Robust Regression Approach Research Articles

Corporate aging and changes in the pricing of stock characteristics

This study reveals that the pricing of several prominent stock and firm characteristics changes as firms age, based on a sample of common stocks in the US. It contributes to the literature by showing that interactions between stock characteristics, particularly firm age, are pricing relevant for the cross-section of stock returns. This finding emerges from a robust multivariate panel regression approach, which helps to address the multivariate challenge in cross-sectional asset pricing and controls for firm-fixed effects.

Data-driven estimation of noise variance stabilization parameters for low-dose x-ray images

Purpose. Denoising x-ray images corrupted by signal-dependent mixed noise is usually approached either by considering noise statistics directly or by using noise variance stabilization (NVS) techniques. An advantage of the latter is that the noise variance can be stabilized to a known constant throughout the image, facilitating the application of denoising algorithms designed for the removal of additive Gaussian noise. A well-performing NVS is the generalized Anscombe transform (GAT). To calculate the GAT, the system gain as well as the variance of electronic noise are required. Unfortunately, these parameters are difficult to predict from the x-ray tube settings in clinical practice, because the system gain observed at the detector depends on the beam hardening caused by the patient. Materials and Methods. We propose a data-driven method for estimating the parameters required to carry out an NVS using the GAT. It utilizes the energy compaction property of the discrete cosine transform to obtain the NVS parameters using a robust regression approach relying on a linear Poisson-Gaussian model. The method has been experimentally validated with respect to beam hardening as well as denoising performance for different dose and scatter levels. Results. Across a range of low-dose x-ray settings, the proposed robust regression approach has estimated both system gain and electronic noise level with an average error of only 4.2%. When used to perform a GAT followed by the denoising of low-dose x-ray images, performance gains of 5% for peak-signal-to-noise ratio and 4% for structural similarity index can be obtained. Conclusion. The parameters needed to calculate the GAT can be estimated efficiently and robustly using a data-driven approach. The improved parameter estimation method facilitates a more accurate GAT-based NVS and, hence, better denoising of low-dose x-ray images when algorithms designed for additive Gaussian noise are applied.

Short and long-term volatility transmission from oil to agricultural commodities – The robust quantile regression approach

This paper investigates permanent and transitory spillover effects from Brent oil futures to four agricultural futures – corn, wheat, soybean and canola. We construct permanent and transitory volatilities via component GARCH model, considering six different distribution functions. Created volatility time-series are embedded in the robust quantile regression framework. Transitory effect from oil market has slightly stronger influence on the agricultural commodities than its permanent counterpart, which is a sign that short-term information flow has more intense effect than fundamental factors. The results indicate that the best diversification instrument in combination with oil is soybean futures, since it is the least subject to oil volatility shocks.

A Modified Robust Support Vector Regression Approach for Data Containing High Leverage Points and Outliers in the Y-direction

The support vector regression (SVR) model is currently a very popular non-parametric method used for estimating linear and non-linear relationships between response and predictor variables. However, there is a possibility of selecting vertical outliers as support vectors that can unduly affect the estimates of regression. Outliers from abnormal data points may result in bad predictions. In addition, when both vertical outliers and high leverage points are present in the data, the problem is further complicated. In this paper, we introduced a modified robust SVR technique in the simultaneous presence of these two problems. Three types of SVR models, i.e. eps-regression (e-SVR), nu-regression (v-SVR) and bound constraint eps-regression (e-BSVR), with eight different kernel functions are integrated into the new proposed algorithm. Based on 10-fold cross-validation and some model performance measures, the best model with a suitable kernel function is selected. To make the selected model robust, we developed a new double SVR (DSVR) technique based on fixed parameters. This can be used to detect and reduce the weight of influential observations or anomalous points in the data set. The effectiveness of the proposed technique is verified by using a simulation study and some well-known contaminated data sets.

Impact of pricing mechanism on IPO oversubscription: evidence from Pakistan stock exchange

PurposeThe purpose of this paper is to examine the effects of pricing mechanism on initial public offerings (IPOs) oversubscription in Pakistan.Design/methodology/approachThis study used cross-sectional data to analyse 85 listed IPOs on the Pakistan stock exchange during the period of 2000-2017 to assess hypotheses related to influential determinants of IPO oversubscription. Accordingly, ordinary least square, robust regression and quantile regression approaches were applied in this study to evaluate the factors that influenced oversubscription.FindingsThe outcome displayed pricing mechanism is negatively significant with an oversubscription of IPOs. This indicates firms using the fixed-price mechanism signalled higher information asymmetry and uncertainty in their value. Thus, investors are aware that they will be offset with underpricing, and it is expected the demand will be higher for the particular IPOs.Research limitations/implicationsThis study is entirely focused on the available information of prospectus that should not be ignored by potential investors at the time of subscription of IPO. Therefore, the study contributes to extending the available literature in signalling theory whereby issuers should consider using the book-building pricing mechanism in enhancing the efficiency of the IPO offer price during the listing.Originality/valueThis paper provides evidence for the determinants of the IPO oversubscription.

Point and Interval Solar Power Forecasting Using Hybrid Empirical Wavelet Transform and Robust Wavelet Kernel Ridge Regression

In this paper, a new and efficient hybrid empirical wavelet transform (EWT)-based reduced robust Mexican hat wavelet kernel ridge regression (RMHWK) model is proposed to achieve both point and interval forecasting of solar power in a smart grid scenario. Initially, the actual nonlinear solar power data series was decomposed by the EWT method. A reduced robust kernel ridge regression (RKRR) approach was incorporated that shows a notable decrease in training time without appreciable loss in forecasting accuracy. The reduction in the size of the kernel matrix was achieved by selecting a set of random support vectors from the training data set. For validating the superior performance of the proposed EWT-RMHWK forecasting model, a numerical experimentation implementing a real-time data set of 1 MW solar power plant (Odisha, India) as well as an online historical data set (Florida, USA) was considered and compared with other hybrid models using either empirical mode decomposition- or wavelet decomposition-based RKRR and EWT-ELM, etc. The kernel parameters were optimized with the chaotic water cycle algorithm to boost the performance of the proposed prediction model. Further, the proposed EWT-RKRR method was used to construct prediction interval forecasting with three different confidence levels with 90%, 95%, and 99% for Florida solar power plant using different time horizons of 15 min, 1 h, and 1 day, respectively.

Modelling Colinearity in the Presence of Non–Normal Error: A Robust Regression Approach

Modelling Colinearity in the Presence of Non–Normal Error: A Robust Regression Approach

Evaluation of 30-Day Hospital Readmission and Mortality Rates Using Regression-Discontinuity Framework

BackgroundThe Hospital Readmissions Reduction Program (HRRP) has been associated with reduced 30-day readmissions for acute myocardial infarction (AMI) and heart failure (HF). ObjectivesThe purpose of this study was to test whether this 30-day readmission reduction is a manifestation of practices that defer or avoid hospitalizations beyond the 30-day period. MethodsAt all U.S. hospitals under HRRP, the authors calculated daily readmission rates for elderly Medicare fee-for-service beneficiaries through day-60 post-discharge following a hospitalization for AMI and HF—the 2 target cardiovascular conditions—as well as pneumonia in July 2008 to June 2016. The authors applied a robust bias-corrected nonparametric regression approach to evaluate for discontinuities in rates around day 30. ResultsThe authors identified 3,256 eligible hospitals, with median readmission rates in the days 1 to 30 and 31 to 60 post-discharge of 19.6% (interquartile range [IQR]: 16.7% to 22.9%) and 7.8% (IQR: 6.5% to 9.4%) for AMI, 23.0% (IQR: 20.6% to 25.3%) and 11.4% (IQR: 10.2% to 12.6%) for HF, and 17.5% (IQR: 15.4% to 19.8%) and 8.3% (IQR: 7.3% to 9.3%) for pneumonia, respectively. Daily readmission rates decreased across most of the 60 post-discharge days, with no discontinuities in the local polynomial regression for readmission at the 30-day mark, with a >95% power to detect 0.1% difference for each outcome across post-discharge day 30. Similarly, there was no discontinuity in mortality at 30 days post-discharge, or for either outcome at hospitals that incurred readmission penalties. ConclusionsThere was no evidence that clinicians adopted strategies that specifically deferred admissions or affected mortality in the 30-day period after discharge. The findings are consistent with the institution of strategies that generally affected readmission risk after discharge.

Solar irradiance forecasting using fuzzy logic and multilinear regression approach: A case study of Punjab, India

<p class="MDPI17abstract">The accurate forecasting of solar irradiance depends on various uncertain parameters like time of day, temperature, wind speed, humidity, and atmospheric pressure. All these play an important role in calculating PV power output. In this paper, a novel approach for forecasting of solar irradiance using flexible and accurate fuzzy logic and robust multi-linear regression approach has been proposed considering the above mentioned five variables. Based on the simultaneous consideration of those five variables, the solar irradiance is forecasted using the proposed methodology at a particular location in India, and the results are compared with the real time measured value of solar irradiance at that location on the days for which solar irradiance are forecasted. The proposed method is validated by comparing the results with real time data. The error analysis of the fuzzy logic based proposed system shows the root mean square error of 10.011 and mean absolute percentage error of 1.703%, while compared with real time data measured by instruments pyranometer, anemometer etc. The same results are found better while compared with the results obtained using multilinear regression approach.</p>

Linking Sleep to Externalizing Behavioral Difficulties: A Longitudinal Psychometric Survey in a Cohort of Italian School-Age Children.

We examined the longitudinal relationship between sleep problems and behavioral problems at primary school in Italian children. We recruited a school-based sample of 227 children (age range 6-10years) in schools located in Pisa (Italy). Parents completed the Sleep Disturbance Scale for Children (SDSC), and teachers completed the Strengths and Difficulties Questionnaire (SDQ) to evaluate children's behavioral difficulties. We used a two-step robust regression approach to identify which aspects of sleep problems might affect children's behavior at school. After removing socio-demographic effects, results indicated an association between an increase in sleep problems and the worsening of inattentive and hyperactive behavioral problems at school 1year later. This association was particularly robust in children whose sleep problems had gotten worse over the year. We found no associations between child sleep problems and conduct problems in school settings. Schools may be a suitable arena in which to identify and prevent the development of severe externalizing behaviors through screening procedures and intervention for children's sleep problems.

A novel robust multivariate regression approach to optimize multiple surfaces

Response surface methodology involves relationships between different variables, specifically experimental inputs as controllable factors, and a response or responses by incorporating uncontrollable factors named nuisance. In order to optimize these response surfaces, we should have accurate response models. A common approach to estimate a response surface is the ordinary least squares (OLS) method. Since OLS is very sensitive to outliers, some robust approaches have been discussed in the literature. Most problems face with more than one response which are mostly correlated, that are called multi-response problem. This paper presents a new approach which takes the benefits of robust multivariate regression to cope with the mentioned difficulties. After estimating accurate response surfaces, optimization phase should be applied in order to have proper combination of variables and optimum solutions. Global criterion method of multi-objective optimization has also been used to reach a compromise solution which improves all response variables simultaneously. Finally, the proposed approach is described analytically by a numerical example.

Efficiency of specialized 3PL providers in an emerging economy

Economic growth increases the need for continuous improvement of logistic processes. In this context, comprehensive services and high-level operational performance are competitive advantages, with special attention to refrigeration services in the cold chain. This study's main objective is to determine the variables that significantly affect the efficiency of third-party logistics (3PL) providers of refrigeration services and propose ways to improve the competitiveness of specialized 3PLs. This study uses Data Envelopment Analysis (DEA) and robust regression approach in secondary data from the annual study published by Revista Tecnologística (years 2008–2016). The aim is to measure the impact of contextual variables on the efficiency of Brazilian 3PL providers of refrigeration services. Results indicate that, in fact, pure, technical and scale efficiency levels are differently impacted by the type of technology adopted and the services offered, what leaves room opened for the adoption of different measures and operational practices to increase efficiency levels in its different dimensions. The study provides guidance for managers who wish to establish or adapt specialized 3PL structures and management systems in cold chains. Our study is the first to help identifying the main determinants of efficiency for 3PL providers of refrigeration services in an emerging economy. Further, it develops performance benchmarks for specialized logistics services.

Simple heuristic for data-driven computational elasticity with material data involving noise and outliers: a local robust regression approach

Data-driven computing in applied mechanics utilizes the material data set directly, and hence is free from errors and uncertainties stemming from the conventional material modeling. This paper presents a data-driven approach that is robust against noise and outliers in the data set. For each structural element, we extract the material property from some nearest data points. Using the nearest neighbors reduces the influence of noise, compared with the existing method that uses a single data point. Also, the robust regression is adopted to reduce the influence of the outliers. Numerical experiments on static equilibrium analysis of trusses are performed to illustrate that the proposed method is robust against the presence of noise and outliers and, hence, is effective for dealing with real-world data.

Sustainability of Chinese airlines: A modified slack‐based measure model for CO2 emissions

AbstractSustainable airline operations have become an increasingly important issue in recent years. With this respect, several initiatives for reducing pollutant emissions—such as carbon dioxide (CO2)—in the airline industry are now under consideration by regulators, policymakers, and companies. The impact of these initiatives upon efficiency levels of airline operations is still being analysed by different authors. This article is focused on the efficiency assessment of 13 major Chinese airlines from 2008 to 2015, applying a modified slack‐based measure model to account for CO2 emissions. The impact of contextual variables related to the airline's age, fleet mix, stock market governance, ownership type, network span, and whether or not it has undergone a previous merger and acquisition process is tested by means of a stochastic non‐linear robust regression approach. Findings suggest that sustainability in Chinese airline operations is dependent upon a number of economic factors such as learning curve, economies of scale, technology type, and network management. Policy implications are derived for Chinese airlines.

An exponential-type kernel robust regression model for interval-valued variables

The presence of outliers is very common in regression problems and the use of robust regression methods is strongly recommended such that the bad fitted observations not affect the parameter estimates of the model. Interval-valued variables are becoming common in data analysis problems since this type of data represents either the uncertainty existing in an error measurement or the natural variability present in the data. Regarding the presence of outliers in interval-valued data sets, few robust regression methods have been proposed in literature. This paper introduces a new robust regression method for interval-valued variables that penalizes the presence of outliers in the midpoints and/or in the ranges of interval-valued observations through the use of exponential-type kernel functions. Thus, the weight given to the midpoint and range of each interval-valued observation is updated at each iteration in order to optimize a suitable objective function. The convergence of the parameter estimation algorithm is guaranteed with a low computational cost. A comparative study between the proposed method against some previous robust regression approaches for interval-valued variables is also considered. The performance of these methods are evaluated based on the bias and mean squared error (MSE) of the parameter estimates for the midpoints and ranges of the intervals, considering synthetic data sets with X-space outliers, Y-space outliers and leverage points, different sample sizes and percentage of outliers in a Monte Carlo framework. The results suggest that the proposed approach presents a competitive performance (or best), in comparison with the previous approaches, on interval-valued outliers scenarios that are comparable to those found in practices. Applications to real interval-valued data sets corroborates the usefulness of the proposed method.

Robust regression for image binarization under heavy noise and nonuniform background

This paper presents a robust regression approach for image binarization under significant background variations and observation noise. The work is motivated by the need of identifying foreground regions in noisy microscopic images or degraded document images, where significant background variations and observation noise make image binarization challenging. The proposed method first estimates the background of an input image, subtracts the estimated background from the input image, and performs a global thresholding operation to the subtracted outcome thus achieving the binary image of the foreground. A robust regression approach is proposed to estimate the background intensity surface with minimal effects of the foreground intensities and observation noise, and a global threshold selector is proposed on the basis of a model selection criterion in a sparse regression. The proposed approach is validated using 26 test images and the corresponding ground truths, and the outcomes are compared with those of nine existing image binarization methods. The approach is also combined with three morphological segmentation methods to show how the proposed approach can improve their image segmentation outcomes.

Least median of squares and iteratively re-weighted least squares as robust linear regression methods for fluorimetric determination of α-lipoic acid in capsules in ideal and non-ideal cases of linearity.

This study outlines two robust regression approaches, namely least median of squares (LMS) and iteratively re-weighted least squares (IRLS) to investigate their application in instrument analysis of nutraceuticals (that is, fluorescence quenching of merbromin reagent upon lipoic acid addition). These robust regression methods were used to calculate calibration data from the fluorescence quenching reaction (∆F and F-ratio) under ideal or non-ideal linearity conditions. For each condition, data were treated using three regression fittings: Ordinary Least Squares (OLS), LMS and IRLS. Assessment of linearity, limits of detection (LOD) and quantitation (LOQ), accuracy and precision were carefully studied for each condition. LMS and IRLS regression line fittings showed significant improvement in correlation coefficients and all regression parameters for both methods and both conditions. In the ideal linearity condition, the intercept and slope changed insignificantly, but a dramatic change was observed for the non-ideal condition and linearity intercept. Under both linearity conditions, LOD and LOQ values after the robust regression line fitting of data were lower than those obtained before data treatment. The results obtained after statistical treatment indicated that the linearity ranges for drug determination could be expanded to lower limits of quantitation by enhancing the regression equation parameters after data treatment. Analysis results for lipoic acid in capsules, using both fluorimetric methods, treated by parametric OLS and after treatment by robust LMS and IRLS were compared for both linearity conditions.

Supply Network Structure and Firm Performance: Evidence From the Electronics Industry

Although research in the supply chain area inherently involves the study of supply networks, the majority of existing work is limited to utilizing a dyadic approach in examining relationships between buyer and supplier firms. However, real-world supply networks are characterized by a complex network of interfirm relationships and not just dyads. Thus, it is important to examine how structural relationships among supply chain partners impact the performance of firms using a network analytic lens. This paper examines the influence of structural characteristics of a firm's supply network on its performance. Using a dataset of 114 firms consisting of firm-level variables drawn from multiple sources in the electronics industry, we investigate two important structural characteristics of the supply network—structural prominence, depicting the centrality of a firm's position in the supply network, and density, characterizing the interconnectedness among firms in a supply network. Using a robust regression approach, we find that both the structural prominence of firms and the density of the supply networks, along with the interaction between them, positively influence the performance of firms as measured by the firm's asset utilization, cost performance, and operational efficiency. We discuss both theoretical and practical implications of our findings, and relate the results to managerial significance.

Chinese airline efficiency under CO2 emissions and flight delays: A stochastic network DEA model

This article is focused on the efficiency assessment of 13 major Chinese airlines from 2006 to 2014, applying a stochastic network DEA (SNDEA) to account for randomness in undesirable outputs such as flight delays and CO2 emissions. Two stages are considered: flight and network efficiency. Efficiency estimates are computed using multivariate copulas to control for time (trend) and individual (DMU) effects. A robust regression approach is subsequently developed to address the impact of contextual variables on efficiency levels. Results suggest that more progress has been made over the course of the years in terms of controlling flight delays that in terms of reducing CO2 emissions. These results call for specific policies that can target the latter issue more properly: authorities should pay a closer look on airlines listed in the stock market and that operate international flights to apprehend best practices and design regulatory marks to the sector. This paper also lends a distinctive contribution to the literature by modeling the first time the trade-off between flight delays and CO2 emissions in airline efficiency problems.

Robust reduced-rank regression

SummaryIn high-dimensional multivariate regression problems, enforcing low rank in the coefficient matrix offers effective dimension reduction, which greatly facilitates parameter estimation and model interpretation. However, commonly used reduced-rank methods are sensitive to data corruption, as the low-rank dependence structure between response variables and predictors is easily distorted by outliers. We propose a robust reduced-rank regression approach for joint modelling and outlier detection. The problem is formulated as a regularized multivariate regression with a sparse mean-shift parameterization, which generalizes and unifies some popular robust multivariate methods. An efficient thresholding-based iterative procedure is developed for optimization. We show that the algorithm is guaranteed to converge and that the coordinatewise minimum point produced is statistically accurate under regularity conditions. Our theoretical investigations focus on non-asymptotic robust analysis, demonstrating that joint rank reduction and outlier detection leads to improved prediction accuracy. In particular, we show that redescending -functions can essentially attain the minimax optimal error rate, and in some less challenging problems convex regularization guarantees the same low error rate. The performance of the proposed method is examined through simulation studies and real-data examples.