Estimation Method Research Articles

Board attributes and tax avoidance: The moderating role of institutional ownership

AbstractThis study investigates the influence of corporate board attributes, such as board size, board independence, board meeting frequency, female representation on board, and audit committee size, on tax avoidance. Moreover, the study also explores the moderating role of institutional ownership on these relationships. We use sample of non‐financial firms listed on Pakistan Stock Exchange over the period 2013–2020. Using the framework of agency theory, we report that board size, board independence, board meetings, gender diversity, and audit committees are associated with lower tax avoidance, and the presence of institutional ownership further strengthens these relationship. To test the hypotheses, ordinary least squares regression analysis is applied and robustness is ensured through by employing Generalized method of moments estimation. Overall, our study offers novel insights into the positive implication of board attributes on tax avoidance, particularly within the framework of institutional ownership settings.

A hierarchical estimation of road grade based on tire force observation

Road grade is important for autonomous vehicles, but it is difficult to measure directly. To address this issue, a hierarchical estimation of road grade is suggested based on the observation of tire forces. First, a 7-degree-of-freedom (DOF) dynamics model, including vehicle longitudinal, lateral, and yaw motions together with wheel rotations, is developed while considering the road grade. Subsequently, a dual-layer road grade estimation strategy is proposed based on an unscented Kalman filter (UKF). The lower-layer UKF estimates the longitudinal and lateral tire forces for road grade observation, and the upper-layer UKF is employed to estimate the road grade by considering the vehicle’s lateral acceleration and yaw rate. Finally, CarSim and MATLAB joint simulations and road tests are performed under different conditions to validate the correctness and effectiveness of the proposed estimation method. The results show that the proposed tire force observation-based estimator exhibits a lower mean absolute error and root mean square error on sloping roads and combined curved and sloping roads, and presents a better overall estimation performance on road grade compared with the widely used kinematics and dynamics model-based estimators.

Enhancing the reliability of deep learning-based head and neck tumour segmentation using uncertainty estimation with multi-modal images

Objective. Deep learning shows promise in autosegmentation of head and neck cancer (HNC) primary tumours (GTV-T) and nodal metastases (GTV-N). However, errors such as including non-tumour regions or missing nodal metastases still occur. Conventional methods often make overconfident predictions, compromising reliability. Incorporating uncertainty estimation, which provides calibrated confidence intervals can address this issue. Our aim was to investigate the efficacy of various uncertainty estimation methods in improving segmentation reliability. We evaluated their confidence levels in voxel predictions and ability to reveal potential segmentation errors. Approach. We retrospectively collected data from 567 HNC patients with diverse cancer sites and multi-modality images (CT, PET, T1-, and T2-weighted MRI) along with their clinical GTV-T/N delineations. Using the nnUNet 3D segmentation pipeline, we compared seven uncertainty estimation methods, evaluating them based on segmentation accuracy (Dice similarity coefficient, DSC), confidence calibration (Expected Calibration Error, ECE), and their ability to reveal segmentation errors (Uncertainty-Error overlap using DSC, UE-DSC). Main results. Evaluated on the hold-out test dataset (n = 97), the median DSC scores for GTV-T and GTV-N segmentation across all uncertainty estimation methods had a narrow range, from 0.73 to 0.76 and 0.78 to 0.80, respectively. In contrast, the median ECE exhibited a wider range, from 0.30 to 0.12 for GTV-T and 0.25 to 0.09 for GTV-N. Similarly, the median UE-DSC also ranged broadly, from 0.21 to 0.38 for GTV-T and 0.22 to 0.36 for GTV-N. A probabilistic network—PhiSeg method consistently demonstrated the best performance in terms of ECE and UE-DSC. Significance. Our study highlights the importance of uncertainty estimation in enhancing the reliability of deep learning for autosegmentation of HNC GTV. The results show that while segmentation accuracy can be similar across methods, their reliability, measured by calibration error and uncertainty-error overlap, varies significantly. Used with visualisation maps, these methods may effectively pinpoint uncertainties and potential errors at the voxel level.

On the Estimation of Fit Indices for the Structural Part of a Model

When a researcher proposes an SEM model to explain the dynamics among some latent variables, the real question in model evaluation is the fit of the model’s structural part. A composite index that lumps the fit of the structural part and measurement part does not directly address that question. The need for more attention to structural-level fit has been noted for a long time, but the literature has been limited to conceptual issues such as how to define the structural-level RMSEA or CFI. How to estimate those structural-level fit indices, however, have been overlooked. Traditional estimation methods for composite-fit RMSEA and CFI are not applicable to structural-level fit indices, even if data are complete and normally distributed. In this article, we give correct point estimators and confidence intervals for structural-level RMSEA and CFI. Simulation results suggest that the new methods have satisfactory performance in realistic situations.

Effective machine lifespan management using determined state–action cost estimation for multi-dimensional cost function optimization

ABSTRACT This study introduces a comprehensive framework designed to enhance production efficiency by integrating maintenance strategies, energy costs, and production specifications. This integration is achieved through a novel empirical method for estimating state–action costs, suitable for both machines with measurable and non-measurable states-of-health. We address the challenge of under-determination in state–action cost optimization by employing a k-means clustering approach, ensuring robustness and applicability. Utilizing an adapted SARSA algorithm, our framework optimally controls shop-floor machinery to minimize the global cost function. The efficacy of the state–action cost estimation method is validated using NASA’s C-MAPSS dataset. Additionally, the optimization strategy is further corroborated through its successful implementation in an autonomous mining cart model on the shop floor. Our results highlight the framework’s ability to optimize machine lifetime and production processes effectively, providing tailored solutions that adapt to varying operational conditions without depending on predefined machine degradation models and costs.

Automatic variable extraction from 3D coxal bone models for sex estimation using the DSP2 method.

Thanks to technical progress and the availability of virtual data, sex estimation methods as part of a biological profile are undergoing an inevitable evolution. Further reductions in subjectivity, but potentially also in measurement errors, can be brought by approaches that automate the extraction of variables. Such automatization also significantly accelerates and facilitates the specialist's work. The aim of this study is (1) to apply a previously proposed algorithm (Kuchař et al. 2021) to automatically extract 10 variables used for the DSP2 sex estimation method, and (2) to test the robustness of the new automatic approach in a current heterogeneous population. For the first aim, we used a sample of 240 3D scans of pelvic bones from the same individuals, which were measured manually for the DSP database. For the second aim a sample of 108 pelvic bones from the New Mexico Decedent Image Database was used. The results showed high agreement between automatic and manual measurements with rTEM below 5% for all dimensions except two. The accuracy of final sex estimates based on all 10 variables was excellent (error rate 0.3%). However, we observed a higher number of undetermined individuals in the Portuguese sample (25% of males) and the New Mexican sample (36.5% of females). In conclusion, the procedure for automatic dimension extraction was successfully applied both to a different type of data and to a heterogeneous population.

An Effective Tag Estimation Method Based upon Artificial Neural Networks and Signal Strength for Anticollision in Radio Frequency Identification Systems

Radio frequency identification (RFID) technology has been widely used in applications such as access control, inventory management, spatial positioning, and object identification. Accurate tag estimation is one of the major challenges in RFID reader systems particularly in areas where large tag populations are to be identified such as shopping carts, warehouse inventory monitoring, and small ruminant farms. This paper proposes a new tag estimation technique employing artificial neural networks (ANNs) and signal strength to read large tag populations. The technique estimates the number of tags through the signal strength of the backscatter channel for efficient implementation of dynamic framed slotted Aloha (DFSA) protocol by analyzing the RN16 and the received signal strength indicator (RSSI). The ANN model is trained using the signal strength of various tag populations and can identify the number of tags with minimal errors. The proposed technique does not require any modification in the tags and is implemented as a minimal software script to be added to the tag estimation module of the reader. The proposed signal strength-ANN model is able to estimate the accurate number of tags thereby improving the performance of the employed DFSA model.

Is CEO power linked to corporate social responsibility disclosure? Evidence from an emerging economy

AbstractThe research aims to analyze the influence of Chief Executive Officer (CEO) power factors on corporate social responsibility (CSR) disclosure by listed companies on the Vietnamese stock market. To examine the relationship between CEO power and CSR disclosure (CSRD), our study used the generalized method of moments (GMM) estimation method with panel data from 210 listed companies on the Vietnamese stock market during the period 2015–2022. The research results indicate that factors such as duality, tenure, share ownership, and CEO compensation reduce the level of CSR disclosure by companies. On the other hand, CEO education and age show a positive relationship with disclosure level. The empirical results of this study provide a solid foundation for managers to establish effective corporate governance mechanisms and enhance the quality of sustainable reporting by companies.

Propensity Score Matching Pada Pemanfaatan Data Hasil Web Scraping Untuk Perbaikan Statistik Resmi

The Central Statistics Agency (BPS) welcomes the challenge of utilizing big data. One of the BPS publications that can be supported using big data is the inflation figure collected from the consumer price survey. One part of the consumer price survey is the HK-4 Survey, which contains house contract rates. So far, the house contract rates produced by BPS have been underestimated or lower than the actual situation. Improvements to house contract rates are carried out by matching BPS data and web scraping of house rental sites using Propensity Score Matching (PSM). The data used in this study includes DKI Jakarta, Bandung, and Semarang from September to October 2023. This study aims to find the best matching model using PSM to improve official statistics (house contract rates) by combining several propensity score value estimation methods and matching algorithms. Furthermore, the results matching the best model will be used to calculate the corrected house contract rates. The study results show that the best matching model generally uses logistic regression propensity score value estimation, the nearest neighbor matching algorithm with returns and uses a 1:1 ratio. The corrected contract rates are far above the official ones (DKI Jakarta corrected 87.27%, Bandung 316.15%, and Semarang 60.04%). Web Scraping allows it to improve official statistics because it is cost and time-saving, enhances the quality of official statistical data, and supports better decision-making in various sectors.

Estimation of Treatment Policy Estimands for Continuous Outcomes Using Off-Treatment Sequential Multiple Imputation.

The estimands framework outlined in ICH E9 (R1) describes the components needed to precisely define the effects to be estimated in clinical trials, which includes how post-baseline 'intercurrent' events (IEs) are to be handled. In late-stage clinical trials, it is common to handle IEs like 'treatment discontinuation' using the treatment policy strategy and target the treatment effect on outcomes regardless of treatment discontinuation. For continuous repeated measures, this type of effect is often estimated using all observed data before and after discontinuation using either a mixed model for repeated measures (MMRM) or multiple imputation (MI) to handle any missing data. In basic form, both these estimation methods ignore treatment discontinuation in the analysis and therefore may be biased if there are differences in patient outcomes after treatment discontinuation compared with patients still assigned to treatment, and missing data being more common for patients who have discontinued treatment. We therefore propose and evaluate a set of MI models that can accommodate differences between outcomes before and after treatment discontinuation. The models are evaluated in the context of planning a Phase 3 trial for a respiratory disease. We show that analyses ignoring treatment discontinuation can introduce substantial bias and can sometimes underestimate variability. We also show that some of the MI models proposed can successfully correct the bias, but inevitably lead to increases in variance. We conclude that some of the proposed MI models are preferable to the traditional analysis ignoring treatment discontinuation, but the precise choice of MI model will likely depend on the trial design, disease of interest and amount of observed and missing data following treatment discontinuation.

Green Innovation and Energy Efficiency: Moderating Effect of Institutional Quality Based on the Threshold Model

AbstractRecent studies demonstrated that green innovation and environment-related technologies reduce energy intensity and improve energy efficiency, contributing to the reduction of carbon emissions. However, the existing studies employ linear estimation methods to examine the relationship between green innovation and energy intensity and do not consider the indirect implications of institutional quality for the effect of green technology on energy intensity. Institutional quality is found to be an essential driver of innovation, and countries may need to achieve at least a minimum level of institutional quality to promote green innovation and improve their energy intensity. To test this hypothesis, this paper examines the relationship between energy intensity and green innovation using a panel dataset from 72 countries between 1996 and 2017 and a panel threshold model when institutional quality is considered a threshold variable. The findings highlight that green innovation reduces the energy intensity if and only if countries surpass a certain threshold of institutional quality. Therefore, countries need to improve their institutional quality to promote green innovation and benefit from green technologies in improving their energy intensity.

Next-generation statistical methodology: Advances health science research

Accurately modeling health science data is crucial for advancing medical research and improving patient outcomes. Traditional statistical analysis methods face significant challenges due to the complexity and diversity of health sciences data. This article introduces a groundbreaking statistical framework designed to overcome these challenges by developing a next-generation family of distributions, with a special focus on the versatility of the Weibull distribution. The data used in this study has been thoroughly authenticated to ensure reliability and validity. Comprehensive Monte Carlo simulations revealed that the Maximum Product of Spacing Estimator is the most effective among seven-point estimation methods, according to standard metrics. Additionally, the study identifies optimal methods for analyzing various types of lifetime data, including the Maximum Product of Spacing Estimator for pharmaceutical efficacy (ED50), the Least Squares Estimator for psychiatric treatment durations, the Cramer-von Mises Estimator for data on 43 leukemia patients and for survival periods of 20 leukemia patients, and the Right Tail Anderson-Darling Estimator for remission times of 128 bladder cancer patients. The adaptability and flexibility of the next-generation Weibull distribution set it apart as the best match among its contemporaries.

Evaluating Empirical, Field, and Laboratory Approaches for Estimating the Hydraulic Conductivity in the Kabul Aquifer

The evaluation of saturated hydraulic conductivity (Ks) constitutes an invaluable tool for the management and protection of groundwater resources. This study attempted to estimate Ks in the shallow aquifer of Kabul City, Afghanistan, in response to the occurring groundwater crisis caused by overexploitation and a lack of an appropriate monitoring system on pumping wells, based on datasets from well drilling logs, various analytical methods for pumping test analyses, and laboratory-based methodologies. The selection of Ks estimation methods was influenced by data availability and various established equations, including Theis, developed by Cooper–Jacob, Kruger, Zamarin, Zunker, Sauerbrei, and Chapuis, and pre-determined Ks values dedicated to well log segments exhibited the highest correlation coefficients, ranging between 60% and 75%, with the real conditions of the phreatic aquifer system with respect to the drawdown rate map. The results successfully obtained local-specific quantitative Ks value ranges for gravel, sand, silt, clay, and conglomerate. The obtained results fall within the high range of Ks classification, ranging from 30.0 to 139.8 m per day (m/d) on average across various calculation methods. This study proved that the combination of pumping test results, predetermined values derived from empirical and laboratory approaches, geological description, and classified soil materials and analyses constitutes reliable Ks values through cost-effective and accessible results compared with conducting expensive tests in arid and semi-arid areas.

Evaluation of input variable selection methods in artificial neural networks for estimating missing daily precipitation

This study evaluates different Input Variable Selection (IVS) methods for precise daily precipitation estimation using artificial neural network (ANN) models. The effectiveness of the models is measured using the average Nash-Sutcliffe efficiency (NSE) from 50 runs per model. Initially, a Multiple Linear Regression and a base model with all sixteen parameters resulted in test NSE scores of 0.390 and 0.656, respectively. Subsequently, the Self-Organizing Map (SOM), Analytic Hierarchy Process, K-Nearest Neighbour, and K-Means methods were used to select input variables of different types. The SOM method achieved its best performance with a test NSE of 0.692 when four variables were selected, while the other methods reached their peak performance with the same NSE of 0.718 when two similar variables were selected. This highlights the importance of IVS methods in using ANN models to overcome overfitting and achieve an efficient model for precise precipitation estimation.

An inverse identification method for automatic estimation of heat source model parameters for laser directed energy deposition

An inverse identification method for automatic estimation of heat source model parameters for laser directed energy deposition

CEO overconfidence and the speed of adjustment of cash holdings

In this study, we examine the links between CEO overconfidence, the speed of adjustment (SOA) of cash holdings, and firm value for listed US firms, finding a positive effect of CEO overconfidence on SOA. We address endogeneity concerns using a difference-in-differences approach, propensity score matching, and entropy balancing. Our results are robust to the use of alternative estimation methods. Finally, we conclude that financial constraints, leverage, and corporate governance quality affect the relation between CEO overconfidence and the SOA of cash holdings.

Reliability of In-band and Broadband Spectral Index Measurement: Systematic Study of the Effect of Signal-to-noise Ratio for uGMRT Data

Low-radio-frequency spectral index measurements are a powerful tool for distinguishing between different emission mechanisms and, in turn, understanding the nature of the sources. Besides the standard method of estimating the “broadband” spectral index of sources from observations in two different frequency “bands,” if the observations were made with large instantaneous bandwidth, the “in-band” spectral index can be determined, either using images of emission at multiple frequency ranges within a band or using the novel Multi Term-Multi Frequency Synthesis (MT-MFS) imaging algorithm. Here, using simulated upgraded Giant Metrewave Radio Telescope (uGMRT) data, we have systematically studied the reliability of various methods of spectral index estimation for sources with a wide range of signal-to-noise ratios (S/Ns). It is found that for synthetic uGMRT point-source data, the MT-MFS imaging algorithm produces in-band spectral indices for S/N ≲ 100 that have errors ≳0.2, making them unreliable. However, at a similar S/N, the sub-band splitting method produces errors ≲0.2, which are more accurate and unbiased than the in-band spectral indices. The broadband spectral indices produce errors ≲0.2 even for S/N ≳ 15, and hence they are most reliable if there are no higher-order variations in the spectral index. These results may be used to improve the uGMRT observation and data analysis strategies, depending on the brightness of the target source.

Comparative Study in Controlling Outliers and Multicollinearity Using Robust Performance Jackknife Ridge Regression Estimator Based on Generalized-M and Least Trimmed Square Estimator

Regression analysis is one of the statistical methods used to determine the causal relationship between one or more explanatory variables to the affected variable. The problem that often occurs in regression analysis is that there are multicollonity and outliers. To deal with such problems can be solved using ridge regression analysis and robust regression. Ridge regression can solve the problem of multicollinearas by assigning a constant k to the matrix Z′Z. But in this method the resulting bias value is still high, so to overcome this problem, the jackknife ridge regression method is used. Meanwhile, to overcome outliers in the data using robust regression methods which have several estimation methods, two of which are the Generalized-M (GM) estimator and the Least Trimmed Square (LTS) estimator. The aim of the study is to solve the problem of multicollinearity and outliers simultaneously using robust jackknife ridge regression method with GM estimators and LTS estimators. The results showed that the robust ridge jackknife regression method with LTS estimator can control multicollinearity and outliers simultaneously better based on MSE, AIC and BIC values compared to the robust ridge jackknife regression method with GM estimators. This is indicated by the value MSE = -6.60371, AIC = 75.823 and BIC = 81.642 on LTS estimators that are of lower value than GM estimators.

Mechanism and Data-Driven Dual Estimation of Coupling Hydraulic-Thermal States for Steam Heating Networks Considering Multi-Time-Scale Characteristics.

For a steam heating network (SHN), which is one of the most important parts of the integrated energy systems in an industrial park, it is essential to provide accurate and reliable estimation of hydraulic and thermal states, to ensure its operational safety and economy. Considering two different time-scale characteristics of the hydraulic and thermal processes in the SHN, this article proposes a mechanism and data-driven dual estimation method for the coupling hydraulic-thermal dynamic states. Due to the fact that the existing coupled estimation methods with one time scale may suffer from heavy computational burden, a data-driven dual sequential acrlong DSE method of the SHN is proposed based on the two-time scales, in which the dynamics of slow thermal states can also be captured when performing the acrlong SE of the fast hydraulic process. Furthermore, to improve the computational and communicational efficiency, a distributed interaction strategy based on the nodal transformation matrix is designed for large-scale steam systems. To verify the effectiveness of the proposed method, a single pipeline system and two real-world industrial superheated steam networks are employed. Compared to other state-of-the-art methods, the proposed method achieves the best tradeoff between the estimation accuracy and computational efficiency.

Probabilistic evaluation method for the stability of large underground cavern considering the uncertainty of rock mass mechanical parameters: A case study of Baihetan underground powerhouse project

For deep large underground cavern projects under complex geological conditions, the determination of rock mass mechanical parameters and stability analysis is fraught with a great deal of uncertainty. This paper proposes a set of methods for estimating rock mass mechanical parameters and probabilistic stability assessment suitable for construction characteristics of large underground caverns. On the one hand, according to the Hoek-Brown criterion and Monte Carlo simulation, a dynamic estimation method for rock mass mechanical parameters is proposed by using Rock Mass Rating (RMR), the uniaxial compressive strength (UCS) of intact rock, and the material constant (mb) as input parameters. On the other hand, considering the uncertainty of rock mass mechanical parameters, a probabilistic evaluation method applicable to the unloading response characteristics of rock mass (including displacement and fracturing zones) around large cavern is put forward, combining point estimation methods and numerical simulation. The proposed methods were applied to an underground powerhouse, with an excavation span of 34 m and height of 88.7 m, at the Baihetan hydropower station in the southwest of China. Based on extensive field investigations and tests, the probability distributions of key rock mass mechanical parameters were obtained. Furthermore, the high sensitivity of input parameters in the H-B criterion for estimating rock mass mechanical parameters were revealed. Through dynamic simulation, the probability distribution of the displacement and fracturing zones in surrounding rock during the layer-by-layer excavation of the cavern was presented. Comprehensive on-site tests showed that the simulated results were in basic agreement with the actual unloading response behavior during the cavern excavation, validating the correctness and applicability of the proposed methods. The study provides a relatively comprehensive approach for estimating rock mass mechanical parameters and stability evaluation in similar underground engineering projects, and also has guiding significance for predicting and preventing engineering rock mass hazards.