Alternative Estimation Methods Research Articles

Prognostic nomogram model based on quantitative metrics of subregions surrounding residual cavity in glioblastoma patients

BackgroundThe hyperintensity area surrounding the residual cavity on postoperative fluid-attenuated inversion recovery (FLAIR) image is a potential site for glioblastoma (GBM) recurrence. This study aimed to develop a nomogram using quantitative metrics from subregions of this area, prior to chemoradiotherapy (CRT), to predict early GBM recurrence.MethodsAdult patients with GBM diagnosed between October 2018 and October 2022 were retrospectively analyzed. Quantitative metrics, including the mean, maximum, minimum, median values, and standard deviation of FLAIR signal intensity (SI) (measured using 3D-Slicer software), were extracted from the following subregions surrounding the residual cavity on post-contrast T1-weighted (CE-T1WI)-FLAIR fusion images: the enhancing region (ER), non-enhancing region (NER), and combined ER + NER. Independent prognostic factors were identified using Cox regression and least absolute shrinkage and selection operator (LASSO) analyses and were incorporated into the prediction nomogram model. The model’s performance was evaluated using the C-index, calibration curves, and decision curves.ResultsA total of 129 adult GBM patients were enrolled and randomly assigned to a training (n = 90) and a validation cohorts (n = 39) in a 7:3 ratio. Sixty-nine patients experienced postoperative recurrence. Cox regression analysis identified subventricular zone involvement, the median FLAIR intensity in the ER, the rFLAIR (relative FLAIR intensity compared to the contralateral normal region) of ER + NER, and corpus callosum involvement as independent prognostic factors. For predicting recurrence within 1 year after surgery, the nomogram model had a C-index of 0.733 in the training cohort and 0.746 in the validation cohort. Based on the nomogram score, post-operative GBM patients could be stratified into high- and low-risk for recurrence.ConclusionsNomogram models which based on quantitative metrics from FLAIR hyperintensity subregions may serve as potential markers for assessing GBM recurrence risk. This approach could enhance clinical decision-making and provide an alternative method for recurrence estimation in GBM patients.

Parameter Identification Method of a Double-Layer Supercapacitor by Using a Real Voltage Source

This article presents a new method for obtaining the electrical parameters of a supercapacitor (SC) modeled as a constant resistor in series with a capacitance that linearly varies with its internal voltage. This model provides sufficiently accurate results when the SC is subjected to rapid, short-term charging and discharging. In other methods described in the literature, the parameters are obtained by charging or discharging the SC with a constant current source of high value. In this study, the electrical parameters are calculated by charging or discharging the cell with a real constant voltage source (RVS) or by discharging the SC through a known and constant resistance. The calculation procedure requires the measurement of the cell voltage as a function of time. Two alternative estimation methods have been employed: the three-point method (3PM) and the least squares method (LSM). A series of experimental tests were conducted on cells from various manufacturers, with capacitances ranging from 150 F to 600 F. The laboratory measurements were then compared with the results obtained from theoretical models incorporating the parameters obtained for the variable capacity model. The results demonstrated that this straightforward procedure is capable of accurately characterizing the main branch of any SC.

ESG and firm performance: The effects of foreigners on board and export orientation

AbstractThis study investigates whether environmental, social, and governance (ESG) disclosures enhance industry‐adjusted firm performance and whether the presence of foreigners on board and export orientation affect this relationship. The Taiwan dataset from 2013 to 2020 indicates that ESG practices positively affect accounting‐ and market‐based performance (i.e., adjusted Tobin's Q and adjusted MTB ratio). However, ESG has no significant relationship with purely market‐based performance (i.e., adjusted Jensen Alpha and adjusted FF3). The results illustrate a significant joint effect of ESG with foreigners on board and export experience on accounting and market‐based performance. The findings provide deeper insight into the complex relationship between sustainability practices, ownership structures, and international businesses. Our findings remain consistent when using alternative estimation methods to account for endogeneity and selection bias.

Modelling multivariate spatio-temporal data with identifiable variational autoencoders

Modelling multivariate spatio-temporal data with complex dependency structures is a challenging task but can be simplified by assuming that the original variables are generated from independent latent components. If these components are found, they can be modelled univariately. Blind source separation aims to recover the latent components by estimating the unknown linear or nonlinear unmixing transformation based on the observed data only. In this paper, we extend recently introduced identifiable variational autoencoder to the nonlinear nonstationary spatio-temporal blind source separation setting and demonstrate its performance using comprehensive simulation studies. Additionally, we introduce two alternative methods for the latent dimension estimation, which is a crucial task in order to obtain the correct latent representation. Finally, we illustrate the proposed methods using a meteorological application, where we estimate the latent dimension and the latent components, interpret the components, and show how nonstationarity can be accounted and prediction accuracy can be improved by using the proposed nonlinear blind source separation method as a preprocessing method.

Estimating Rainfall Intensity Using an Image-Based Convolutional Neural Network Inversion Technique for Potential Crowdsourcing Applications in Urban Areas

High-quality rainfall data are essential in many water management problems, including stormwater management, water resources management, and more. Due to the high spatial–temporal variations, rainfall measurement could be challenging and costly, especially in urban areas. This could be even more challenging in tropical regions with their typical short-duration and high-intensity rainfall events, as some of the undeveloped or developing countries in those regions lack a dense rain gauge network and have limited resources to use radar and satellite readings. Thus, exploring alternative rainfall estimation methods could be helpful to back up some shortcomings. Recently, a few studies have examined the utilisation of citizen science methods to collect rainfall data as a complement to the existing rain gauge networks. However, these attempts are in the early stages, and limited works have been published on improving the quality of such data. Therefore, this study focuses on image-based rainfall estimation with potential usage in citizen science. For this, a novel convolutional neural network (CNN) model is developed to predict rainfall intensity by processing the images captured by citizens (e.g., by smartphones or security cameras) in an urban area. The developed model is merely a complementary sensing tool (e.g., better spatial coverage) to the existing rain gauge network in an urban area and is not meant to replace it. This study also presents one of the most extensive datasets of rain image data ever published in the literature. The estimated rainfall data by the proposed CNN model of this study using images captured by surveillance cameras and smartphone cameras are compared with observed rainfall by a weather station and exhibit strong R2 values of 0.955 and 0.840, respectively.

Linear measurements of the mandible on panoramic radiograph for sex estimation in populations in Yogyakarta, Indonesia

BackgroundIdentification is one of the main aspects of forensics. Sex estimation is an essential part of identification because it can simplify the whole process. Mandible is the largest, strongest, and sexually dimorphic bone and is part of the orofacial structure, which has the second highest level of dimorphism after the pelvic bone. Sex estimation using the mandible can be performed by conducting various linear measurements on a panoramic radiograph, including right minimum ramus width (RMiRW), left minimum ramus width (LMiRW), right projective height of ramus (RPHR), left projective height of ramus (LPHR), right maximum ramus width (RMxRW), left maximum ramus width (LMxRW), right coronoid height (RCH), left coronoid height (LCH), symphysis height (SH), right mandibular corpus height (RMCH), and left mandibular corpus height (LMCH). This study aimed to analyze how linear measurements in panoramic radiographs differ between men and women and to estimate sex by utilizing these parameters.ResultsIn this study, 195 panoramic radiographs from 95 men and 100 women aged 20–40 years obtained from our dental hospital were used as a training data. Meanwhile, 61 panoramic radiographs from 29 men and 32 women outside the training data with same characteristics were used as the testing data. The linear measurements of the mandible using panoramic radiographs were taken with EzDent-i Vatech software. Independent t-test showed significant difference (p < 0.05) in some linear measurements of the mandible between adult men and women. These significantly different linear measurements were then subjected to discriminant function analysis to produce sex estimation equations. The equation accuracy percentage ranged between 63.6 and 94.4% for the training sample and 59.5% and 85% for the testing sample.ConclusionThe linear measurements of the mandible using diagnostically acceptable panoramic radiographs taken from a patient with standardized head positioning can serve as an alternative method for sex estimation. The accuracy of discriminant analysis for sex estimation varies depending on the parameter used in the estimation.

Impact of city size on self-employment behavior: Evidence from Chinese migrants

Self-employment is an important approach for migrants to gain a foothold in cities and improve their economic and social welfare. This study investigates the relationship between city size and migrants’ self-employment behavior using data from the 2011–2018 CMDS and macro-statistical sources. We find that in the Chinese context, larger cities correlate with an increased likelihood of migrants engaging in self-employment. These results persist under various methodological checks, including alternative estimation methods, sample screening techniques, and addressing concerns about endogeneity. Market potential and diversified agglomeration emerge as important channels through which larger cities encourage migrants’ decisions to pursue self-employment. Moreover, the positive impact of larger cities is more pronounced among rural-to-urban migrants and own-account workers compared to urban-to-urban migrants and employers. This paper provides insights into high-quality development and migrant management strategies in large cities in China.

Analysis of Seasonal Time Poverty and Aspirations in the Upper East Region of Ghana

While studies on aspiration formation have progressively gained the interest of development practitioners and policymakers, little is known about the link between seasonal time poverty and aspirations. This study uses primary data on 337 smallholder households in rural northern Ghana to examine the relationship between seasonal time poverty and aspirations about income, assets, child education, and social status from household headship and age perspectives. The results indicate that seasonal time poverty is correlated with higher aspirations of income and assets but a lower social status. The effect is more significant in dry than wet seasons, and more evident in female- and adult-headed households. The findings are robust to alternative estimation methods for addressing endogeneity. Income is identified as the main potential channel through which seasonal time poverty influences aspirations formation and the implications of the findings are discussed. HIGHLIGHTS In Ghana, seasonal time poverty is positively associated with income and asset aspirations. Seasonal time poverty reduces social status aspiration. The effect of seasonal time poverty on aspiration is higher among women and adults. Programs that promote rural household income must be targeted, pursued, and sustained to improve aspirations.

Selection Bias in Reporting of Median Waiting Times in Organ Transplantation

Median organ waiting times published by transplant organizations may be biased when not appropriately accounting for censoring, death, and competing events. This can lead to overly optimistic waiting times for all transplant programs and, consequently, may deceive patients on the waiting list, transplant physicians, and health care policymakers. To apply competing-risk multistate models to calculate probabilities for transplantation and adverse outcomes on the Swiss national transplant waiting list. The WAIT (Waitlist Analysis in Transplantation) study was a retrospective cohort study of all transplant candidates in Switzerland listed from January 1, 2018, or later and observed until December 31, 2023. Transplant candidates were listed in 1 of the 6 transplant centers (Basel, Bern, Geneva, Lausanne, St Gallen, and Zurich) for heart, liver, lungs, kidney, or pancreas and/or islet transplant. A total of 4352 candidates were listed during the study period, of whom 709 (16.3%) were excluded due to living-donor transplant (691 in the kidney program and 18 in the liver program). Waiting for organ transplant. Time to transplantation, death, or delisting. Competing-risk multistate models were used to analyze time-to-event data from the national organ waiting list with the Aalen-Johansen estimator to compute probabilities for both transplant and adverse outcomes. Results were compared with the sample median among only those undergoing transplant and the Kaplan-Meier method with censoring of competing events. Data from 3643 transplant candidates (2428 [66.6%] male; median age, 56 [range, 0-79] years) were included in the analysis. The median time to transplantation (MTT) was 0.91 (95% CI, 0.83-1.07) years for heart, 3.10 (95% CI, 2.57-3.77) years for kidney, 1.32 (95% CI, 0.76-1.55) years for liver, 0.80 (95% CI, 0.37-1.12) years for lung, and 1.62 (95% CI, 0.91-2.17) years for pancreas and/or islet programs. Alternative estimation methods introduced bias to varying degrees: the sample median among only persons undergoing transplantation underestimated the waiting time by 38% to 61% and the Kaplan-Meier method by 2% to 12% compared with the MTT. In this cohort study of transplant candidates in Switzerland, the MTT, the duration at which the transplant probability is 0.50, was used as a measure of average waiting time. Suboptimal methods led to biased and overly optimistic waiting time estimations; thus, applying appropriate competing-risk methods to address censoring and competing events is crucial.

Compliance measurement and labour regulation enforcement: a randomized field experiment

ABSTRACT Labour regulation matters for the economy. But measuring firms’ compliance with this regulation poses significant challenges as inspections, enforcement, or reporting might be correlated to compliance risk factors of each firm. We propose estimating the compliance rate by a randomized controlled trial (RCT) of standard labour inspections. We run the experiment on small and medium firms (SME) in Chile. We estimate a non-compliance sanction rate of 13%. Our results are robust to various alternative estimation methods. Additional results look at the effects of enforcing labour laws, showing no significant differences on firms’ growth; neither on the intensive (employment and sales) nor the extensive margin (exit). Nonetheless, the experiment had limited power for these additional results. Our methods and results matter for the monitoring and enforcement of labour policies.

Estimating vehicle sideslip angle through kinematic and dynamic contributions: Theory and experimental results

Vehicle lateral stability plays an important role within vehicle passenger safety. The study of lateral stability is typically related to investigating the dynamics of relevant vehicle states: among these, the vehicle sideslip angle ([Formula: see text]) emerges as a prominent candidate. Sideslip angle measurement is expensive and impractical, hence estimation techniques are often used, typically based on Kalman filters or neural networks, both with their issues. This work presents an alternative estimation method based on the idea of splitting sideslip angle into kinematic and dynamic contributions, and by observing that the kinematic contribution is straightforward to estimate. Therefore, efforts are devoted into estimating dynamic sideslip angle, which is herein obtained through a parametric interpolation harnessing lateral acceleration. Only data available from traditional vehicle onboard sensors are used in the process. Experimental results are presented along several manoeuvres on a full-scale vehicle, with the estimator running online within a dSPACE unit, ultimately supporting the efficacy and real-time feasibility of the proposed approach.

Calibration and Evaluation of Alternative Methods for Reliable Estimation of Reference Evapotranspiration in South Korea

Calibration and Evaluation of Alternative Methods for Reliable Estimation of Reference Evapotranspiration in South Korea

The Impact of Stochastic Volatility on Option Pricing Using the Black-Scholes Model: Empirical Evidence from NVIDIA

This paper investigates the impact of volatility on option pricing using the Black-Scholes model. Utilizing historical stock and option data from NVIDIA, this study demonstrates the limitations of the Black-Scholes model in capturing dynamic market conditions. The empirical analysis reveals significant discrepancies between model predictions and actual market prices, highlighting the importance of accurate volatility estimation. The findings suggest that incorporating better volatility measures can improve the accuracy of option pricing models. Additionally, the implications of these findings for traders and risk managers are discussed, emphasizing the need for more sophisticated approaches to volatility estimation in financial markets. This study adds to the body of knowledge by presenting actual data regarding the efficiency of different volatility metrics in option pricing. Moreover, it underscores the necessity of continuous improvement in financial modeling techniques to adapt to changing market dynamics. The study's results align with previous research that has shown the limitations of the Black-Scholes model and the benefits of alternative volatility estimation methods. This paper also discusses the potential application of these findings to improving trading strategies and risk management practices. The practical implications are significant, suggesting that traders and risk managers should consider incorporating advanced volatility measures into their models to enhance decision-making processes and financial outcomes. The conclusions drawn from this study highlight the critical role of accurate volatility estimation in achieving more reliable option pricing and underscore the need for ongoing research and innovation in this field.

A simple bootstrap for Chatterjee’s rank correlation

Abstract We prove that an m-out-of-n bootstrap procedure for Chatterjee’s rank correlation is consistent whenever asymptotic normality of Chatterjee’s rank correlation can be established. In particular, we prove that m-out-of-n bootstrap works for continuous as well as discrete data with independent coordinates; furthermore, simulations indicate that it also performs well for discrete data with dependent coordinates, and that it outperforms alternative estimation methods. Consistency of the bootstrap is proved in the Kolmogorov distance as well as in the Wasserstein distance.

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.

A method for estimating the bulk density and particle density of granite residual soil based on the construction of pedotransfer functions

AbstractParticle density (ρs) and bulk density (ρb) are key factors in the calculation of total soil porosity. However, direct measurements of ρs and ρb are labour‐intensive, time‐consuming, and sometimes impractical. Pedotransfer functions (PTFs) provide alternative methods for indirect estimation of ρs and ρb. In this paper, the accuracy of typical 12 ρs and 9 ρb PTFs was evaluated using easily measurable soil properties (sand, silt, clay, and soil organic matter (SOM) content) from granitic residual soils collected from six study areas in subtropical China, and the accuracy of PTFs constructed based on multiple linear stepwise regression (MSR) and machine‐learned algorithms (backpropagation neural network, k‐nearest neighbour algorithms, random forests, support vector machines, and gradient boosted decision trees) was compared to determine the accuracy of PTFs. The results show that typical PTFs have poor accuracy (R2adjusted &lt; 0.020) and are not applicable to the indirect estimation of ρs and ρb in granitic residual soils. The PTFs constructed by machine learning algorithms all performed better than MSR, with the highest estimation accuracy of the PTFs constructed by the random forest algorithm, with R2adjusted values of 0.923 and 0.933 for the ρs and ρb PTFs, respectively, and root‐mean‐square error of 0.020 g·cm−3 and 0.023 g·cm−3, respectively. Compared with MSR, the random forest algorithm has greater accuracy and eliminates the restriction of PTFs on predictors, which provides support for understanding the changing rules of ρs and ρb in granite residual soils in subtropical regions, evaluating soil quality and improving soil structure.

GFR Estimation and Correlation for Oncology Patients by Two Methods, Gates Method and Dual Time Point Plasma Sampling Method

Background: With the increasing number of oncology cases and a parallel surge in chemotherapeutic drugs for treatment, the treating physicians conducts nephrotoxicity evaluation to provide a personalized dosing strategy. Of the various tests available, glomerular filtration rate (GFR) under gamma camera with help of Gates method has gained importance, being a good index of overall kidney functions. In addition to this, there has been an alternate and old method for GFR estimation: plasma sampling. We at our Institution conducted both the methods for better evaluation of GFR in cancer patient management. Aim: Comparison of Gates’ camera based GFR based on kidney depth correction using Tonessen’s method and CT based manual depth calculation with dual time point plasma sampling in cancer patients. Method: A retrospective study wherein patients’ database were evaluated over a period of four months after approval from our Institutional Review Board. Thirty patients were included in the study. GFR was evaluated by two methods: Gates camera based and dual time plasma sampling method. Statistical analysis was done to help evaluate a correlation coefficient between the methods (Gates’ method with and without CT based manual depth correction and dual time point plasma sampling). Results: Our study showed moderate correlation between Gates’ camera based GFR and dual time plasma sampling method. Conclusion: One need to understand the limitation of each method and see if the renal depth corrections can be done with the help of CT or lateral images of NM for near accurate GFR and in case of selecting dual plasma sampling, errors to be minimized in pipetting and sample counting. Hence, it will be better to use both the methods for coming to a conclusion.

Electrification or deforestation? Evidence from household practices in Côte d’Ivoire

This paper investigates the impact of electrification on household practices related to deforestation in Côte d’Ivoire, specifically focusing on the expansion of arable farms and the use of biomass fuels. A theoretical framework inspired by the heterogeneous agricultural households model proposed by Angelsen (1999) is employed to theoretically elucidate the relationship between electrification and the expansion of arable farms. Using data from the latest four waves of the household Living Standards Measurement Surveys (1998, 2002, 2008, and 2015) and a pseudo-panel fixed effects regression model, we find that increased access to electricity significantly reduces both the average size of arable farms and the collection of firewood from forests. These results are robust across various alternative specifications and estimation methods. Moreover, an electrification threshold of approximately 80% has been identified, beyond which the beneficial impacts of electrification on forest loss would disappear.

Energy innovation investment and renewable energy in OECD countries

Achieving carbon neutrality by 2050 remains fundamental to limiting global warming to 1.5 °C this century and mitigating the catastrophic effects of climate change. Policymakers have indicated that the transition towards a renewable energy economy is the catalyst for achieving this. Transitioning towards a renewable energy economy requires substantial investment in renewable energy technologies. While most empirical studies have explored the linkage between investment in research and development (R&D) and carbon emissions, not much is known empirically about the effect of energy innovation R&D on renewable energy generation. This study, therefore, contributes to the literature by investigating the impact of energy innovation R&D on renewable energy generation using a comprehensive panel dataset of 26 OECD countries from 1974 to 2020. Using a battery of robust alternative estimation methods, the results indicate that energy innovation R&D generally does not increase total renewable energy generation in the panel of OECD countries. The results further show that energy innovation R&D has a heterogeneous effect on disaggregated renewable energy sources such as solar energy, wind energy, nuclear energy, and hydro energy generation.

Economic sanctions and sovereign debt default

This paper analyzes the effect of US economic sanctions on sovereign debt default across 118 developing countries from 1980 to 2018. We use entropy balancing method, and provide robust evidence that US sanctions increase sovereign debt default in the targeted states. This finding withstands several robustness tests, including alternative specifications and databases, and alternative estimation methods. The results also reveal some heterogeneity depending on the type of sanctions (for example, trade versus financial sanctions), on whether they are imposed unilaterally or multilaterally, on the severity of the sanctions, on the geographical distance from the United States, the time since sanctions were imposed, and some structural characteristics. In addition, we find that the development of cryptocurrencies reduces the effect of sanctions, and that failed sanctions have a greater effect on default. The compositon effect based on the type of debt in default shows that the effect of US sanctions is only observed on the default on external debt and not on that of internal debt. Next, we identify three main channels through which sanctions trigger sovereign risk; namely economic growth contraction, the occurrence of banking crisis and reduced access to international financial market. Finally, we explore the effect of other sanctions, notably those of the European Union and the United Nations, and find that these sanctions also raise the probability of sovereign debt default.