Quantile Values Research Articles

Analyzing the Effect of Factors on Individuals’ Subjective Well-Being with Quantile Bayesian Structural Equation Modeling Approach

This study investigates factors influencing individuals’ levels of subjective well-being by examining fundamental variables, called life domains, such as satisfaction with health, education, marriage, housing district, work life, social life, relationships, and public services. The aim is to understand how these domains affect various quantile values of subjective well-being. To achieve this, the Life Satisfaction Survey dataset for 2020, obtained from the Turkish Statistical Institute, was utilized, and Bayesian Quantile Structural Equation Modeling and Bayesian Structural Equation Modeling methods were applied. The empirical study indicates that all life domains were found to have a positive impact on subjective well-being, except for housing-district satisfaction and education satisfaction. In particular, for individuals with low subjective well-being, satisfaction with work life, relationships, and public services has a stronger effect on subjective well-being compared to those in higher quantiles. Conversely, marriage satisfaction has a stronger effect on subjective well-being among individuals in the higher quantiles. This study’s unique contribution is that examining subjective well-being according to quantile values provides detailed information on how the factors influencing individuals’ subjective well-being vary across different levels.

Diameter Increment Estimations of Open-Grown Stone Pine (Pinus pinea L.) Trees in Urban Parks in Istanbul, Türkiye

Open-grown trees in cities can improve environmental conditions by providing sustainable ecosystem services. Reliable data are necessary for assessing the functions of urban trees. The diameter at breast height (DBH), diameter increment, and annual ring measurements are the main parameters in the development of reliable models. To model periodic mean diameter increments calculated for different time periods (5, 10, 15, 20, and 25 years), a total of 43 open-grown stone pines (Pinus pinea L.) of different diameter classes were sampled in several urban parks in Istanbul, Türkiye. The DBH was measured, and increment cores were extracted from each tree at 1.30 m stem height using an increment borer. Tree age at breast height was determined by counting annual rings, and periodic mean diameter increments were calculated for different periods based on the measured tree-ring widths. The periodic mean increments of different periods were related to the inside-bark diameter at breast height and tree age. Since there was no significant relationship between tree age and periodic mean increments for each period’s length, as shown in the correlation analysis, models used to estimate the periodic mean increments of inside-bark DBH were developed using the least squares regression and quantile regression (QR) techniques. As the period length increased, the estimation success of the diameter increment models increased while the mean absolute percentage error (MAE) values decreased from 40 to 32%. The best model was the one used for the last 25-year period with the quantile value q = 0.50 which estimated the diameter increment with an RMSE = 1.391 mm/year and MAE = 32.27%.

Sensitivity and Uncertainty Analysis of the GeeSEBAL Model Using High-Resolution Remote-Sensing Data and Global Flux Site Data

Actual evapotranspiration (ETa) is an important component of the surface water cycle. The geeSEBAL model is increasingly being used to estimate ETa using high-resolution remote-sensing data (Landsat 4/5/7/8). However, due to surface heterogeneity, there is significant uncertainty. By optimizing the quantile values of the reverse-modelling automatic calibration algorithm (CIMEC) endpoint-component selection algorithm under extreme conditions through 212 global flux sites, we obtained the optimized quantile values of 11 vegetation types of cold- and hot-pixel endpoint components (Ts and NDVI). Based on the observation data of the global FLUXNET tower, the sensitivity of 20 parameters in the improved geeSEBAL model was determined through Sobol’s sensitivity analysis. Among them, the parameters dT and SAVI,hot were confirmed as the most sensitive parameters of the algorithm. Subsequently, we used the differential evolution Markov chain (DE-MC) method to analyse the uncertainty of the parameters in the geeSEBAL model used the posterior distribution of the parameters to modify the sensitive parameter values or ranges in the improved geeSEBAL model and to simulate the daily ETa. The results indicate that by analysing the end element components of the geeSEBAL model (Ts and NDVI), quantile numerical optimization and parameter optimization can be performed. Compared with the original algorithm, the improved geeSEBAL model has significantly improved simulation performance, as shown by higher R2 values, higher NSE values, smaller bias values, and lower RMSE values. The most suitable values of the predefined parameter Zoh were determined, and the reanalysis of meteorological data inputs (relative humidity (RH), temperature (T), wind speed (WS), and net radiation (Rn)) was also found to be an important source of uncertainty for the accurate estimation of ETa. This study indicates that optimizing the quantiles and key parameters of the model end component has certain potential for further improving the accuracy of the geeSEBAL model based on high-resolution remote-sensing data in estimating the ETa for various vegetation types.

Long–period trends in water temperature changes in the northern part of the Atlantic Ocean from ocean reanalysis data

The results of assessing long–period changes in water temperature in the North Atlantic Ocean (0°–70° N, 8°–80° W) based on data from ocean reanalyses and objective analyses for the periods 1961– 2011 and 1980–2011 are presented. The obtained estimates are based on the application of a nonparametric method of regression analysis (quantile regression) to the monthly ocean temperature for a quantile value of 0.5. During the period 1961–2011 warming was mainly observed in the upper 400 m layer in the region from the equator to 70° N. Over this 51-year period, the increase in the median monthly ocean temperature averaged over the analyzed water area ~0.5°C, and in the Gulf Stream–North Atlantic Current system ~1°C. During the period 1980–2011 warming in the North Atlantic Ocean mainly occurred in the upper 1 km layer at high latitudes (50°–65° N). Over this 32-year period, the increase in the median monthly ocean temperature in the subpolar gyre in the upper 400 m layer was ~1°C.

Modeling of mRNA deadenylation rates reveal a complex relationship between mRNA deadenylation and decay

Complete cytoplasmic polyadenosine tail (polyA-tail) deadenylation is thought to be essential for initiating mRNA decapping and subsequent degradation. To investigate this prevalent model, we conducted direct RNA sequencing of S. cerevisiae mRNAs derived from chase experiments under steady-state and stress condition. Subsequently, we developed a numerical model based on a modified gamma distribution function, which estimated the transcriptomic deadenylation rate at 10 A/min. A simplified independent method, based on the delineation of quantile polyA-tail values, showed a correlation between the decay and deadenylation rates of individual mRNAs, which appeared consistent within functional transcript groups and associated with codon optimality. Notably, these rates varied during the stress response. Detailed analysis of ribosomal protein-coding mRNAs (RPG mRNAs), constituting 40% of the transcriptome, singled out this transcript group. While deadenylation and decay of RPG mRNAs accelerated under heat stress, their degradation could proceed even when deadenylation was blocked, depending entirely on ongoing nuclear export. Our findings support the general primary function of deadenylation in dictating the onset of decapping, while also demonstrating complex relations between these processes.

Data-Driven State of Health Interval Prediction for Lithium-Ion Batteries

The accurate prediction of the state of health (SOH) for lithium-ion batteries is a key factor for improving the performance of battery management systems (BMS). However, traditional point prediction methods are difficult to effectively eliminate errors due to the uncertainty of variables and application environments. This paper presents a model for predicting the interval of lithium-ion batteries based on health indicators (HIs) during charging, which addresses the limitations of current point prediction in practical applications. First, twelve HIs are extracted from the current and voltage variables of the charging process. Secondly, feature selection is performed by random forest (RF) training, and the selected HIs are dimensioned using partial least squares (PLS). Finally, a long short-term memory network (LSTM) combined with quantile regression (QR) is used to derive the quantile values of the prediction points and each quantile is employed as input information for Gaussian kernel density estimation (KDE) to obtain the SOH probability density distribution. The experimental results based on the NASA PCOE Li-ion battery dataset and CALCE Li-ion battery dataset show that the SOH interval coverage is more than 90% and the average width of the interval is less than 0.294.

Bankruptcy prediction indicators approach: A tool for measuring steinhoff’s risk

Research aims: The objective of this article is to ascertain whether Steinhoff International Holdings could have successfully employed the Bankruptcy Prediction Indicators Approach model as an instrument for measuring business continuity risk prior to its significant share price fall in 2012.Design/Methodology/Approach: The study utilized a qualitative research design and document analysis approach to examine Steinhoff International Holdings' financial data from 2012-2021, using the Integrated Real-time Equity System (IRESS BFA McGregor) and the Bankruptcy Prediction Indicators Approach. In MS Excel, quantile values were determined by dividing the probability distribution into equal segments.Research findings: The findings of the analysis revealed that the Bankruptcy Prediction Indicators Approach was used to identify Steinhoff International Holdings' business continuity risk in 2012, demonstrating its usefulness in measuring risk and providing investigative tools for management amid COVID-19 challenges. It has been determined that ROE is the primary indicator with the highest predictive power of all the indicators.Theoretical contribution/Originality: This article adds to the body of knowledge on business continuity risk, and practitioners can use the findings to incorporate the Bankruptcy Prediction Indicators Approach into their annual reporting.Research limitation/Implication: The limitation consists of the sample from a single retail company listed on the Johannesburg Stock Exchange, and the published annual reports were the primary source of information for the analysis and measurement.

Robust Multiplicative Scatter Correction Using Quantile Regression

ABSTRACTA robust method for multiplicative scatter correction (MSC) in infrared spectroscopy is presented. Using quantile regression, the outlier wavelengths (concentration‐dependent wavelengths) that are irrelevant to the regression are identified and therefore excluded from the regression model. This new MCS method, which could be implemented in its simple or extended form, is much simpler than the recently proposed methods and has only one hyperparameter (the quantile value) to be adjusted. To achieve this, a scoring function based on residual analysis can automatically determine the correct quantile value. The method is first explained using simulation data sets and then its validation is explained by analysing some experimental data sets. It was found that our new method can perform well in the presence of strong outlying variables. On the other hand, when the data sets are not associated outlying wavelengths, this method behaves similarly to the conventional MSC method.

Aggregate Loss Models to Calculate Risk Measures

The concept of aggregate loss models pertains to a stochastic variable representing the total sum of all losses encountered within a set of insurance policies. In the non-life insurance sector, it is employed to assess the potential losses that an insurance company may face when claims made by policyholders exceed the allocated claim reserves. The purpose of studying aggregate loss models is to ascertain risk measures such as standard deviation of premium principles, value at risk (VaR), and conditional tail expectation (CTE). These steps aid insurance companies in the management and quantification of risks associated with aggregate losses. The standard deviation of premium principles is calculated analytically by substituting expected values and variances, while VaR is estimated using the Monte Carlo method to determine quantile values and confidence intervals. CTE is evaluated by computing the average losses that surpass the VaR threshold. These distributions and parameters require the Pareto distribution, which characterizes claim sizes, and the Poisson or Negative Binomial distribution, which factors in the number of claims. It is crucial to carefully consider the selection of the appropriate distribution, as it plays a significant role in determining the accuracy and reliability of the model. Furthermore, other influencing factors, such as loading factors and confidence intervals, should also be taken into account. These factors have the potential to significantly impact the quantification of risk arising from the model.

The Effect of Maternal Bariatric Surgery on Offspring Anthropometry: A Mixed Cohort.

Although bariatric surgery is associated with a decrease in obesity complications, it may affect the children's growth by a maternal nutritional deficiency. This study was conducted to assess the effect of maternal bariatric surgery on offspring anthropometry. In a mixed cohort, anthropometric status of children aged 5years or less born to mothers with a history of bariatric surgery was compared with a control group consists of peers born to mothers with obesity but without bariatric surgery. Anthropometric indices including crude and quantile values for BMI, weight, height, and head circumference at birth and the first 5years of life were measured. Then, the adjusted effect of maternal surgical history on anthropometric status was estimated by linear regression. From a total of 56 children, 28 born to mothers with a history of bariatric surgery, and 28 born to mothers with obesity but without bariatric surgery. At birth, weight (2915 vs 3225g) and BMI (11.72 vs 12.94kg/m2) were lower in the group with maternal bariatric surgery than in the control group (P = 0.02 and P = 0.03, respectively), although after regression adjustment, there was only a significant difference between the two groups in height for age value in children less than 5years old (B = 0.872, P-value = 0.001). Bariatric surgery with decrease of weight indices at birth has a probable influence on growth and development in next years. Therefore, it is recommended further studies to identify unknown effect of types of preconception surgical procedures on childhood outcomes.

MCS-based quantile value approach for reliability-based design of tunnel face support pressure

This paper develops a new approach for reliability-based design (RBD) of tunnel face support pressure from a quantile value perspective. A surrogate model is constructed to calculate the collapse pressures of the random samples generated by a single run of Monte Carlo simulation (MCS). The cumulative distribution function (CDF) of the collapse pressure is then obtained and the support pressure aiming at a target failure probability is chosen as the upper quantile value of the collapse pressures. The proposed approach does not require repetitive reliability analyses compared to the existing methods. Moreover, a direct relationship between the target failure probability and the required support pressure is established. An illustrative example is used to demonstrate the implementation procedure. The accuracy of the reliability-based support pressures is verified by direct MCS incorporating with three-dimensional numerical simulations. Finally, the influencing factors, including the sample size of MCS, the correlation coefficient between random variables, the choice of experimental points, and the surrogate model, are investigated. This method can play a complementary role to available approaches due to its advantages of simplicity and efficiency.

PHF19 before and post induction treatment possess favorable potency of reflecting treatment response to protease inhibitors, event-free survival, and overall survival in multiple myeloma patients

ABSTRACT Objective Plant homeodomain finger protein 19 (PHF19) regulates hematopoietic stem cell differentiation and promotes multiple myeloma (MM) progression. This study intended to explore the potency of PHF19 at baseline and post induction treatment in estimating treatment response to protease inhibitors and survival in MM patients. Methods This retrospective study screened 69 MM patients who received protease inhibitors with bone marrow (BM) samples available at both baseline and post induction treatment. Twenty healthy BM donors were included as healthy controls (HCs). PHF19 in plasma cells from BM was quantified by reverse transcription-quantitative polymerase chain reaction. Results PHF19 at baseline and post induction treatment in MM patients were increased than in HCs. In MM patients, PHF19 was declined post induction treatment. Elevated PHF19 at baseline and post induction treatment were correlated with renal impairment, beta-2-microglobulin ≥5.5 mg/L, t (4; 14), higher international staging system (ISS) stage, and higher revised ISS (R-ISS) stage. Concerning treatment response, PHF19 at baseline and post induction treatment were negatively associated with complete response and overall response rate. Notably, abnormal PHF19 (above 95% quantile value of PHF19 in HCs) at baseline and post induction treatment were linked with shortened event-free survival (EFS) and overall survival (OS). After adjustment, abnormal PHF19 post induction treatment was independently related to shortened EFS (hazard ratio = 2.474) and OS (hazard ratio = 3.124). Conclusion PHF19 is aberrantly high and declines post induction therapy, which simultaneously reflects unfavorable treatment response to protease inhibitors as well as shorter EFS and OS in MM patients.

Can ETFs affect U.S. financial stability? A quantile cointegration analysis

This study evaluates whether exchange traded funds (ETFs) threaten financial market stability by testing two hypotheses relating the growing importance of ETFs to increased market volatility and rising equity valuations. We estimate quantile cointegration models using Standard & Poor's 500 Index (S&P 500) and Chicago Board Options Exchange volatility Index (VIX) data for 1994–2020. We found that an increase in ETFs is positively and significantly related to the long-term valuation of the S&P 500 for quantile values above the median. By contrast, ETFs have only a negative and significant effect on the VIX for quantiles around the median. Ultimately, two novel results were obtained. First, the distortion in the value of the S&P 500 relative to its fundamentals is driven by investor flow into ETFs during a bull market. Second, the impact of equity ETFs on the VIX is only affected when fundamental factors are in play, decreasing it. Therefore, ETFs contribute to forming equity bubbles and support valuation market dynamics. Both regulators and policymakers should consider these conclusions.

African stock markets’ connectedness: Quantile VAR approach

The present paper investigates African stock markets’ linkages by considering stocks in the continent’s largest economies, specifically Egypt, Kenya, Morocco, Nigeria, South Africa, and Tunisia. Using a dataset that spanned November 25, 2008, to September 18, 2023, the quantile connectedness approach of Chatziantoniou et al. (2021) is employed, and the results unfold these interesting dynamics of African market connectivity: (i) In the bearish market phase, South African stock dominated the entire network, transmitting shocks to the remaining stocks, while Moroccan and Kenyan stocks played similar role mildly. (ii) In the bullish market phase, Nigerian stock dominated the market as a major net transmitter of shock supported by South African and Kenyan stock markets. (iii), The Egyptian and Tunis stock markets are net shock receivers in both the bear and bull market phases. (iv), At the median quantile value, stocks become less riskier and the Kenyan stock market becomes the most vulnerable while Nigerian, Egyptian, and South African stock markets are influenced by other stock markets when markets are calm. (v), Though, African stocks are underperforming, interested portfolio managers will learn from the trading strategies to be adopted to maximize their returns. These findings will benefit portfolio managers, international stakeholders, and regulators.

Detecting Respiratory Viruses Using a Portable NIR Spectrometer-A Preliminary Exploration with a Data Driven Approach.

Respiratory viruses' detection is vitally important in coping with pandemics such as COVID-19. Conventional methods typically require laboratory-based, high-cost equipment. An emerging alternative method is Near-Infrared (NIR) spectroscopy, especially a portable one of the type that has the benefits of low cost, portability, rapidity, ease of use, and mass deployability in both clinical and field settings. One obstacle to its effective application lies in its common limitations, which include relatively low specificity and general quality. Characteristically, the spectra curves show an interweaving feature for the virus-present and virus-absent samples. This then provokes the idea of using machine learning methods to overcome the difficulty. While a subsequent obstacle coincides with the fact that a direct deployment of the machine learning approaches leads to inadequate accuracy of the modelling results. This paper presents a data-driven study on the detection of two common respiratory viruses, the respiratory syncytial virus (RSV) and the Sendai virus (SEV), using a portable NIR spectrometer supported by a machine learning solution enhanced by an algorithm of variable selection via the Variable Importance in Projection (VIP) scores and its Quantile value, along with variable truncation processing, to overcome the obstacles to a certain extent. We conducted extensive experiments with the aid of the specifically developed algorithm of variable selection, using a total of four datasets, achieving classification accuracy of: (1) 0.88, 0.94, and 0.93 for RSV, SEV, and RSV + SEV, respectively, averaged over multiple runs, for the neural network modelling of taking in turn 3 sessions of data for training and the remaining one session of an 'unknown' dataset for testing. (2) the average accuracy of 0.94 (RSV), 0.97 (SEV), and 0.97 (RSV + SEV) for model validation and 0.90 (RSV), 0.93 (SEV), and 0.91 (RSV + SEV) for model testing, using two of the datasets for model training, one for model validation and the other for model testing. These results demonstrate the feasibility of using portable NIR spectroscopy coupled with machine learning to detect respiratory viruses with good accuracy, and the approach could be a viable solution for population screening.

An efficient approach for safety factor-based and reliability-based design of tunnel face support pressure using BP Neural Network

ABSTRACT This paper develops an efficient approach for safety factor-based design and reliability-based design of tunnel face support pressure by constructing a surrogate model to predict limit support pressures. The Back Propagation Neural Network is utilised to fit a surrogate model based on a set of training samples with limit support pressures computed by a proposed numerical method. Several scenarios are used to demonstrate the applications after the prediction performance of the model has been evaluated. A safety factor-based design is implemented by predicting the limit support pressure of a scenario with strength parameters divided by the target safety factor from those of the original scenario, whereas a reliability-based design is performed by predicting the limit support pressures of a certain number of Monte Carlo simulation samples and then choosing a quantile value from the sorted limit support pressures. The design results are checked by direct numerical simulations, demonstrating that the model is valid for conventional scenarios within the specified design domain. The approach is convenient and efficient for applicable scenarios because the most complex and time-consuming process of constructing a surrogate model is no longer needed.

VaR calculation by binary response models

<p>The original Risk-Metrics method is underpinned by the assumption that daily asset returns are conditional Gaussian independently identically distributed (iid) random variables with a mean of zero. In this paper, a new method to calculate Value at Risk (VaR) was suggested to overcome the shortcoming of Risk-Metrics by employing binary response models to compute probability forecasts of the portfolio return by exceeding a grid of candidate quantile values. From those values, the VaR quantile value was selected. The proposed model was called BRV (Binary Response VaR method). Consistent application of BRV to the Dow Jones Industrial Average (INDEXDJX: DJI) and Dow Jones U.S. Marine Transportation Index (DJUSMT) time series proved that it was more accurate than the Risk-Metric system. This method not only worked similar to quantile regression but had the advantage that conventional maximum likelihood methods could be used for parameter estimation and inference. The BRV method was the best performing method for computing the daily VaR at both the 95% and 99% confidence levels over the period 02/01/06–31/12/08. The BRV and the QR (quantile regression) methods performed similarly, but the BRV method had the practical advantage that conventional maximum likelihood (ML) technique could be used for parameter estimation and robust inference.</p>

Classifying heterogeneous urban form into local climate zones using supervised learning and greedy clustering incorporating Landsat dataset

The concept of Local climate zone (LCZ) is used to generate LCZ maps worldwide as a part of World Urban Database and Access Portal Tools (WUDAPT) project utilizing Landsat imagery. Though WUDAPT has become the standard for LCZ mapping, it has certain limitations: 1) its outcome for heterogeneous cities is less accurate. 2) Due to fine granularity and huge-sized imagery, validating WUDAPT outcome becomes difficult for an urban expert. To overcome these, we have presented the LCZ classification and greedy clustering framework for heterogeneous cities (Nagpur (India)). Three classification schemes based on random forest, deep neural network, 1D-convolutional neural network and, subset ensemble are proposed. The overall accuracies are respectively 88%, 86%, 82%, 88%. Contextual information (mean, minimum, maximum, median, 25th, and 75th quantile values) at 90 m resolution is adopted while classification to handle spatial heterogeneity. Adding contextual information increases the overall accuracy by 4–6%, urban and mixed classes' accuracy by 4–7% and, 3–4% respectively. Further, clustering groups the spatially connected but similar pixels (classified Landsat) using breadth-first search concept and deque data structure creating LCZ map. Such LCZ map is helpful for an urban expert to validate correctness, select suitable level of granularity, visualize heterogeneity and suggest improvements.

Updating the prior parameters of concrete compressive strength through Bayesian statistics for structural reliability assessment

The stochastic model of concrete compressive strength is a critical aspect for the reliability-based design of new concrete structures. The distribution scatter of this variable can compromise the quality of structural reliability assessments. The existing stochastic models in Probabilistic Model Code – a reference document for structural engineering practitioners published by the Joint Committee on Structural Safety – are based on previous investigations conducted during the 1980’s, which might be outdated. This paper investigates the validity of these models by using an extensive database of measured strength values of ready-mixed concretes by means of Bayesian updating methods with the use of prior information. Furthermore, a mathematical procedure is proposed to update the stochastic models when new strength values are available. This investigation confirms that the stochastic model for the most common strength classes can be ameliorated. For most classes, the revision resulted in larger 5% quantile values and lower mean values than the ones from those early studies, which could be an indication of a more precise concrete production. Additionally, the results suggest that the updating might be sensitive to the sample configuration. Finally, recommendations are given to improve the newly-derived models by means of additional strength values.

Future Joint Probability Characteristics of Extreme Precipitation in the Yellow River Basin

The relationship between climate change and extreme precipitation is extremely complex. From a probabilistic perspective, a proper understanding of the response of extreme precipitation to climate change is of significant importance. This study was based on daily precipitation provided by CMIP6 climate models and employed copula functions to construct joint distributions of precipitation amount and precipitation intensity indices at different quantile levels. A spatial–temporal assessment of the susceptibility areas for extreme precipitation in the Yellow River Basin was conducted while considering bivariate return periods and design values. The results indicate that there were significant spatial differences in the bivariate return periods. Taking the R90P-SDII (90) index for a 20a return period as an example, the difference between the maximum and minimum joint return periods within the Yellow River Basin was 1.4 times, while the co-occurring return period was 7.0 times, and the Kendall return period was 4 times. Moreover, this difference increased with the increase in the return period. The magnitude order of the four return periods is as follows: TAnd > TKendall > TSingle-variable > TOr. Joint return periods (Or) and co-occurring return periods (And) could be considered as the extreme cases under single-variable return periods, serving as an estimation interval for actual return periods. Under the influence of climate change, the bivariate design values for future periods exhibited a variability increase of 6.76–28.8% compared to historical periods, and this increase grew with higher radiative forcing scenarios, ranking as SSP126 < SSP245 < SSP585. The bivariate design values showed a noticeable difference in variability compared to the single-variable design values, ranging from −0.79% to 18.67%. This difference increased with higher quantile values, with R95P-SDII (95) > R90P-SDII (90) > PRCPTOT-SDII.