Absolute Deviation Method Research Articles

Optimizing Prognostic Predictions in Liver Cancer with Machine Learning and Survival Analysis.

This study harnesses RNA sequencing data from the Cancer Genome Atlas to unearth pivotal genetic markers linked to the progression of liver hepatocellular carcinoma (LIHC), a major contributor to cancer-related deaths worldwide, characterized by a dire prognosis and limited treatment avenues. We employ advanced feature selection techniques, including sure independence screening (SIS) combined with the least absolute shrinkage and selection operator (Lasso), smoothly clipped absolute deviation (SCAD), information gain (IG), and permutation variable importance (VIMP) methods, to effectively navigate the challenges posed by ultra-high-dimensional data. Through these methods, we identify critical genes like MED8 as significant markers for LIHC. These markers are further analyzed using advanced survival analysis models, including the Cox proportional hazards model, survival tree, and random survival forests. Our findings reveal that SIS-Lasso demonstrates strong predictive accuracy, particularly in combination with the Cox proportional hazards model. However, when coupled with the random survival forests method, the SIS-VIMP approach achieves the highest overall performance. This comprehensive approach not only enhances the prediction of LIHC outcomes but also provides valuable insights into the genetic mechanisms underlying the disease, thereby paving the way for personalized treatment strategies and advancing the field of cancer genomics.

ANALYSIS OF OPTIMAL PORTFOLIO FORMATION ON IDX30 INDEXED STOCK WITH THE MEAN ABSOLUTE DEVIATION METHOD

In investing in stocks, an investor must be able to form a stock portfolio to obtain optimal results. Factor analysis is one way to select stocks to form a portfolio. Factor analysis with Principal Component Analysis (PCA) extraction is used to summarize many variables into new smaller factors by producing the same information. The new factor formed is called a portfolio. This study aims to form an optimal portfolio using the Mean Absolute Deviation (MAD) method, which is an alternative to Markowitz optimization, and assess the stock portfolio's performance using the Sharpe index. This research uses IDX30-indexed stocks because the stocks in this index have high market capitalization and liquidity. The data used in this study are daily close stock price data on the IDX30 index from September 20, 2022, to September 20, 2023. The data used is secondary data obtained from the official website https://finance.yahoo.com/. From the analysis, three stock portfolios were obtained. With MAD optimization, the investment weight of each stock is obtained namely, in the first portfolio, the investment weight of AMRT shares is 21.95%, BBCA shares are 30%, BBNI shares are 18.05%, and BBRI shares are 30%. In the second portfolio, the investment weight of AKRA shares is 34.03%, BRPT shares are 40%, and MEDC shares are 25.97%. In the third portfolio, the investment weight of BMRI shares is 50%, and INDF shares are 50%. By measuring the performance of the Sharpe index, the optimal portfolio is obtained in the second portfolio with an expected return portfolio of 0.155% and a portfolio risk of 1.927%.

Herding towards pygmalion: Examining the cultural dimension of market and bank based systems

The study aims to not only detect the presence of herd behavior in the countries studied, but also to examine the effect of cultural dimensions and market/bank-based systems on the herding behavior of financial market investors. The study employs the Cross-Sectional Standard Deviation and Cross-Sectional Absolute Deviation methods to analyze daily data from public companies traded in the capital markets in Emerging Seven and Group of Seven economies. The results suggest that being a member of E7-G7, a Future Oriented (FO), and a Performance Oriented (PO) cultures are the most important factors in explaining herd behavior. Additionally, the study found that the Ridge Classifier and CatBoost Classifier algorithms arethe most superior model for estimating herd behavior periods determined by the CSSD and CASD models, respectively. The feature selection results show that the Assertiveness (A) in-group collectivism (GC) are the three most important explanatory factors of the herd behavior.

Evaluating the Characteristics of Spatial Variability of Soil in Vertical Direction Highly Heterogeneous Region Based on Cone Penetration Test

Abstract Key parameters describing the spatial variability of soil properties based on the random field theory are the scale of fluctuation (SOF) and coefficient of variation (COV). To characterize the spatial variability of soil properties, reducing the impact of these errors and uncertainties is necessary. To accomplish this, for the five main layers of soil, we collected 18 cone penetration test (CPT) data from a highly heterogeneous region in Lianyungang New Airport, Jiangsu Province, China, and used the control variable method to analyze the influence of the estimation method of tendency, its function type and outliers. The results show that, compared with the ordinary least square method (OLSM), the least absolute deviation method (LADM) can more truly reflect the trend component of CPT parameters in the vertical direction, and the influence of other factors on SOF and COV is also studied, such as outliers and estimation functions of trend components. On this basis, a reasonable calculation process of SOF and COV is summarized, which provides a reference for the calculation of SOF and COV in vertical direction in the future. By comparing the SOF calculated by different models, the results show that the squared exponential (SQX) model has the highest SOF in 68.3% of the evaluation, and the single exponential (SNX) model has the lowest SOF in 64.4% of the evaluation. Moreover, we compared the SOF and COV of cone tip resistance (qc) and sleeve friction (fs), which showed that SOF of qc and COV of qc is lower than that of fs in 54.4 and 73.3% of all evaluations, respectively.

Descent along nodal straight lines and simplex algorithm: two variants of regression analysis based on the least absolute deviation method

A comparative analysis of the computational complexity of exact algorithms for estimating linear regression equations was conducted using the least absolute deviation method. The goal of the study is to compare the computational efficiency of exact algorithms for descent along nodal lines and algorithms based on solving linear programming problems. For this purpose, the algorithm of gradient descent along nodal lines and algorithms for solving the equivalent primal and dual linear programming problems using the simplex method were considered. The computational complexity of algorithms for implementing the method of least modules in solving direct and dual linear programming problems was estimated. A comparison between the average time for determining the regression coefficients using the primal and dual linear programming problems and the average time for gradient descent along nodal lines was conducted using the Monte Carlo method of statistical experiments. It is shown that both options are significantly inferior behind gradient descent along nodal lines, both in terms of the computational complexity of the algorithms and in terms of computation time, and this advantage increases with the sample size, reaching hundred times or more.

Using Regularized Methods to Validate Q-Matrix in Cognitive Diagnostic Assessment

One of the central components of cognitive diagnostic assessment is the Q-matrix, which is an essential loading indicator matrix and is typically constructed by subject matter experts. Nonetheless, to a large extent, the construction of Q-matrix remains a subjective process and might lead to misspecifications. Many researchers have recognized the importance of estimating or validating the Q-matrix, but most of them focus on the conditions of relatively large sample sizes. This article aims to explore Q-matrix validation possibilities under small sample conditions and uses regularized methods to validate the Q-matrix based on the compensatory reparametrized unified model and generalized deterministic inputs, noisy “and” gate models. Simulation studies were conducted to evaluate the viability of the modified least absolute shrinkage and selection operator (Lasso) and modified smoothly clipped absolute deviation (SCAD) methods, comparing them with existing methods. Results show that the modified Lasso and the modified SCAD methods outperform the stepwise, Hull, and MLR-B methods in general, especially under the conditions of small sample sizes. While good recovery in all small sample size conditions is not guaranteed, the modified methods demonstrate advantages across various item quality conditions. Also, a real data set is analyzed to illustrate the application of the modified methods.

A novel method for multiple targets localization based on normalized cross-correlation adaptive variable step-size dynamic template matching

The template matching method has been widely utilized in the defect detection of wafer surfaces. However, the traditional matching approaches are limited by illumination, noise, and deformation, which cannot meet the requirements of accuracy and robustness. In this paper, a novel multiple targets localization method, named Normalized Cross-correlation Adaptive Variable Step-Size Dynamic Template (NCC-AVSSDT) matching, is proposed to improve the accuracy and efficiency of image localization, which combines the advantages of NCC and AVSSDT. The AVSSDT method is utilized to dynamically adjust the scanning step size based on the NCC matching coefficients. This approach optimizes the scanning process, accelerating convergence toward the optimal matching position. Experimental results verify the accuracy and robustness of the proposed method under different conditions, especially when dealing with rotational variations and variations in noise textures. Therefore, NCC-AVSSDT can be used to perform multiple targets localization of chip image in nearly real-time. Three experiment types were used for comprehensive evaluations, including multiple targets, noise, and rotation angles. Experimental results show that NCC-AVSSDT is much better than the sequential similarity detection algorithm and mean absolute deviation methods in terms of multiple targets (0.667 vs 0.811 s, 0.832 s) and success rate (100% vs 35%, 20%).

Construction of ellipsoid convex model of bounded uncertainties with outlier detection for application in non-probabilistic topology optimization

The ellipsoid convex model can be suitably used for structural analysis and design optimization under uncertain-but-bounded parameters and loads. Such a model can be constructed using measured samples of the uncertainties. However, the presence of outliers is often unavoidable due to system fluctuations in the measurements. Thus, it is necessary to detect any outliers among the samples before constructing the uncertainty model to prevent over-conservativeness. To this end, the present paper proposes a rational approach for constructing ellipsoid convex models with outlier detection. The concept of the local outlier factor (LOF) is utilized, in conjunction with the k-nearest natural neighbor to adaptively determine the neighborhood range. Then the outliers are detected using the scaled median absolute deviation method, based on the LOF values obtained. Finally, the minimum-volume ellipsoid convex model is constructed with a mathematically strict and efficient semi-definite programming formulation using the normal samples. The validity of the proposed approach is demonstrated with numerical examples. The application of the constructed uncertainty model in non-probabilistic robust topology optimization is demonstrated, and the results show the effectiveness of the proposed approach.

A new three-dimensional rock strength criterion based on shape function in deviatoric plane

Rock strength criteria are the theoretical grounding of geotechnical design and stability estimation, the Mohr–Coulomb (MC) and Hoek–Brown (HB) criteria are the widely accepted criteria at present, due to their reasonability and unambiguous concept, however they overlook the effect of intermediate principal stress, and contain six singular corners in π plane. Aimed at overcoming those limitations, the MC and normal parabolic criterion (NPC) were improved to their 3D versions that lead to smooth and convex for a wide range of strength parameters. The extended 3D strength criteria coincide with corresponding original forms in the triaxial compression and triaxial extension states, which not only take intermediate principal stress into account, but also provide great convenient in numerical calculation. Multigroup of poly-axial strength datasets gathered from the references are used to check the prediction accuracy of the proposed 3D criteria by the least absolute deviation method. Research proved that the 3D NPC criterion has a relatively larger deviation on poly-axial strength data prediction, but the proposed 3D MC criterion can describe peak strength with low misfit for soft or hard rocks. Peak strength σ1 increases first and then decreases with the increase of σ2, whether increasing or decreasing σ2, both will result in rock failure. Moreover, the 3D MC can fit the poly-axial strength data well for lower or higher values of σ3, which strongly suggests the proposed 3D MC criterion is adequate. Applicability of the proposed strength criterion will be discussed in further research.

The LAD estimation of UMAR model with imprecise observations

Uncertain time series analysis is a method of predicting future values by analyzing imprecise observations. In this paper, the least absolute deviation (LAD) method is applied to solve for the unknown parameters of the uncertain max-autoregressive (UMAR) model. The predicted value and confidence interval of the future data are calculated using the fitted UMAR model. Moreover, the relative change rate of parameter is proposed to test the robustness of different estimation methods. Then, two comparative analyses demonstrate the LAD estimation can handle outliers better than the least squares (LS) estimation and the necessity of introducing the UMAR model. Finally, a numerical example displays the LAD estimation in detail to verify the effectiveness of the method. The LAD estimation is also applied to a collection of actual data with cereal yield.

The modification of Mohr-Coulomb criteria based on shape function and determination method of undetermined parameters

Rock strength criteria is a key step to estimate the stability of rock engineering, especially, the effect of intermediate principal stress on rock failure should be considered in formation at great depth. However, the mostly used Mohr-Coulomb criterion don't consider the effect of intermediate principal stress, besides, most rock mechanics laboratories do not have the ability to do true triaxial strength, the strength parameters in poly-axial strength criteria are difficult to be determined. In order to solve these problems, based on the least absolute deviation method, the square of the correlation coefficient, absolute difference and mean difference are adopted to verify the fitting effect of 10 strength criteria on 32 groups of true triaxial strength data. On this basis, the elliptical Lode angle shape function, hyperbolic Lode angle shape function, and the Lode angle shape function based on spatial slip plane are used to establish the modified MC strength criteria, which not only meets the requirements of smoothness and convexness, but also can be used widely in many geomaterials. Besides, strength parameters of the established criteria can be decided by conventional triaxial strength experiment, and the prediction accuracy of the modified MC strength criteria for the 32 groups of true triaxial strength data is better than or close to the lowest fitting error of the existing poly-axial strength criteria.

ANALYSIS OF THE EFFECTIVENESS OF THE BROKER CODE CLOSING POLICY IN RUNNING TRADE ON HERDING BEHAVIOR

The broker code closure policy in running trades carried out by the Indonesia Stock Exchange on December 6, 2021, aims to overcome herding behavior and make better price spreads in the Indonesian capital market. This study aims to determine the policy's success by examining the existence of herding behavior before and after the broker code closure policy is enforced using the Cross-Sectional Absolute Deviation method and by looking at price volatility during the research period. Based on the research that has been carried out, it is known that there is a herding behavior before the policy is implemented, and there is no herding behavior after the policy is enforced. This means that the broker code closure policy in running trades carried out by the Indonesia Stock Exchange has succeeded in overcoming the herding behavior contained in the Indonesian capital market but is still not effective in overcoming price spreads that occur in the Indonesian capital market because there is the market sentiment towards economic conditions that occur both nationally and globally.

Regression analysis of data based on the method of least absolute deviations in dynamic estimation problems

The use of regression analysis in dynamic problems of system estimation requires a high-speed algorithm of model parameter determination. Moreover, the original data may have stochastic heterogeneity which entails the necessity of the estimates of model parameters be resistant to various data anomalies. However, stable estimation methods, including the least absolute deviations method, are significantly inferior to the parametric ones. The goal of the study is to describe a computationally efficient algorithm for implementing the method of least absolute deviations for dynamic estimation of regression models and to study its capabilities for solving practical problems. This algorithm is based on descending along nodal lines. In this case, instead of the values of the objective function, its derivative in the direction of descent is considered. The computational complexity of the algorithm is also reduced due to the use of the solution of the problem at the previous step as a starting point and efficient updating of observations in the current data sample. The external performance of the proposed dynamic version of the algorithm of gradient descent along nodal lines has been compared with the static version and with the least squares method. It is shown that the dynamic version of the algorithm of gradient descent along the nodal lines make it possible to bring the speed close to that of the least squares method for common practical situations and to use the proposed version in dynamic estimation problems for a wide class of systems.

Risk assessment of ionizing radiation and radiological thresholds to compound an environmental baseline for the unconventional gas industry.

The exploration of unconventional hydrocarbons may be very effective in promoting economic development and confronting energy crisis around the world. However, the environmental risks associated with this practice might be an impediment if not adequately dimensioned. In this context, naturally occurring radioactive materials and ionizing radiation are sensitive aspects in the unconventional gas industry that may compromise the environmental sustainability of gas production and they should be properly monitored. This paper provides a radioecological assessment of the São Francisco Basin (Brazil) as part of an environmental baseline evaluation regarding the Brazilian potential for exploring its unconventional gas reserves. Eleven and thirteen samples of surface waters and groundwater were analyzed for gross alpha and beta using a gas flow proportional counter. A radiological background range was proposed using the ± 2 Median Absolute Deviation method. Using geoprocessing tools, the annual equivalent doses and lifetime cancer risk indexes were spatialized. Gross alpha and beta background thresholds in surface water ranged from 0.04-0.40Bq L-1 to 0.17-0.46Bq L-, respectively. Groundwater radiological background varies from 0.006-0.81Bq L-1 to 0.06-0.72Bq L-1 for gross alpha and beta, respectively. All environmental indexes are relatively higher in the south of the basin, probably a direct response to the local volcanic formations. Traçadal fault and local gas seepages might also influence the gross alpha and beta distribution. All samples have radiological indexes below the environmental thresholds, and should remain at acceptable levels with the development of the unconventional gas industry in Brazil.

Do Firm Value and Institutional Investors Affect on Herding Behavior?

This research aims to determine the effect of firm value and institutional investors on herding behavior. The newnes of this research to add an indicator that have never been used for herding measurement to firm value. The existence of previous research result on herding that have not been conclusive raise empirical gaps and population gaps that are worthy of re-study. The measurement of firm value is carried out using the Book to Market Ratio and Firm Size. The size of institutional investors uses the company's institutional ownership, and the measurement of herding behavior uses the Cross-Sectional Absolute Deviation (CSAD) method. The sample in this study are LQ45. The data analysis method used in this study was carried out with multiple regression analysis. The study results show that there is indication of the occurrence of Herding Behavior in LQ45 companies in sample. Firm Value and institutional investors do not influence Herding Behavior. Keywords: Herding Behavior, Firm Value, Institutional Investors, LQ45 Companies

COVID-19 pandemic and herd behavior: Evidence from a frontier market

This paper examines the presence of herd behavior in the Vietnamese stock market using the cross-sectional absolute deviation (CSAD) method and by applying quantile regression (QR). We detect herd behavior in the Vietnamese stock market from January 2016 to May 2022. Herd behavior is less pronounced for bullish markets, yet more prominent under other market conditions. Importantly, the paper provides insight into the herd phenomenon during COVID-19’s fourth wave outbreak in Vietnam. We discover that during the fourth wave outbreak, investors on the Hanoi Stock Exchange (HNX) do not engage in herding. However, herd behavior does manifest on the Ho Chi Minh Stock Exchange (HOSE) with falling stock prices engendering pessimistic herd selling. Knowledge of this empirical evidence of herd behavior in the Vietnamese stock market should prove useful to investors in determining the intrinsic value of stocks, and to policymakers wishing to enhance the efficiency of the equity market.

Analytic hierarchy process and data envelopment analysis: A match made in heaven

Analytic hierarchy process (AHP) and data envelopment analysis (DEA) are two popular decision science methods with many business, science, and engineering applications. AHP is a Multi-Attribute Decision-Making (MADM) method for prioritizing alternatives, and DEA is a non-parametric method for estimating production frontiers. Each method has known strengths and weaknesses, and the strengths in one method can overcome the weaknesses in the other. We study several strategies to diminish the weaknesses of DEA with the strengths of pairwise comparisons in AHP. We tackle the low discrimination power inherent to conventional DEA methods. AHP, with its pairwise comparison capability, has been consistently used to increase the discrimination power and accuracy in DEA. We propose and evaluate several new hybrid MADM-DEA models of different computational complexity and consistency, including combinations of the best-worst method (BWM) and its variants with DEA as well as a novel method composed of Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH) and DEA. We further develop a new technique for evaluating the similarity among multiple ranking results in MADM. The new simple but powerful technique is called Rank Absolute Deviation (RAD) and is inspired by the mean absolute deviation method. Several numerical examples and a real-world problem are used to demonstrate the applicability and efficacy of the new BWM-DEA, MACBETH-DEA, and RAD methods proposed in this study. We illustrate how less computationally demanding MADM-DEA techniques provide rankings that are highly correlated with the benchmark DEA-AHP and different consensus ranking models.

Analisis Herding Behavior di Bursa Efek Indonesia pada Tahun 2020

The aim of this study is to analyze herding behavior in the Indonesian capital market as an emerging market which has experienced high JCI volatility throughout 2020. The analysis of herding behavior takes place in three market periods: (a) throughout the 2020 period with 242 observation dates (233 after the outliers were removed), (b) ) bearish (down) conditions with 59 observation dates (51 after the outliers were removed), and (c) bullish (rising) conditions with 183 observation dates. The sample consisted of 36 companies taken from members of the LQ-45 index for the periods August 2019-January 2020, February 2020-July 2020, and August 2020-January 2021. Herding behavior was identified through regression analysis and the Cross-Sectional Absolute Deviation (CSAD) method. The results of the study did not show any herding behavior during the observation period, which means that investors make investment decisions by considering the available information.
 Keywords: Herding; Cross Sectional Absolute Deviation.

Water Level Change of Qinghai Lake from ICESat and ICESat-2 Laser Altimetry

Long-term satellite observations of the water levels of lakes are crucial to our understanding of lake hydrological basin systems. The Ice, Cloud, and Land Elevation satellite (ICESat) and ICESat-2 were employed to monitor the water level of Qinghai Lake in the hydrological basin. The median of absolute deviation (MAD) method was exploited to remove the outliers. The results confirmed that the MAD range of ICESat was from 0.0525 to 0.2470 m, and the range of σ was from 0.0778 to 0.3662 m; the MAD range of ICESat-2 was from 0.0291 to 0.0490 m, and the range of σ was from 0.0431 to 0.0726 m; ICESat-2 was less than that of ICESat. The reference ellipsoid and geoid transfer equations were applied to convert the water level to the World Geodetic System (WGS84) and Earth Gravitational Model 2008 (EGM2008) geoid. The water level, as derived from laser altimeters, was validated by the Xiashe Hydrological Station; with ICESat, the coefficient of association (R) was 0.8419, the root mean square error (RMSE) was 0.1449 m, and the mean absolute error (MAE) was 0.1144 m; with ICESat-2, the R was 0.6917, the RMSE was 0.0531 m, and the MAE was 0.0647 m. The water levels from ICESat-2 are much more accurate than those from ICESat. The two combined laser altimeters showed that the R was 0.9931, the RMSE was 0.1309 m, and the MAE was 0.1035 m. The water level rise was 3.6584 m from 2004 to 2020. The rising rate was 0.2287 m/a. The collaborative use of the ICESat-2 and ICESat satellites made it easier to obtain the lake water levels.

Ridge regression coupled with a new uninformative variable elimination algorithm as a new descriptor screening method: Application of data reduction in QSAR study of some sulfonated derivatives as c-Met inhibitors

An uninformative variable elimination algorithm was combined with the Ridge regression method. This combination makes the penalized Ridge method, which is essentially incapable of deleting or selecting a variable, an efficient descriptor screening method for screening of descriptors in QSAR studies. The importance of descriptors was used to identify uninformative and redundant descriptors. The descriptor importance was defined as the ratio of the mean coefficients to the standard deviation of the coefficients derived from the leave-one-out (LOO) Ridge models. Then, with an iterative algorithm, among 392 molecular descriptors obtained after initial preprocessing, 358 uninformative descriptors were identified and removed from the data set. The remaining 34 descriptors were used for further variable selection and modeling through the smoothly clipped absolute deviation (SCAD) method. The proposed screening method was used to screen and reduce the size of QSAR data containing 63 compounds as c-Met inhibitors with anti-cancer activity. For comparison, the SCAD models were constructed using original high-dimensional data (SCAD), and the reduced-size data via the UVE-Ridge screening method (UVE-Ridge-SCAD). Both models were used to predict biological activities (pIC50) for compounds in the external test set. The coefficients of determination (R2) and the FIT parameter for the test data predicted using the SCAD model (with 12 selected descriptors) were equal to 0.8042 and 3.78, respectively. The UVE-Ridge-SCAD model (with 9 descriptors) showed R2 and FIT values of 0.9025 and 4.38, respectively. Higher R2 and FIT values indicate the excellent predictive power of the UVE-Ridge-SCAD model. Finally, the UVE-Ridge-SCAD model was used to suggest new high-potency compounds. The accuracy of the proposed compounds was investigated using ligand-receptor interactions.