of the specifications to be included in the analysis are never assumed independent of each other. Grounded in this consideration, this study provides a compact statistical characterisation of the process of elicitation of the specifications in Multiverse Analysis and conceptually adjacent methods, connecting previous insights from meta-analytical Statistics, model averaging, Network Theory, Information Theory, and Causal Inference. The calibration of the multiversal estimates is treated with references to the adoption of Bayesian Model Averaging vs. alternatives. In the applications, it is checked the theory that Bayesian Model Averaging reduces both error and uncertainty for well-specified multiversal models but amplifies errors when a collider variable is included in the multiversal model. In well-specified models, alternatives do not perform better than Uniform weighting of the estimates, so the adoption of a gold standard remains ambiguous. Normative implications for misinterpretation of Multiverse Analysis and future directions of research are discussed.

Fast standard error estimation for joint models of longitudinal and time-to-event data based on stochastic EM algorithms.

Maximum likelihood inference can often become computationally intensive when performing joint modeling of longitudinal and time-to-event data, due to the intractable integrals in the joint likelihood function. The computational challenges escalate further when modeling HIV-1 viral load data, owing to the nonlinear trajectories and the presence of left-censored data resulting from the assay's lower limit of quantification. In this paper, for a joint model comprising a nonlinear mixed-effect model and a Cox Proportional Hazards model, we develop a computationally efficient Stochastic EM (StEM) algorithm for parameter estimation. Furthermore, we propose a novel technique for fast standard error estimation, which directly estimates standard errors from the results of StEM iterations and is broadly applicable to various joint modeling settings, such as those containing generalized linear mixed-effect models, parametric survival models, or joint models with more than two submodels. We evaluate the performance of the proposed methods through simulation studies and apply them to HIV-1 viral load data from six AIDS Clinical Trials Group studies to characterize viral rebound trajectories following the interruption of antiretroviral therapy (ART), accounting for the informative duration of off-ART periods.

Excess Mortality and its Determinants During the COVID-19 Pandemic in 21 Countries: An Ecological Study from the C-MOR Project, 2020 and 2021.

The COVID-19 pandemic overwhelmed health systems, resulting in a surge in excess deaths. This study clustered countries based on excess mortality to understand their response to the pandemic and the influence of various factors on excess mortality within each cluster. This ecological study is part of the COVID-19 MORtality (C-MOR) Consortium. Mortality data were gathered from 21 countries and were previously used to calculate weekly all-cause excess mortality. Thirty exposure variables were considered in five categories as factors potentially associated with excess mortality: population factors, health care resources, socioeconomic factors, air pollution, and COVID-19 policy. Estimation of Latent Class Linear Mixed Model (LCMM) was used to cluster countries based on response trajectory and Generalized Linear Mixture Model (GLMM) for each cluster was run separately. Using LCMM, two clusters were reached. Among 21 countries, Brazil, the USA, Georgia, and Poland were assigned to a separate cluster, with the mean of excess mortality z-score in 2020 and 2021 around 4.4, compared to 1.5 for all other countries assigned to the second cluster. In both clusters the population incidence of COVID-19 had the greatest positive relationship with excess mortality while interactions between the incidence of COVID-19, fully vaccinated people, and stringency index were negatively associated with excess mortality. Moreover, governmental variables (government revenue and government effectiveness) were the most protective against excess mortality. This study highlighted that clustering countries based on excess mortality can provide insights to gain a broader understanding of countries' responses to the pandemic and their effectiveness.

An Integrated Fuzzy Delphi and Fuzzy AHP Model for Evaluating Factors Affecting Human Errors in Manual Assembly Processes

Human errors (HEs) are prevalent issues in manual assembly, leading to product defects and increased costs. Understanding and knowing the factors influencing human errors in manual assembly processes is essential for improving product quality and efficiency. This study aims to determine and rank factors influencing HEs in manual assembly processes based on expert judgments. To achieve this objective, an integrated model was developed using two multi-criteria decision-making (MCDM) techniques—specifically, the fuzzy Delphi Method (FDM) and the fuzzy Analytic Hierarchy Process (FAHP). Firstly, two rounds of the FDM were conducted to identify and categorize the primary factors contributing to HEs in manual assembly. Expert consensus with at least 75% agreement determined that 27 factors with influence scores of 0.7 or higher significantly impact HEs in these processes. After that, the priorities of the 27 influencing factors in assembly HEs were determined using a third round of the FAHP method. Data analysis was performed using SPSS 22.0 to evaluate the reliability and normality of the survey responses. This study has divided the affecting factors on assembly HEs into two levels: level 1, called main factors, and level 2, called sub-factors. Based on the final measured weights for level 1, the proposed model estimation results revealed that the most influential factors on HEs in a manual assembly are the individual factor, followed by the tool factor and the task factor. For level 2, the model results showed a lack of experience, poor instructions and procedures, and misunderstanding as the most critical factors influencing manual assembly errors. Sensitivity analysis was performed to determine how changes in model inputs or parameters affect final decisions to ensure reliable and practical results. The findings of this study provide valuable insights to help organizations develop effective strategies for reducing worker errors in manual assembly. Identifying the key and root factors contributing to assembly errors, this research offers a solid foundation for enhancing the overall quality of final products.

An Intertemporal Risk Factor Model

Prominent factor models are based on tradable factors that do not represent theoretically relevant risks. To address this issue, we develop a factor model that captures the risks to long-term investors present in the intertemporal capital asset pricing model (ICAPM). Empirically, we construct intertemporal risk factors as long-short portfolios based on stock exposures to dividend yield and realized variance. These tradable factors mimic news to long-term expected returns and volatility, and they offset part of the marginal utility increase in recessions induced by wealth declines. Our intertemporal factor model estimation implies significant risk prices that are consistent with the ICAPM restrictions under moderate risk aversion. Moreover, our model performs well relative to previous factor models in terms of its tangency Sharpe ratio and its pricing of key test assets, including single stocks, industry portfolios, and portfolios sorted on risk exposures and lagged anomalies. This paper was accepted by Will Cong, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00261 .

Wavelet estimation of a regression model with mixed noises

Wavelet estimation of a regression model with mixed noises

On the Estimation of Logistic Models with Banking Data Using Particle Swarm Optimization

This paper presents numerical works on estimating some logistic models using particle swarm optimization (PSO). The considered models are the Verhulst model, Pearl and Reed generalization model, von Bertalanffy model, Richards model, Gompertz model, hyper-Gompertz model, Blumberg model, Turner et al. model, and Tsoularis model. We employ data on commercial and rural banking assets in Indonesia due to their tendency to correspond with logistic growth. Most banking asset forecasting uses statistical methods concentrating solely on short-term data forecasting. In banking asset forecasting, deterministic models are seldom employed, despite their capacity to predict data behavior for an extended time. Consequently, this paper employs logistic model forecasting. To improve the speed of the algorithm execution, we use the Cauchy criterion as one of the stopping criteria. For choosing the best model out of the nine models, we analyze several considerations such as the mean absolute percentage error, the root mean squared error, and the value of the carrying capacity in determining which models can be unselected. Consequently, we obtain the best-fitted model for each commercial and rural bank. We evaluate the performance of PSO against another metaheuristic algorithm known as spiral optimization for benchmarking purposes. We assess the robustness of the algorithm employing the Taguchi method. Ultimately, we present a novel logistic model which is a generalization of the existence model. We evaluate its parameters and compare the result with the best-obtained model.

Estimating Asset Pricing Models in the Presence of Cross-Sectionally Correlated Pricing Errors

In this study, we propose an adversarial learning approach to the asset pricing model estimation problem which aims to find estimates of factors and loadings that capture time-series covariations while minimizing the worst-case cross-sectional pricing errors. The proposed estimator is defined by a novel min-max optimization problem in which finding a solution is known to be difficult. This contrasts with other related estimators that admit a well-defined analytic solution but do not effectively account for correlations among the pricing errors. To this end, we propose an approximate algorithm based on the alternating optimization procedure and empirically demonstrate that our proposed adversarial estimation framework outperforms other existing factor models, especially when the explanatory power of the pricing model is limited.

An innovative fractional grey system model and its application

In order to expand the applicability of the conventional fractional grey model, an innovative fractional grey model is proposed by the introduction of the innovative fractional accumulation. Three efforts are made in this study. First, the innovative fractional accumulation and its inverse operation are designed. Based on the innovative form, the parameter estimation and discrete time response of the novel model are given. In addition, the moth flame optimization algorithm is used to determine optimal hyper parameters for the novel model, and the rolling mechanism is used to enhance the prediction performance. To comprehensively confirm the forecasting ability of the proposed model, it is applied in three kinds of data with inverted U-shaped, W-shaped and oscillating features. The experimental results show the novel model is superior to all competitors in terms of level accuracy. Therefore, the novel model is considered a promising method for enhancing the existing fractional grey model.

Accurate Modeling and Parameters Estimation of Photovoltaic Models: Analytical and Artificial Intelligence Solutions

Accurate Modeling and Parameters Estimation of Photovoltaic Models: Analytical and Artificial Intelligence Solutions

Global Rate Optimality of Integral Curve Estimators in High Order Tensor Models: Supplemental Material

Global Rate Optimality of Integral Curve Estimators in High Order Tensor Models: Supplemental Material

Effects of model-overfit on model-assisted forest inventory in boreal forests with remote sensing data

Abstract While remote sensing can be an effective tool in building a forest inventory, field measurements and model fitting can be both expensive and challenging. One strategy to reduce forest inventory costs is to leverage forest inventory models fitted to a different population (external models), although the effectiveness of external models is poorly understood. One concern is that models may predict well to the sample data, but poorly to the population—which is termed ‘overfitting’. The effect of overfit may be especially problematic in attempts to predict for a different population (a forest area not covered by any sample plots). Assessing overfit is difficult and its consequence for estimation are not well understood, especially in the context of prediction using external models. This study assesses how overfitting affects model-assisted forest inventory estimation when using internal and external models. We used field and remotely sensed data (Sentinel-2 images and airborne laser scanning data) from two forest areas in Finland. We evaluated four modeling approaches: ordinary least square regression (OLS), random forest, k-nearest neighbors, and gaussian process regression. Both analytical and bootstrap variance estimators were used to evaluate model-assisted estimation performance. Internal models, especially OLS, were the most affected by model overfitting, leading to bias in the population means and underestimation of variance. Estimates using external models provided unbiased means and realistic intervals except in the case of deliberate excessive overfitting. The bootstrap variance estimator was found to be more robust to overfit than the analytical variance estimator for the internal model, but was not helpful for the external model. Internal models should be parsimonious to generalize well to the population and avoid bias. The bootstrap estimator of variance is recommended for internal models, especially if there is concern about model overfitting.

Real-time modeling of transient crustal deformation through the quantification of uncertainty deduced from GNSS data

We propose a new method for real-time uncertainty monitoring of earthquake and volcano source models using data from the global navigation satellite system (GNSS) and explore its application concerning observation station placement. The Geospatial Information Authority of Japan operates two main types of GNSS earth observation network system (GEONET) coordinates for crustal deformation monitoring on different time scales: post-processing analysis values and real-time GEONET analysis system for rapid deformation monitoring (REGARD). REGARD uses the Markov Chain Monte Carlo (MCMC) method termed real-time automatic uncertainty estimation of a coseismic single rectangular model using GNSS data (RUNE) for single rectangular fault model estimation to handle uncertainty. Thus far, no GNSS monitoring system can automatically detect transient crustal deformation events, such as volcanic activity and earthquake swarms, on timescales of a day or less. We extended RUNE and developed a core program for a new monitoring system for earthquake and volcanic source models and their uncertainties. Our program achieved automatic and stable MCMC utilization for rectangular fault, dike, Mogi, and spheroid models by increasing the computational speed, improving search efficiency, and adjusting hyperparameters. The program automatically determines the standard deviation of the likelihood function assuming a normal distribution with weights for each observation station. The calculation time was within 15 s for 1 × 106 samples on a standard 1U server. We assessed the reliability of the developed method using synthetic and observed GNSS data from the 2015 Sakurajima volcanic event. The results were consistent with the assumed model and previous studies and indicated an advantage in automatically quantifying uncertainty in a short computation time. Based on MCMC samples, we developed a new visualization algorithm to indicate areas on a map in which the number of observation stations should be expanded. We assessed the reliability using data from the 2023 Noto Peninsula earthquake [Mj 6.5]. The results indicate that the algorithm is helpful in studying the placement of stations. The above model extensions and their application are essential to achieve a rapid quantitative understanding of disaster events near urban areas and for utilizing this information in emergency response activities.Graphical

Moving Target Detection in Clutter Environment Based on Track Posture Hypothesis Testing

ABSTRACTMoving target detection in a heavy clutter area is a challenging problem in the field of radar automatic target detection. Within the framework of track posture hypothesis testing, the radar‐blips‐oriented target detection algorithms named ErgSad and RanSaD are proposed for single target and formation targets detection, respectively in this paper. With the assumption that target motion is a linear Gaussian process in a short time, the algorithms use dual blips in different scans to generate the track hypothesis and test the hypothesis with residual blips in other scans to determine the existence of moving targets. To reduce time consumption and minimize the error of model estimation, the sampling rules and the supporting domain related to timestamps of track hypothesis models were designed. Simulation data experiments show that the proposed algorithms have more superior performance to detect targets than the state‐of‐the‐art algorithms in the serious clutter environment.

Coherent Set Identification Via Direct Low Rank Maximum Likelihood Estimation

We analyze connections between two low rank modeling approaches from the last decade for treating dynamical data. The first one is the coherence problem (or coherent set approach), where groups of states are sought that evolve under the action of a stochastic transition matrix in a way maximally distinguishable from other groups. The second one is a low rank factorization approach for stochastic matrices, called direct Bayesian model reduction (DBMR), which estimates the low rank factors directly from observed data. We show that DBMR results in a low rank model that is a projection of the full model, and exploit this insight to infer bounds on a quantitative measure of coherence within the reduced model. Both approaches can be formulated as optimization problems, and we also prove a bound between their respective objectives. On a broader scope, this work relates the two classical loss functions of nonnegative matrix factorization, namely the Frobenius norm and the generalized Kullback–Leibler divergence, and suggests new links between likelihood-based and projection-based estimation of probabilistic models.

Combining Instrumental Variable Estimators for a Panel Data Model with Factors

We address the estimation of factor-augmented panel data models using observed measurements to proxy for unobserved factors or loadings and explore the use of internal instruments to address the resulting endogeneity. The main challenge consists in that economic theory rarely provides insights into which measurements to choose as proxies when several are available. To overcome this problem, we propose a new class of estimators that are linear combinations of instrumental variable estimators and establish large sample results. We also show that an optimal weighting scheme exists, leading to efficiency gains relative to an instrumental variable estimator. Simulations show that the proposed approach performs better than existing methods. We illustrate the new method using data on test scores across US school districts.

Estimation for conditional moment models based on martingale difference divergence

We provide a new estimation method for conditional moment models via the martingale difference divergence (MDD). Our MDD‐based estimation method is formed in the framework of a continuum of unconditional moment restrictions. Unlike the existing estimation methods in this framework, the MDD‐based estimation method adopts a non‐integrable weighting function, which could capture more information from unconditional moment restrictions than the integrable weighting function to enhance the estimation efficiency. Due to the nature of shift‐invariance in MDD, our MDD‐based estimation method can not identify the intercept parameters. To overcome this identification issue, we further provide a two‐step estimation procedure for the model with intercept parameters. Under regularity conditions, we establish the asymptotics of the proposed estimators, which are not only easy‐to‐implement with expectation‐based asymptotic variances, but also applicable to time series data with an unspecified form of conditional heteroskedasticity. Finally, we illustrate the usefulness of the proposed estimators by simulations and two real examples.

Estimation of Contagion: Bayesian Model Averaging on Tail Dependence of Mixture Copula

This study introduces a novel approach to estimate tail dependence in financial contagion using mixture copulas. Addressing the challenges of weight parameter estimation in conventional models, we propose a Bayesian model averaging method to determine optimal copula weights. Through both simulations and empirical studies, the proposed method demonstrates improved robustness and accuracy, particularly when handling extreme weight scenarios. These advancements offer more reliable measurements of financial contagion, contributing to enhanced risk management and policy-making in interconnected financial markets.