Weighted Least-squares Regression Research Articles

Exploring the Convergence of Macroeconomic Variables and Market Capitalization in an Emerging Economy: An Empirical Analysis Using Arbitrage Pricing Theory

This study delves into the interaction between macroeconomic variables and market capitalization in Emerging Capital Markets, i.e., Bangladesh. We examine the effects of the invest-to-deposit ratio (IDR) by financial institutions, market interest rate (MKTINT), and Consumer Price Index (CPI) on market capitalization (MAKTCAP). This study utilizes a dataset spanning 83 months, encompassing the period from 2015 to 2021. The macroeconomic influence is examined empirically by using the Arellano–Bover/Blundell–Bond generalized method of moments (GMM) estimator, supplemented by the ordinary least squares (OLS) and weighted least squares regression (WLS). Also, use Breusch-Pagan and Cook-Weisberg tests to measure heteroskedasticity in the model. The findings show a strong correlation between IDR and MAKTCAP, pointing to IDR as the most important yet overlooked factor influencing capital market returns. However, MKTINT and CPI results address a negative relationship with MAKTCAP. Higher IDR indicates that the capital market receives more cash flow to enhance market capitalization. At the same time, lowering MKTINT and CPI enables the capital market to get more money flow.

Selecting a Suitable Model for Roundabout Entrance Capacity Estimation: A Case Study

Abstract In India, urban vehicular traffic is highly heterogeneous; it appears to be extremely challenging to accurately anticipate roundabout capacity. The main objective of this paper is to develop two models which are predicting the capacity of roundabouts in the Indian scenario. One is Weighted Least Square Regression (WLSR), a linear regression is carried out using weights of each data variable while the other one is Self Adaptive Segregative Genetic Algorithm with Simulated Annealing (SASEGASA), which is a combination of Segregative Genetic Algorithm (SEGA) and self-adaptive selection pressure with the application of simulated annealing. The coefficient of determination (R2) and Nash-Sutcliffe model efficiency coefficient (E) for the models like SASEGASA and WLSR were obtained as (0.947, 0.949) and (0.906, 0.905) respectively. Ranking among the proposed models is carried out using a complex phenomenon known as Modified Ranking Index (MRI), where the highest rank generated from five estimates is chosen as the best model for this study. SASEGASA is ranked as the appropriate model to estimate the roundabout capacity as per MRI. As compared to previously used models, SASEGASA model is quite relevant for field application.

Assessing surrogacy using restricted mean survival time ratio for overall survival in liver cancer: a narrative review.

The application of immunotherapy in cancers, including liver cancer, has been increasing. However, non-proportional hazard (NPH) is often observed in cancer immunotherapy trials. In presence of violation of proportional hazard (PH) assumption, restricted mean survival time (RMST) ratio was proposed as an alternative to hazard ratio (HR) for evaluating the treatment effects of such trials. To shorten the total study duration, an intermediate endpoint with shorter follow-up such as progressionfree survival (PFS) is used as the primary endpoint. Our aim is to evaluate the applicability of RMST ratio in addition to the HR in assessing the level of PFS serving as a surrogacy of overall survival (OS). Phase II or phase III hepatocellular carcinoma (HCC) immunotherapy studies that were published between January 2013 and August 2022 were identified via the search in PubMed. Weighted leastsquare regression (WLSR) was applied to analyze the trial level data with the sample size of study being set as the weight. The evaluation was conducted twice with RMST ratio and HR being applied in respective evaluation to examine the level of PFS as a surrogacy for OS. Based on the results of eight included trials, the R-square values of WLSR with either HR or RMST ratio being applied were 0.31 and 0.16 separately, indicating a moderate and low correlation between PFS and OS respectively. In this study, our results demonstrated the potential of RMST ratio in addition to HR for evaluating the level of surrogacy in immunotherapy trials. Furthermore, including more large scale and homogeneous studies into the research may help better understand the level of surrogacy in liver cancer.

Estimation of tropical forest aboveground biomass in Nepal using multiple remotely sensed data and deep learning

ABSTRACT This study assessed the prediction accuracy of the forest aboveground biomass (AGB) model using remotely sensed data sources (i.e. airborne laser scanning (ALS), RapidEye, Landsat), and the combination of ALS with RapidEye/Landsat using parametric weighted least squares (WLS) regression. We also analysed the AGB model using random forests, extremely randomized trees, and deep learning stacked autoencoder (SAE) network from nonparametric statistics to compare the performance with WLS regression. We also compared the widths of the 95% confidence intervals for estimates of the mean AGB per unit area using the model-based estimator. The study site in the Terai Arc Landscape, Nepal, comprised 14 protected areas extending from the southern part of Nepal to India and encompassed mosaics of continuous dense forest and tall grassland. The ALS data provided the largest prediction accuracy (0.30–0.35 relative root mean squared error (rRMSE)), whereas RapidEye and Landsat had smaller prediction accuracies (0.48‒0.54 and 0.47‒0.55 rRMSE, respectively) for the estimation of AGB. The combined use of ALS and RapidEye predictors in the AGB model reduced the rRMSE and narrowed the confidence interval compared with ALS alone, but the improvements were minor. The SAE prediction technique provided the largest prediction accuracy, with inputs of combined ALS and RapidEye predictors that yielded an R2 of 0.80, an rRMSE of 0.30, and a confidence interval of 176‒184 compared to other tested prediction techniques. The SAE prediction technique can become more powerful than other tested prediction techniques if properly adjusted and tuned for accurate forest AGB mapping applications. To our knowledge, this is the first study assessing the performance of the SAE in AGB modelling with a range of hyper-parameter values.

Tobin-Q, Liquidity and Momentum risk-premia: A Demonstration of Weighted Least Squares Regression Approach

Purpose- The basic purpose of the study is to examine whether Tobin-q, liquidity and momentum risk-premium contributes the explanatory power in terms of explaining portfolio returns in PSX. Design/Methodology- The Weighted Least Square (WLS) regression technique is empirically used to examine the nexus between risk-factor and portfolio returns using PSX dataset. The models provide useful tools for making efficient strategies in the jurisdiction of investments and portfolio constructions. Findings- The study reveals that multidimensional liquidity exhibits weak significant results while Tobin-q and momentum risk-factors demonstrate statistically significant determinants for PSX. Furthermore, WLS regression produces robust coefficient results than OLS regression as except liquidity all the factors exhibit substantially improved results. Practical Implications- The study findings would be useful for stocks and portfolio managers constructing optimal and diversified portfolios while investing in PSX.

A post-marketing safety assessment of COVID-19 mRNA vaccination for serious adverse outcomes using administrative claims data linked with vaccination registry in a city of Japan

IntroductionThe safety profiles of COVID-19 vaccines are incompletely evaluated in Japan. ObjectivesTo examine the risk of serious adverse effects after COVID-19 mRNA vaccination (BNT162b2 and mRNA-1273) in cohort studies and self-controlled case series (SCCS). MethodsUsing an administrative claims database linked with the COVID-19 vaccination registry in a city in Japan between September 2020 and September 2021, we identified health insurance enrolees aged ≥ 18 years. We evaluated the risk of acute myocardial infarction, appendicitis, Bell’s palsy, convulsions/seizures, disseminated intravascular coagulation, immune thrombocytopenia, pulmonary embolism, haemorrhagic or ischemic stroke, venous thromboembolism, and all-cause mortality, 21 days following any COVID-19 mRNA vaccination, compared with non-vaccination periods. For the cohort studies, we estimated incidence rate ratios (IRRs) by Poisson regression and rate differences (IRDs) by weighted least-squares regression, adjusting for sex, age, and Charlson comorbidity index. We applied a modified SCCS design to appropriately treat outcome-dependent exposures. For the modified SCCS, we estimated within-subject IRRs by weighted conditional Poisson regression. Subgroup analyses stratified by sex and age were also conducted. ResultsWe identified 184,491 enrolees [male: 87,218; mean (standard deviation) age: 64.2 (19.5) years] with 136,667 first and 127,322 s dose vaccinations. The risks of any outcomes did not increase in any analyses, except for the fact that the modified SCCS indicated an increased risk of pulmonary embolism after the first dose in women (within-subject IRR [95%CI]: 3.97 [1.18–13.32]). ConclusionThe findings suggested that the COVID-19 mRNA vaccine was generally safe, whilst a signal of pulmonary embolism following the first dose of the COVID-19 mRNA vaccine was observed.

Effect of clinical peer review on mortality in patients ventilated for more than 24 hours: a cluster randomised controlled trial

BackgroundAlthough clinical peer review is a well-established instrument for improving quality of care, clinical effectiveness is unclear.MethodsIn a pragmatic cluster randomised controlled trial, we randomly assigned 60 German Initiative Qualitätsmedizin...

Assessing surrogacy using restricted mean survival time ratio for overall survival in non-small cell lung cancer immunotherapy studies.

Proportional hazards (PH) assumption is often violated in cancer immunotherapy studies. Restricted mean survival time (RMST) ratio is a valid metric to quantify the size of treatment effect when non-proportional hazard (NPH) is present. This study investigated the use of RMST ratio and hazard ratio (HR) in studying progression-free survival (PFS) as a surrogate endpoint for overall survival (OS) in non-small cell lung cancer immunotherapy trials. Trial level data were collected from 14 phase III trials published between 2012 and 2018. A weighted least-square regression (WLSR) was performed to evaluate the trial-level surrogacy. Surrogacy was evaluated via the association between RMST ratios for PFS and OS and between HRs for PFS and OS. Using data extracted from published articles, low to moderate correlation (0.49) between PFS and OS was observed for HR while low correlation (0.35) was observed for RMST ratio. When trials violating PH in PFS were included, more consistent correlations for both HR (0.43) and RMST ratio (0.44) were observed. In summary, the strength of PFS surrogacy for OS depends on whether HR or RMST ratio are chosen. RMST ratio and additional sensitivity analysis should be considered in addition to HR.

Assessment of the uncertainty in spatial-correlation models for earthquake ground motion due to station layout and derivation method

The evaluation of the aggregate risks to spatially distributed infrastructures and portfolios of buildings requires quantification of the estimated shaking over a region. To characterize the spatial dependency of ground motion intensity measures (e.g. peak ground acceleration), a common geostatistical tool is the semivariogram. Over the past decades, different fitting approaches have been proposed in the geostatistics literature to fit semivariograms and thus characterize the correlation structure. A theoretically optimal approach has not yet been identified, as it depends on the number of observations and configuration layout. In this article, we investigate estimation methods based on the likelihood function, which, in contrast to classical least-squares methods, straightforwardly define the correlation without needing further steps, such as computing the experimental semivariogram. Our outcomes suggest that maximum-likelihood based approaches may outperform least-squares methods. Indeed, the former provides correlation estimates, that do not depend on the bin size, unlike ordinary and weighted least-squares regressions. In addition, maximum-likelihood methods lead to lower percentage errors and dispersion, independently of both the number of stations and their layout as well as of the underlying spatial correlation structure. Finally, we propose some guidelines to account for spatial correlation uncertainty within seismic hazard and risk assessments. The consideration of such dispersion in regional assessments could lead to more realistic estimations of both the ground motion and corresponding losses.

Education Equality and Economic Growth in Indonesia

Last decade, Indonesia's economic growth experienced a downward trend. The study examines the role of investment, equality in education, poverty, income inequality, and regime to economic growth in Indonesia. We used time-series data between 1970-2017. It was obtained from BPS and World Bank (Indodapur) publications. The model used is the Weighted Least Square Regression (WLS). The results showed the factors that contributed significantly to increasing Indonesia's economic growth were education equality, poverty, and income inequality. While investment/capital, economic transparency, and the regime did not significantly contribute to increasing economic growth. Expanding access to education for high school or equivalent is important by the Government, including the development of school infrastructure in remote areas and teacher distribution. The Government should maintain the poverty trend that continues to decline. The future study dynamic models look at the long-term relationships related to education equality, distribution of income, and poverty on economic growth.

Fitting process-dependence performance of the van Genuchten soil water retention model to simulate the soil water flow

Determining the parameters of the soil water retention curve (SWRC) is essential to model the water flow in soil and investigate the water and nutrients transport around the roots of plants and ensure optimal management of irrigation. The unweighted least squares regression (ULS) is the most common approach applied to fit the SWRC functions to the observed data-points to optimize their parameters. The main purpose of this study was to evaluate the ULS and the weighted least squares regression (WLS) fitting process of SWRC and their impact on simulations of soil water flow. In this regard, the measured SWRC data in six parallel samples from given depth were fitted to the SWRC equations to optimize their parameters, through either the WLS or the ULS. In the WLS approach, the weights were calculated as the inverse of the variance in each pressure head of different iterations (six repetitions in this study). The results showed that although WLS generally resulted in an increased error in estimating the SWRC in the van Genuchten-Mualem model (RMSE = 0.027 and 0.043 cm3 cm−3 in the ULS and WLS methods, respectively), it improved the accuracy of the estimations at lower water contents (dry-end), as compared to the ULS. However, the efficiency of the ULS and WLS in estimating the SWRC was different from that in simulating the soil water flow. The water flow in the soil was more accurately simulated using the hydraulic parameters obtained by the WLS (with RMSE = 0.029 and 0.026 cm3 cm-3 at x = 0 and x = 20 cm, respectively, in the WLS, and RMSE = 0.062 and 0.065 cm3 cm-3 at x = 0 and x = 20 cm, respectively, in the ULS), especially at the low water range. While there was an important difference between the ULS and WLS in terms of estimating the SWRC and water flow simulations, other dispersion scenarios should be evaluated in future studies to compute the error of the fitting processes at low-pressure heads.

Development and validation of a LC-HRMS method for the quantification of cannabinoids and their metabolites in human plasma

The resurgence of Cannabis therapeutic discoveries have led to the need for sensitive and selective analytical methods for the detection of cannabinoids and their metabolites in biological matrices. High resolution mass spectrometry (HRMS) enables good sensitivity and provides more selectivity due to its accurate mass measurement of the targeted compounds. The aim of this study was to develop and validate a sensitive liquid chromatography high resolution mass spectrometry (LC-HRMS) method for the quantitative analysis of cannabidiol (CBD), cannabinol (CBN), Δ9-tetrahydrocannabinol (Δ9-THC) and its major metabolites 11-Hydroxy-Δ9-THC and 11-Nor-9-carboxy-Δ9-THC in human plasma. The method utilized a simple liquid-liquid extraction of the cannabinoids from plasma samples followed by an isocratic chromatographic separation and detection by ESI-HRMS Q-Exactive plus platform. The lower limit of quantification (LLOQ) was 0.2 ng/ mL for the targeted cannabinoids and its metabolites with sample volume of 0.5 mL plasma. The method was linear from 0.2 to 100.0 ng/mL with an average correlation coefficient of >0.995 using weighted (1/x) linear least-squares regression. No significant carry-over was noticed for all analytes and the extraction recovery ranged from 60.4 % to 85.4 %. Dilution results indicated no influence on the accuracy of analysis. The method's intra-day and inter-day precision (CV %) ranged from 2.90 to 10.80 % and accuracy within -0.9 to 7.0 from nominal. Matrix effect ranged from 1.1 % to 49.8 %. The analytes were stable in the autosampler for 6 and 12 h, respectively. This method was sensitive and can be applicable to cannabinoids pharmacokinetics study.

Statistical evaluation of analytical curves for quantification of pesticides in bananas

The aim of this paper is to statistically validate the analytical curves of a chromatography method to identify and quantify azoxystrobin, difenoconazole and propiconazole residues in banana pulp, using QuEChERS and GC-SQ/MS. A matrix-matched calibration was used and analytical curves were estimated by weighted least squares regression (WLS), confirming heteroscedasticity for all compounds. Statistical tests were performed to confirm the quality adjustment of the proposed linear model. The correlation coefficient for azoxystrobin, difenoconazole and propiconazole were, respectively, 0.9985, 0.9966 and 0.9997 (concentration range: 0.05 and 2.0 mg kg−1). The limits of detection and quantification were, respectively, between 0.007 and 0.066 mg kg−1, and between 0.022 and 0.199 mg kg−1, below the maximum limits stipulated by Brazilian, American, and European legislation. Only difenoconazole had an insignificant matrix effect (6.8%). Thus, the weighted least squares method is shown to be a safe linear regression model, providing greater reliability of the results.

Factors affecting the academic performance of real estate students in a specialized Federal University of Technology in Nigeria

PurposeThe purpose of this paper is to examine the factors affecting the academic outcome of real estate students in a specialized Federal University in Nigeria. Furthermore, this paper investigates the phenomenon of publication bias in the extant literature as such evidence poses severe threats to the validity of empirical findings on factors affecting the degree outcome of undergraduate students.Design/methodology/approachThe standard statistical approach adopted was to examine whether the reported coefficient estimates from ten empirical studies (105 observations) are independent of their standard errors by employing both ordinary least squares (OLS) and weighted least squares (WLS). In this paper, this approach enabled evidence of publication bias in the cited literature to be refuted. In addition, data were also collected on the academic measure and demographic information of 449 students who graduated between 2005 and 2011. For the purpose of analysis, the study utilized a stepwise logistic regression technique to examine the factors impacting on the degree outcome of real estate students.FindingsThe results of the OLS and WLS regression indicate that there is no significant evidence of any empirical effect of publication bias in the extant literature. The results of the logistic regression also revealed that grade point average, gender differences, prior knowledge of real estate discipline and potential difference in year of enrollment impact on students’ academic performance in terms of their ability to graduate at first attempt. In addition, factors such as age, marital status, high school grade and geopolitical/ethnic background of undergraduate real estate students do not influence their opportunities to graduate at first attempt from the university.Research limitations/implicationsThis paper focuses only on one specialized university of technology offering a bachelor’s program in real estate in Nigeria, so as to remove any extraneous factor(s) that could be present in the other institutional settings where students have completed such program. Extending similar study to tertiary institutions in Nigeria that share similar geographical characteristics and institutional settings can produce far-reaching generalization.Originality/valueThis paper contributes to the scanty literature on factors affecting the academic performance of students in an undergraduate real estate program in Nigeria. A scientific element of novelty in this paper is the evidence of the absence of the underlying effect of publication bias in the extant literature on students’ academic outcome in tertiary institutions. Findings from this study serve as the basis for university officers to monitor significant transitions in real estate students’ academic progress, so as to identify those who are unlikely to graduate at first attempt early at the entrant level. Generally, the outcome of this research could provide faculty and admission officers in tertiary institutions with complementary information in arriving at an informed decision in a non-discriminatory admission process.

Recent progress on Thomson scattering diagnostic on HL-2A tokamak

Some new progress has been made to develop the multi-point Thomson scattering (TS) diagnostic for the HL-2A tokamak physics experiments. Hardware of silicon avalanche photodiode detector electronics, motorized stages to control the laser beam for beam alignment, 3 modules of fast digitizers with more than 100 channels to record the time evolution of the TS pulses at 2.5 GS/s with 12-bit resolution, and 15 polychromators for 15-point measurements of core plasma electron temperature. The data processing code is further adjusted to manage the digitized raw data. The TS intensity is obtained by direct summing method and by Gaussian-function fitting, respectively, and then different value of electron temperature is derived by the technique of weighted least-squares regression. As to the latter, the electrical noise and perturbations of the TS signal is significantly reduced, the resulting value of electron temperature has a better quality than that of the former. New processing code is in development.

Data Science for Vibration Heteroscedasticity and Predictive Maintenance of Rotary Bearings

Electric motors are widely used in our society in applications like cars, household appliances, industrial equipment, etc. Costly failures can be avoided by establishing predictive maintenance (PdM) policies or mechanisms for the repair or replacement of the components in electric motors. One of key components in the motors are bearings, and it is critical to measure the key features of bearings to support maintenance decision. This paper proposes a data science approach with embedded statistical data mining and a machine learning algorithm to predict the remaining useful life (RUL) of the bearings in a motor. The vibration signals of the bearings are collected from the experimental platform, and fault detection devices are developed to extract the important features of bearings in time domain and frequency domain. Regression-based models are developed to predict the RUL, and weighted least squares regression (WLS) and feasible generalized least squares regression (FGLS) are used to address the heteroscedasticity problem in the vibration dataset. Support vector regression (SVR) is also applied for prediction benchmarking. Case studies show that the proposed data science approach handled large datasets with ease and predicted the RUL of the bearings with accuracy. The features extracted from time domain are more significant than those extracted from frequency domain, and they benefit engineering knowledge. According to the RUL results, the PdM policy is developed for component replacement at the right moment to avoid the catastrophic equipment failure.

Enhancement of SNR in fetal ECG signal extraction using combined SWT and WLSR in parallel EKF

A Fetal Electrocardiogram (FECG) signal will contain some potential information which is precise for assisting the clinicians to make appropriate decisions that are timely at the time of pregnancy and labor. Extraction and detection of such FECG signals from their composite maternal signals of the abdomen using very powerful and advanced methodologies is a very critical need in case of fetal monitoring. The mining of pure FECG signals from the abdominal of the prenatal woman is focused in this paper. This FECG signal is very vulnerable to noise and is very difficult to process it accurately with no significant distortion that can imped its use. So, in order to be able to get some proper information on the status of the fetus or its condition it is important to be able to improve the abdominal signal and its SNR. Since the wavelet transform is well in providing information in time and frequency, the combination of Stationary Wavelet Transform (SWT), Weighted Least Square Regression (WLSR) and parallel Extended Kalman Filter (Par-EKF) are used for FECG extraction. The analysis for different values of Signal to Noise Ratio (SNR) between fetal and maternal ECG’s is implemented using MATLAB. As a Result, the highest SNR obtained is 33.5 which showed that the proposed method is efficient in extracting more information about FECG from Maternal ECG (MECG).

Evaluation of three statistical prediction models for forensic age prediction based on DNA methylation

DNA methylation is a promising biomarker for forensic age prediction. A challenge that has emerged in recent studies is the fact that prediction errors become larger with increasing age due to interindividual differences in epigenetic ageing rates. This phenomenon of non-constant variance or heteroscedasticity violates an assumption of the often used method of ordinary least squares (OLS) regression. The aim of this study was to evaluate alternative statistical methods that do take heteroscedasticity into account in order to provide more accurate, age-dependent prediction intervals. A weighted least squares (WLS) regression is proposed as well as a quantile regression model. Their performances were compared against an OLS regression model based on the same dataset. Both models provided age-dependent prediction intervals which account for the increasing variance with age, but WLS regression performed better in terms of success rate in the current dataset. However, quantile regression might be a preferred method when dealing with a variance that is not only non-constant, but also not normally distributed. Ultimately the choice of which model to use should depend on the observed characteristics of the data.

Statistical Analysis for Quality Adjustment of the Analytical Curve for Determination of Pesticide Multiresidue in Pineapple Samples

This work describes the development of an analytical methodology based on the modified QuEChERS method and GC/MS detection involving the analysis of 38 pesticides in pineapple from a local market in Fortaleza-Brazil. Matrix-standard calibration and heteroscedasticity have been studied by a weighted least squares linear regression model application. The analytical curves for most of the analytes (except cyproconazole and pyraclostrobin) presented heteroscedasticity by weighted least square regression (WLS). The weighted correlation coefficient values were in the range of 0.9690 to 0.9987 (linear range of 0.03 to 0.50 mg kg−1). The calibration curves obtained by applying regression by ordinary least squares (OLS) showed relative errors of approximately 32.3% for fenpropathrin, 23.1% for buprofezin, 17.4% for methyl parathion, and 13.9% for ametryn. Analysis of commercial pineapple samples revealed the presence of methyl parathion, buprofezin, ametryn, and fenpropathrin at 0.023 ± 0.0037, 0.013 ± 0.0047, 0.043 ± 0.0156, and 0.031 ± 0.0049 mg kg−1, respectively.

Natural Resource Management and Household Well-being: The Case of POSAF-II in Nicaragua

Summary Measuring the impact of natural resource programs is a key element in the formulation and implementation of policies designed to promote farm income while enhancing the quality of the surrounding environment. In this paper, we analyze the economic impact of natural resource technologies delivered by the Socio-environmental and Forestry Development Program-II (POSAF-II) in Nicaragua. We use cross-sectional data for 1,483 households, from 212 treated and control communities. Results obtained from propensity score matching (PSM), ordinary least squares (OLS), weighted least squares regression (WLS) based on PSM, and instrumental variables (IV) regression indicate that POSAF-II has had a positive impact on the total value of agricultural production of beneficiary farmers. An internal rate of return analysis supports the hypothesis that increasing household income while encouraging the sustainable use of natural resources through the implementation of suitable management programs can be complementary development objectives.