Duration Model Research Articles

Research on the Characteristics of Agricultural Drought Disaster in China Based on Three-Dimensional Copula Function

Estimating the probability and consequences of drought disasters is an important task in drought risk assessment, which contributes to the development of mitigation strategies. Based on rainfall data from 2481 stations and the drought-affected arable land of each province from 1961 to 2021, a probabilistic analysis model of drought duration, drought severity and the proportion of affected farmland area (PAFA) was constructed by a three-dimensional copula function. The results show that the distribution functions of drought duration, drought severity and the PAFA are well given based on the principle of maximum entropy and can pass the Kolmogorov–Smirnov (K-S) distribution test with a significance level of 0.05. Among the three Archimedean copulas, Frank’s method has a relatively better accuracy, suggesting that it captures the dependencies between drought characteristics better and is more suitable for constructing joint distribution functions. The value of PAFA in Gansu, Inner Mongolia, Shanxi and Liaoning is about 0.3, which is higher than other provinces. Drought duration and drought severity levels of 3 to 4 are the main causes of a PAFA greater than 0.3, which can be used as an early warning line for drought risk. At the same level of PAFA, the drought in the southern region lasted longer and was more intense.

Disentangling Neurodegeneration From Aging in Multiple Sclerosis Using Deep Learning: The Brain-Predicted Disease Duration Gap.

Disentangling brain aging from disease-related neurodegeneration in patients with multiple sclerosis (PwMS) is increasingly topical. The brain-age paradigm offers a window into this problem but may miss disease-specific effects. In this study, we investigated whether a disease-specific model might complement the brain-age gap (BAG) by capturing aspects unique to MS. In this retrospective study, we collected 3D T1-weighted brain MRI scans of PwMS to build (1) a cross-sectional multicentric cohort for age and disease duration (DD) modeling and (2) a longitudinal single-center cohort of patients with early MS as a clinical use case. We trained and evaluated a 3D DenseNet architecture to predict DD from minimally preprocessed images while age predictions were obtained with the DeepBrainNet model. The brain-predicted DD gap (the difference between predicted and actual duration) was proposed as a DD-adjusted global measure of MS-specific brain damage. Model predictions were scrutinized to assess the influence of lesions and brain volumes while the DD gap was biologically and clinically validated within a linear model framework assessing its relationship with BAG and physical disability measured with the Expanded Disability Status Scale (EDSS). We gathered MRI scans of 4,392 PwMS (69.7% female, age: 42.8 ± 10.6 years, DD: 11.4 ± 9.3 years) from 15 centers while the early MS cohort included 749 sessions from 252 patients (64.7% female, age: 34.5 ± 8.3 years, DD: 0.7 ± 1.2 years). Our model predicted DD better than chance (mean absolute error = 5.63 years, R2 = 0.34) and was nearly orthogonal to the brain-age model (correlation between DD and BAGs: r = 0.06 [0.00-0.13], p = 0.07). Predictions were influenced by distributed variations in brain volume and, unlike brain-predicted age, were sensitive to MS lesions (difference between unfilled and filled scans: 0.55 years [0.51-0.59], p < 0.001). DD gap significantly explained EDSS changes (B = 0.060 [0.038-0.082], p < 0.001), adding to BAG (ΔR2 = 0.012, p < 0.001). Longitudinally, increasing DD gap was associated with greater annualized EDSS change (r = 0.50 [0.39-0.60], p < 0.001), with an incremental contribution in explaining disability worsening compared with changes in BAG alone (ΔR2 = 0.064, p < 0.001). The brain-predicted DD gap is sensitive to MS-related lesions and brain atrophy, adds to the brain-age paradigm in explaining physical disability both cross-sectionally and longitudinally, and may be used as an MS-specific biomarker of disease severity and progression.

Duration Models for Subduction Earthquakes

ABSTRACT An empirical model for the significant duration of acceleration for subduction zone earthquakes is developed based on the normalized Arias intensity. The data set is a subset from the Next Generation Attenuation-Subduction database (Bozorgnia and Stewart, 2020). The functional form of the duration model uses an additive model for the source, path, and site terms with three lognormally distributed random effects for the source, path, and site. The remaining aleatory variability is modeled by a power-normal distribution. There are two parts of the duration model: a model for the D5–75 duration and a model for the D5–X/D5–75 ratio, which can be combined with the D5–75 model to estimate the DY–X for any interval. The D5–75 model includes regionalized coefficients for Japan, New Zealand, South America, and Taiwan. The large-magnitude (M &amp;gt;7) scaling of the D5–75 model is much weaker than the recent duration model by Bahrampouri et al. (2021). The residuals of the D5–75 model are negatively correlated with the lnPGA residuals with a correlation coefficient of −0.50 such that above-average peak ground acceleration values are associated with below-average duration values.

Parametric Quantile Autoregressive Conditional Duration Models With Application to Intraday Value‐at‐Risk Forecasting

ABSTRACTThe modeling of high‐frequency data that qualify financial asset transactions has been an area of relevant interest among statisticians and econometricians—above all, the analysis of time series of financial durations. Autoregressive conditional duration (ACD) models have been the main tool for modeling financial transaction data, where duration is usually defined as the time interval between two successive events. These models are usually specified in terms of a time‐varying mean (or median) conditional duration. In this paper, a new extension of ACD models is proposed which is built on the basis of log‐symmetric distributions reparametrized by their quantile. The proposed quantile log‐symmetric conditional duration autoregressive model allows us to model different percentiles instead of the traditionally used conditional mean (or median) duration. We carry out an in‐depth study of theoretical properties and practical issues, such as parameter estimation using maximum likelihood method and diagnostic analysis based on residuals. A detailed Monte Carlo simulation study is also carried out to evaluate the performance of the proposed models and estimation method in retrieving the true parameter values as well as to evaluate a form of residuals. Finally, we derive a semiparametric intraday value‐at‐risk (IVaR) model and then the proposed models are applied to two price duration data sets.

Assessing Urban Mobility Resilience: An Exploratory Approach Using Hazard-Based Duration Models

Urban systems are vulnerable to disturbances from both natural and human origins, which can disrupt their normal functioning. Evaluating the resilience of these systems, particularly the main transportation networks and their usage levels, is crucial and innovative for understanding the impacts of such disturbances. Thus, this work aims to assess resilience in urban mobility through the probability of a particular journey using a specific mode: “surviving” through critical travel time. To achieve this, a methodology was developed based on the Weibull model with gamma heterogeneity (hazard-based duration models), which was applied to a medium-sized Portuguese municipality. Eighteen groups representing active populations were set and compared. The results indicated that using the bus and cycling are the most resilient modes of transport, whereas walking is the least resilient. Additionally, a specific group was identified as having lower mobility resilience, making them more vulnerable to disruptions in the transport system. Finally, the findings of this study demonstrate the practical application of this methodology, which relies on travel time to assess resilience and, thus, guide political attention and actions to the less resilient mode. Future research should aim to develop a comprehensive framework that incorporates several variables to fully describe the complex nature of transport systems and their resilience.

Estimating Methane Emission Durations Using Continuous Monitoring Systems.

We propose a method for estimating methane emission durations on oil and gas sites, referred to as the Probabilistic Duration Model (PDM), that uses concentration data from continuous monitoring systems (CMS). The PDM probabilistically addresses a key limitation of CMS: nondetect times, or the times when wind blows emitted methane away from the CMS sensors (resulting in no detections). Output from the PDM can be used to bound the duration of emissions detected by survey-based technologies, such as plane or satellites, that have limited ability to characterize durations due to the typically low temporal frequency (e.g., quarterly) at which they observe a given source. Linear regression indicates that the PDM has a bias of -4.9% (R2 = 0.80) when evaluated on blinded controlled releases at the Methane Emissions Technology Evaluation Center (METEC), with 86.8% of estimates within a factor of 2× error from the true duration. We apply the PDM to a typical production site in the Appalachian Basin and use it to bound the duration of survey-based measurements. We find that failing to account for CMS nondetect times results in underestimated emission durations of up to a factor of 65× (6400%) on this site.

A novel analytical model of the tooth pair meshing duration of orthogonal face gears

A novel analytical model of the tooth pair meshing duration of orthogonal face gears

Empirical Monte Carlo evidence on estimation of timing-of-events models

This article builds on the Empirical Monte Carlo simulation approach to study the estimation of Timing-of-Events (ToE) models. We exploit rich Swedish data of unemployed job seekers with information on participation in a training program to simulate placebo treatment durations. We first use these simulations to examine which covariates are key confounders to be included in dynamic selection models for training participation. The joint inclusion of specific short-term employment history indicators (notably, the share of time spent in employment), together with baseline socio-economic characteristics, regional and inflow timing information, is important to deal with selection bias. Next, we omit subsets of explanatory variables and estimate ToE models with discrete distributions for the ensuing systematic unobserved heterogeneity. In many cases, the ToE approach provides accurate effect estimates, especially if time-varying variation in the unemployment rate of the local labor market is taken into account. However, assuming too many or too few support points for unobserved heterogeneity may lead to large biases. Information criteria, in particular those penalizing parameter abundance, are useful to select the number of support points. A comparison with other duration models shows that a Stratified Cox model performs well with abundant multiple spells but less well when multiple spells are uncommon. The standard Cox regression model performs poorly in all configurations as it is unable to account for unobserved heterogeneity.

Feature selection before propositionalization of multi-source oil drilling data

Despite recent improvements in collected drilling data quality and volume, the actual number of wells being used in studies remain low and are often limited to a single source and oil field, producing results that are prone to overfitting and are non-transferable. In our study, we access oil drilling data from 5 of more than 20 oil drilling companies collected from 2005 to 2016 from our industrial partner to create well drilling duration models for well planning. This project could lead to the creation of more generalized models from larger datasets than others in literature. However, the data is difficult to process without expert knowledge, further complicated by properties such as unharmonized, source-locked, semantic heterogeneity, sparse and unlabelled. Conventional automated methods for feature selection, propositionalization, multi-source, or block-wise missing techniques could not be used. In this paper, we describe our method to assist the Knowledge Discovery in Databases (KDD) Selection stage of the abovementioned data - Feature Selection before Propositionalization (FSbP) via Database Attribute Health Feature Reduction (DAHFR) and Report Feature Correlation Matrix (RFCM), collectively known as FvDR. DAHFR and RFCM are filter-type feature selection techniques that could measure relational missingness and keyword correlations respectively despite the complexity of multi-source oil drilling data. FvDR successfully reduced the scope from 700 tables containing 20,000 columns to 22 tables containing fewer than 707 columns while successfully selecting 13 of 16 relevant tables suggested by literature. Despite the loss of information from limitations of subsequent KDD procedures, preliminary models show promising results with over half the test predictions falling within the 20% error margin required for well planning. FvDR proves to be indispensable in KDD as a FSbP framework as it reduces features for examination and streamlines the research process necessary to understand business rules for data harmonization and propositionalization.

Investigating work-related distraction’s impact on male taxi driver safety: A hazard-based duration model

With the increasing use of phone-based ride-hailing apps, concerns have arisen regarding road safety and driver distraction. Despite the recognized safety risks of driver distraction, limited research has explored how distractions from various ride-hailing systems affect drivers in the taxi industry. To close this gap, the current research utilized a driving simulator experiment involving 51 male taxi drivers in two road environments (urban street and motorway) and three distracted driving conditions (no distraction, auditory distraction via radio dispatching system, and visual-manual distraction via mobile application). A car-following scenario with sudden brake events was incorporated into the experiments because this is a typical safety–critical situation where attention will determine the outcome. The collected performance indicators include brake reaction time, time headway, and car-following distance. The grouped random parameters Weibull accelerated failure time model was applied to model the duration data under different road conditions. The brake reaction time and time headway are dependent variables, while the car-following distance is a covariate in the models. The results indicate that although taxi drivers show longer brake reaction time when distracted by mobile app and radio system, this does not necessarily equate with greater risk or reduced safety since they compensate for the risk of rear-end crashes by maintaining a longer time headway. In general, taxi drivers’ brake reaction time and time headway are more profoundly affected by mobile apps when distracted in both urban and motorway scenarios. This highlights the elevated risks associated with such technologies. In addition, significant interaction effects revealed the observed heterogeneity, which suggests that drivers’ personal characteristics influence the relationship between distraction type and driving performance. This research provides valuable insights for designing safer ride-hailing operations and systems.

Analysis of the Duration of Mandatory Lane Changes for Heavy-Duty Trucks at Interchanges

Due to the different driving characteristics of different vehicle models, inappropriate mandatory lane changes (MLCs) by heavy vehicles at interchanges often lead to serious traffic accidents. Therefore, this paper focuses on the impact of road geometric design on the MLC duration of heavy trucks by using full time-domain trajectory data. Specifically, we use the generalized additive time-varying Cox model to establish the MLC duration model of heavy trucks at interchanges, then analyze the combined influence of geometric elements. The results show that the consistency index of the model is 0.9, indicating that it has advantages in building models in complex environments. The length of the deceleration lane, ramp type, and curve radius have a significant impact on the validity and duration of MLCs. This finding provides a theoretical and methodological reference for the safety analysis of interchange areas and the refinement of road geometric design.

Exploring the innovative effort: duration models and heterogeneity

Perseverance in R&D effort is the first fundamental step towards any kind of innovation. We investigate the beginning of the innovation story, rather than its end, through duration models. Among the drivers of our unconventional IO approach, we focus on heterogeneity, path dependence and market power, measured as elasticity of firm-specific demand. The Schumpeterian hypothesis emerges at the firm level. Heterogeneity at the industry level reveals Schumpeterian and Arrovian patterns, as well as U-shaped and inverted U-shaped patterns. We suggest considering the entire supply chain from a holistic perspective when evaluating mergers and innovation policies that support small firms by reducing their financial uncertainty and improving their institutional environment.

Transitioning from the University to the Workplace: A Duration Model with Grouped Data

Labor market surveys usually measure unemployment duration in time intervals. In these cases, traditional duration models such as Cox regression and parametric survival models are not suitable for studying the duration of unemployment spells. In order to deal with this above issue, we use Han and Hausman’s ordered logit model for grouped durations, which has more flexibility than standard specifications. In particular, its flexibility arises from the fact that we do not need to specify any functional form for the baseline hazard function—it also circumvents problems associated with heterogeneity. The focus of interest is on the first unemployment duration of higher education graduates. The analysis is accomplished by using a large dataset from a graduate survey of Spanish university graduates. The results show that the university-to-work transition of higher education graduates is significantly associated with the graduate’s age, participation in internship programs, field of study, type of university, and gender. Specifically, graduates who participated in internship programs, engineering graduates, and graduates from private universities experience a smooth transition.

Neurotoxicity of nanoplastics: A review

With the increase in plastic waste in the environment, it is undeniable that humans and most organisms are exposed to plastic particles of various sizes, including nanoplastics (NPs). Humans are at risk owing to various routes of entry, including ingestion, inhalation, and dermal contact. While the toxicity of NPs is still debatable due to the scarcity of resources and research, most studies have concluded that NPs may exert toxicity, which exacerbates their neurotoxicity potential. Earlier studies concluded that NPs can cause oxidative stress, which results in apoptosis of neuronal cells. Some studies have shown that NPs can affect fundamental cell functions by inducing physical stress through deposition. Furthermore, studies on in vivo models exposed to NPs have demonstrated behavioral changes that are presumably due to alterations in acetylcholinesterase activity and neurotransmitter levels. This review discusses studies conducted on the neurotoxic potential of NPs and their effects, which are dependent on several parameters, including size and type of NPs, exposure concentration, duration, and various models at risk of NP exposure. Furthermore, speculations on how NPs are related to neurotoxicity are also discussed.

Language confidence and job satisfaction among foreign-born nurses in Japan: mediating effect of workplace discrimination and moderating effect of immigration duration

AimThis study explored the relationship between language confidence and job satisfaction, the mediating role of workplace discrimination, and the moderating role of immigration duration among foreign-born nurses in Japan.IntroductionJob satisfaction is an important factor in preventing migrant nurses’ turnover intentions; however, the relationships among language confidence, immigration duration, workplace discrimination, and job satisfaction among foreign-born nurses remain unclear.MethodsA cross-sectional study was conducted. Data were collected between June and August 2022 through an online survey of nurses who were born outside of Japan but were currently working as registered nurses in Japan. PROCESS v4.0 Macro for SPSS 28.0 was applied to analyze the effect of language confidence on job satisfaction, the mediator effect of workplace discrimination (model 4), and the moderator effect of immigration duration (model 15).ResultsData from 187 participants were analyzed. The results showed that 1) foreign-born nurses’ language confidence was negatively correlated with workplace discrimination and positively correlated with job satisfaction; 2) workplace discrimination played a partially mediating role between language confidence and job satisfaction; and 3) immigration duration positively moderated the relationship between language confidence and job satisfaction.ConclusionForeign-born nurses with stronger confidence in their proficiency in Japanese perceived less workplace discrimination and higher job satisfaction. Workplace discrimination acted as a mediator in the relationship between language confidence and job satisfaction, and this relationship was strengthened with longer migration periods. Managers and policymakers should implement policies and strategies to combat workplace discrimination and provide tailored support to improve foreign-born nurses’ job satisfaction, which may contribute to their retention in Japan.

Reprint of: Profiling the plight of disconnected youth in America

In a successful transition from youth to adulthood, individuals pass through a sequence of roles involving school, work, and family formation that culminate in their becoming self-sufficient adults. However, some “disconnected” youth spend extended periods of time outside of any role that constitutes an element of the pathway towards adult independence. Assisting these youth requires a systematic understanding of what “disconnection” means, how many disconnected youth there are, who these youth are, and how the scale of the problem has evolved over time. Using the National Longitudinal Surveys of Youth for 1997 and 1979, we address these issues by creating concrete definitions of “disconnection spells” using rich data on youths’ enrollment, work, and personal histories. We estimate a multi-state duration model to account for right censoring and to understand differences across salient sub-groups. Our estimates imply that in the early 2000s, almost 19% and 25% of young men and young women, respectively, experienced a disconnection spell by age 23 using our basic definition. These rates are substantially higher for certain sub-groups defined by race/ethnicity, parental education, and government aid receipt, rising as high as 30+% by age 23. Approximately 60% of youth with a disconnection spell have it last longer than a year, and close to 10% have it last longer than 4 years. However, once reconnected, a majority of youth go at least three years without a re-disconnection spell. Patterns of initial disconnection changed markedly from the 1980s to the 2000s, as young women saw a 12 percentage point decline over time. Moreover, the Black-White gap in disconnection has fallen for women, but increased for men. Our profile of disconnection experiences provides a starting point for government agencies aiming to understand where, how, and with whom to intervene to prevent lengthy disconnection spells.

Using Machine Learning to Predict the Duration of Atrial Fibrillation: Model Development and Validation.

Atrial fibrillation (AF) is a progressive disease, and its clinical type is classified according to the AF duration: paroxysmal AF, persistent AF (PeAF; AF duration of less than 1 year), and long-standing persistent AF (AF duration of more than 1 year). When considering the indication for catheter ablation, having a long AF duration is considered a risk factor for recurrence, and therefore, the duration of AF is an important factor in determining the treatment strategy for PeAF. This study aims to improve the accuracy of the cardiologists' diagnosis of the AF duration, and the steps to achieve this goal are to develop a predictive model of the AF duration and validate the support performance of the prediction model. The study included 272 patients with PeAF (aged 20-90 years), with data obtained between January 1, 2015, and December 31, 2023. Of those, 189 (69.5%) were included in the study, excluding 83 (30.5%) who met the exclusion criteria. Of the 189 patients included, 145 (76.7%) were used as training data to build the machine learning (ML) model and 44 (23.3%) were used as test data for predictive ability of the ML model. Using a questionnaire, 10 cardiologists (group A) evaluated whether the test data (44 patients) included AF of more than a 1-year duration (phase 1). Next, the same questionnaire was performed again after providing the ML model's answer (phase 2). Subsequently, another 10 cardiologists (group B) were shown the test results of group A, were made aware of the limitations of their own diagnostic abilities, and were then administered the same 2-stage test as group A. The prediction results with the ML model using the test data provided 81.8% accuracy (72% sensitivity and 89% specificity). The mean percentage of correct answers in group A was 63.9% (SD 9.6%) for phase 1 and improved to 71.6% (SD 9.3%) for phase 2 (P=.01). The mean percentage of correct answers in group B was 59.8% (SD 5.3%) for phase 1 and improved to 68.2% (SD 5.9%) for phase 2 (P=.007). The mean percentage of answers that differed from the ML model's prediction for phase 2 (percentage of answers where cardiologists did not trust the ML model and believed their own determination) was 17.3% (SD 10.3%) in group A and 20.9% (SD 5%) in group B and was not significantly different (P=.85). ML models predicting AF duration improved the diagnostic ability of cardiologists. However, cardiologists did not entirely rely on the ML model's prediction, even if they were aware of their diagnostic capability limitations.

Multi-level time-varying causality analysis of secondary conflicts based on hazard-based duration models

Secondary conflicts occur frequently and would cause multi-vehicle collisions. In order to prevent multi-vehicle collisions, a better understanding of the factors that affect secondary conflict propagation is crucial. Previous studies have identified the influencing factors of primary conflicts’ occurrence, but have not explored the time-varying factors that affect secondary conflicts’ propagation. In addressing this gap, about 20,000 secondary conflicts are extracted from real trajectory dataset, and a multi-level variable system is established, including segment types, traffic status, front chain conflict status, and direct interaction behaviors. Further, a Kaplan-Meyer model and a random parameters hazard-based duration model are constructed to explore the single-factor and multiple-factor influence on the propagation of secondary conflicts, respectively. The results suggest that the first 2.6 s after a conflict is a critical post-monitoring period to prevent the secondary conflicts propagation. In addition, diverging and merging segments shorten the survival time of secondary conflicts by about 12%, indicating a higher occurrence probability of secondary conflicts near the ramps of expressways. More importantly, the front chain conflict status and the front direct conflict status reveal a different effect on the secondary conflicts. The high risk of chain conflict ahead would increase the occurrence probability of secondary conflicts, while the high risk of front conflict would decrease the probability. Overall, this research is of great significance to understand the influencing factors of secondary conflict and avoid secondary crashes.

Duration and resource constraint prediction models for construction projects using regression machine learning method

PurposeThe construction projects are highly subjected to uncertainties, which result in overruns in time and cost. Realistic estimates of workforce and duration are imperative for construction projects to attain their intended objectives. The aim of this study is to provide accurate labor and duration estimates for the construction projects, considering actual uncertainties.Design/methodology/approachThe dataset was formulated from the information collected from 186 construction projects through direct interviews, group discussions and questionnaire methods. The actual uncertainties and exposure conditions of construction activities were recorded. The data were verified with the standard guideline to remove the outliers. The prediction model was developed using support vector regression (SVR), a machine learning (ML) method. The performance was evaluated using the widely adopted regression metrics. Further, the cross validation was made with the visualization of residuals and predicted errors, ridge regression with transformed target distribution and a Gaussian Naive Bayes (NB) regressor.FindingsThe prediction models predicted the duration and labor requirements with the consideration of actual uncertainties. The residual plot indicated the appropriate use of SVR to develop the prediction model. The duration (DC) and resource constraint (RC) prediction models obtained 80 and 82% accuracy, respectively. Besides, the developed model obtained better accuracy for the training and test scores than the Gaussian NB regressor. Further, the range of the explained variance score and R2 was from 0.95 to 0.97, indicating better efficiency compared with other prediction models.Research limitations/implicationsThe researchers will utilize the research findings to estimate the duration and labor requirements under uncertain conditions and further improve the construction project management practices.Practical implicationsThe research findings will enable industry practitioners to accurately estimate the duration and labor requirements, considering historical uncertain conditions. A precise estimation of resources will ensure the attainment of the intended project outcomes.Social implicationsDelays in construction projects will be reduced by implementing the research findings, which significantly ensures the effective utilization of resources and attainment of other economic benefits. The policymakers will develop a guideline to develop a database to collect the uncertainties of the construction projects and relatively estimate the resource requirements.Originality/valueThis is the first study to consider the actual uncertainties of construction projects to develop RC and DC prediction models. The developed prediction models accurately estimate the duration and labor requirements with minimal computational time. The industry practitioners will be able to accurately estimate the duration and labor requirements using the developed models.

A systematic review of models for the prediction of corporate insolvency

A thorough evaluation of recent developments in bankruptcy prediction models developed specifically for listed firms in India is presented in this research. Beginning with influential contributions from the evolution of bankruptcy prediction methodologies is traced through various statistical techniques, including logistic regression, neural networks and discriminant analysis. Recent innovations, such as duration models, partial least squares with support vector machines, and efficiency-driven distress prediction, are discussed in the context of their applicability to the Indian market. The paper highlights the significance of early warning systems in the wake of bankruptcy reforms in India and examines the regulatory framework's impact on bankruptcy prediction modeling. And it goes further into how macroeconomic variables and industry-specific variables might make bankruptcy models better predictors. Limitations such as small sample sizes, short time periods for samples, and the incorporation of qualitative data into predictive models are highlighted in the study as areas that require further investigation in future studies. Overall, this paper provides valuable insights for academics, practitioners, and policymakers involved in bankruptcy prediction and risk management within the Indian corporate sector.