Linear Regression Estimation Research Articles

Robust Estimation of High-Dimensional Linear Regression With Changepoints

Robust Estimation of High-Dimensional Linear Regression With Changepoints

Financial Technology (Fintech) as a Driver for Poverty Reduction in Nigeria

ABSTRACT: The human race has been confronting the issue of poverty which is threatening its continued existence for a long time. Poor access to finance can lead to poverty this breeds health issues, lack of access to social and recreational facilities, social delinquencies, reduced living standard, malnutrition and economic difficulties. The goal of defeating poverty in its various ramification remains a cardinal concern for policy formulators in the Nigerian state and international bodies such as the World Bank and United Nations. Following the thriving emergence of financial technology (fintech) in Nigeria, there exist a dearth in academic researches to examine how fintech can be a driver in the reduction of poverty. This study examined the impact of financial technology on poverty reduction in Nigeria. Data on poverty rate, financial technology, mortality rate, literacy rate and gross domestic products were obtained from the National Bureau of Statistics (NBS) in Nigeria and the Central Bank of Nigeria (CBN) statistical bulletin for the period of 1991 – 2021. This study employed the multiple linear regression estimation technique to analyse collected data as it examines a cross-section of major key independent variables across a time series data, on the premise of a common effect model. Data analysed indicates a negative and significant nexus between poverty rate and financial technology innovation. The result also suggest that literacy rate and poverty rate have a negative and insignificant relationship. The poverty rate was also found to be positively and insignificantly related to mortality rate and gross domestic products respectively. The study findings have several significant policy implications. Firstly, infrastructures that support internet and mobile telephony should be expanded by providing incentives for investment since technology is the backbone of the operations of fintech. Secondly, education on fintech, information communication technology and finance should be increased to fill the gap among citizens who are not technological inclined especially in the rural areas to increase the use of fintech. Lastly, there should be further regulatory interventions through reforms to remove the barriers to credit.

Effectiveness of Iterative Learning Control for Vision-Based Tracking in Repeated Tasks Under Varying Lighting Conditions

Among available techniques for lateral tracking in autonomous driving, none possesses the capability to learn from past behaviors and progressively reduce lateral errors. In contrast, our proposed iterative learning control (ILC) scheme significantly enhances tracking performance in vision-based classical control, particularly in challenging environmental conditions. Our vision-based control system incorporates real-time semantic segmentation using the ENet model for unconstructed road area extraction. Linear regression estimates steering adjustments, while a visual PID controller maintains the vehicle’s position at the road’s centerline. This control system’s performance varies with lighting conditions, notably in dense shade, where the vehicle tends to deviate from the desired path. To assess ILC’s potential in error reduction over successive trials, we examined various ILC structures and compared them with a pure PID controller. Despite challenges in directly comparing PID and ILC designs due to changing lighting conditions during experiments, ILC consistently reduced tracking errors and improved path alignment with each iteration. Notably, the degree of error reduction became more pronounced with a greater number of learning gains in the ILC design. Our experimental results underscore the overall effectiveness of ILC in tracking the desired path in autonomous driving scenarios, particularly in varying environmental conditions.

Robust tests for equality of regression curves based on characteristic functions

This paper focuses on the problem of testing the null hypothesis that the regression functions of several populations are equal under a general nonparametric homoscedastic regression model. It is well known that linear kernel regression estimators are sensitive to atypical responses. These distorted estimates will influence the test statistic constructed from them so the conclusions obtained when testing equality of several regression functions may also be affected. In recent years, the use of testing procedures based on empirical characteristic functions has shown good practical properties. For that reason, to provide more reliable inferences, we construct a test statistic that combines characteristic functions and residuals obtained from a robust smoother under the null hypothesis. The asymptotic distribution of the test statistic is studied under the null hypothesis and under root − n contiguous alternatives. A Monte Carlo study is performed to compare the finite sample behaviour of the proposed test with the classical one obtained using local averages. The reported numerical experiments show the advantage of the proposed methodology over the one based on Nadaraya–Watson estimators for finite samples. An illustration to a real data set is also provided and enables to investigate the sensitivity of the p − value to the bandwidth selection.

Asymptotic normality of local linear functional regression estimator based upon censored data

This article is concerned with the local linear non parametric regression estimator of a right censored scalar response random variable, given a functional random covariate. First, we investigate its asymptotic normality under the concentration properties of small ball probability functions. Then, we apply the results to present the asymptotic (1−α) confidence interval of the regression function for 0 < α < 1 . To lend further support to our theoretical results, we carry out a simulation study to illustrate the performance and the asymptotic Gaussian behavior of our estimator. Finally, an application to real data is provided.

Hyperspectral Characteristics and SPAD Estimation of Wheat Leaves under CO2 Microleakage Stress.

To non-destructively and rapidly monitor the chlorophyll content of winter wheat leaves under CO2 microleakage stress, and to establish the quantitative relationship between chlorophyll content and sensitive bands in the winter wheat growing season from 2023 to 2024, the leakage rate was set to 1 L/min, 3 L/min, 5 L/min, and 0 L/min through field experiments. The dimensional reduction was realized, fractional differential processing of a wheat canopy spectrum was carried out, a multiple linear regression (MLR) and partial least squares regression (PLSR) estimation model was constructed using a SPA selection band, and the model's accuracy was evaluated. The optimal model for hyperspectral estimation of wheat SPAD under CO2 microleakage stress was screened. The results show that the spectral curves of winter wheat leaves under CO2 microleakage stress showed a "red shift" of the green peak and a "blue shift" of the red edge. Compared with 1 L/min and 3 L/min, wheat leaves were more affected by CO2 at 5 L/min. Evaluation of the accuracy of the MLR and PLSR models shows that the MLR model is better, where the MLR estimation model based on 1.1, 1.8, 0.4, and 1.7 differential SPAD is the best for leakage rates of 1 L/min, 3 L/min, 5 L/min, and 0 L/min, with validation set R2 of 0.832, 0.760, 0.928, and 0.773, which are 11.528, 14.2, 17.048, and 37.3% higher than the raw spectra, respectively. This method can be used to estimate the chlorophyll content of winter wheat leaves under CO2 trace-leakage stress and to dynamically monitor CO2 trace-leakage stress in crops.

Estimator Comparison for the Prediction of Election Results

Cluster randomized experiments and estimator comparisons are well-documented topics. In this paper, using the datasets of the popular vote in the presidential elections of the United States of America (2012, 2016, 2020), we evaluate the properties (SE, MSE) of three cluster sampling estimators: Ratio estimator, Horvitz–Thompson estimator and the linear regression estimator. While both the Ratio and Horvitz–Thompson estimators are widely used in cluster analysis, we propose a linear regression estimator defined for unequal cluster sizes, which, in many scenarios, performs better than the other two. The main objective of this paper is twofold. Firstly, to indicate which estimator is most suited for predicting the outcome of the popular vote in the United States of America. We do so by applying the single-stage cluster sampling technique to our data. In the first partition, we use the 50 states plus the District of Columbia as primary sampling units, whereas in the second one, we use 3112 counties instead. Secondly, based on the results of the aforementioned procedure, we estimate the number of clusters in a sample for a set standard error while also considering the diminishing returns from increasing the number of clusters in the sample. The linear regression estimator is best in the majority of the examined cases. This type of comparison can also be used for the estimation of any other country’s elections if prior voting results are available.

(104) PREDICTORS OF LENGTH OF STAY IN PELVIC INFLAMMATORY DISEASE HOSPITALIZATION: INSIGHTS FROM THE NATIONAL INPATIENT SAMPLE

Abstract Introduction Pelvic inflammatory disease (PID) is a common and potentially serious reproductive health condition that may require hospitalization for proper management. Accurate prediction of the length of stay (LOS) for PID hospitalizations can aid clinicians in resource allocation, patient counseling, and improving overall patient care. Prior studies have explored potential risk factors for a poor clinical course among women hospitalized with PID, but to our knowledge, no study to date has explored potential predictors of LOS in PID hospitalization. Objective This study aims to characterize the predictors for LOS in PID hospitalizations based on patient and hospital characteristics using a large national database. Methods The National Inpatient Sample (NIS) database was queried from 2019 to 2020 for all female patients 18 years and older with a primary diagnosis of acute pelvic inflammatory disease using ICD-10 codes. Data on patient demographics, clinical characteristics, comorbidities, and hospital-related variables were collected using variables as defined by NIS and summarized using descriptive statistics. Bivariate and multivariable associations between LOS and covariates were assessed using linear regression models and estimates were reported as regression coefficients. Covariates that were statistically significant in the bivariate models were included as covariates during the multivariate regression analysis. Results A total of 14,505 hospitalized female patients with a principal diagnosis code for acute PID were included. Mean LOS for PID hospitalization was 5.8 days, with a range of 0 – 88 days. The average patient age was 42.9 years (age range 18 – 90 years old), with 55.1% White, 23.4% Black, 12.5% Hispanic, and 2.3% Asian or Pacific Islander. The main primary payers included Medicaid (40.0%), private insurance (36.1%), Medicare (19.1%), and self-pay (4.7%). About 34% of patients were in the lowest quartile for median household income, 45% in the middle two quartiles, and 19% in the highest quartile. In univariable analysis, increased LOS was associated with increasing age, larger hospital bed size, urban teaching hospital status, and the presence of hypertension, diabetes, obesity, or immunodeficiency. Decreased LOS was associated with Hispanic or Native American race and having a non-Medicare primary payer. In multivariable analysis, increased LOS was associated with increasing age (+0.06 days per increasing year, p &amp;lt; 0.0001) and large hospital bed size (+0.6 days compared to small bed size, p = 0.03). Decreased LOS was associated with Asian or Pacific Islander race (-1.1 days compared to White race, p = 0.02), Native American race (-1.4 days compared to White race, p = 0.03), and being billed as ‘no charge’ (-2.1 days compared to Medicare, p = 0.03). Conclusions This study identifies possible predictors of LOS in PID hospitalization, providing insights that may help optimize resource allocation and better characterize factors that may impact PID management. Several factors, including age, race, expected primary payer, and bed size of hospital were found to significantly impact LOS. Additionally, this study highlights the potential role of social determinants of health in PID hospitalization and management and can help guide future research and initiatives aimed at improving population health and addressing disparities in PID. Disclosure No.

Endovascular thrombectomy versus intravenous thrombolysis for primary distal, medium vessel occlusion in acute ischemic stroke

Abstract Introduction In the absence of clinical trials, the benefit of endovascular therapy (EVT) on the treatment of acute ischemic stroke (AIS) with primary distal and medium vessel occlusions (DMVO) is still not well defined. The aim of the study is to evaluate EVT with or without intravenous thrombolysis (EVT ± IVT) in primary DMVO stroke in comparison with a control cohort treated with IVT alone. Methods We analysed all consecutive AIS with proven primary DMVO. Primary endpoints were excellent outcome, functional independence at 3 months follow-up, and early neurologic improvement at 1 day after treatment. Results One hundred and fourteen patients with DMVO strokes were included between 2019 and 2023. Propensity-weighted analysis showed no significant differences in EVT ± IVT vs IVT for the excellent outcome (adjusted OR [aOR], 1.575; 95% CI, 0.706–3.513), functional independence (aOR, 2.024; 95% CI, 0.845–4.848), early neurological improvement (aOR, 2.218; 95% CI, 0.937–5.247), mortality (aOR, 0.498; 95% CI, 0.177–1.406), symptomatic intracranial haemorrhage (aOR, 0.493; 95% CI, 0.102–2.385), and subarachnoid haemorrhage (aOR, 0.560; 95% CI, 0.143–2.187). The type of revascularization did not influence the percentage of cerebral volume lost (adjusted linear regression estimate, −19.171, t value, 11.562; p = 0.104). Conclusions This study supports the hypothesis that patients with primary DMVO stroke treated with EVT (±IVT) or IVT alone have comparable outcomes.

Taylor Approximation of Inventory Policies for One-Warehouse, Multi-Retailer Systems with Demand Feature Information

We consider a distribution system in which retailers replenish perishable goods from a warehouse, which, in turn, replenishes from an outside source. Demand at each retailer depends on exogenous features and a random shock, and unfulfilled demand is lost. The objective is to obtain a data-driven replenishment and allocation policy that minimizes the average inventory cost per time period. The extant data-driven methods either cannot guarantee a feasible solution for out-of-sample feature observations or generate one with excessive computational time. We propose a policy that resolves these issues in two steps. In the first step, we assume that the distributions of features and random shocks are known. We develop an effective heuristic policy by using Taylor expansion to approximate the retailer’s inventory cost. The resulting solution is closed-form, referred to as Taylor Approximation (TA) policy. We show that the TA policy is asymptotically optimal in the number of retailers. In the second step, we apply the linear quantile regression and kernel density estimation to the TA solution to obtain the data-driven policy called Data-Driven Taylor Approximation (DDTA) policy. We prove that the DDTA policy is consistent with the TA policy. A numerical study shows that the DDTA policy is very effective. Using a real data set provided by Fresh Hema, we show that the DDTA policy reduces the average cost by 11.0% compared with Hema’s policy. Finally, we show that the main results still hold in the cases of correlated demand features, positive lead times, and censored demand. This paper was accepted by J. George Shanthikumar, data science. Funding: Y. Yang acknowledges financial support from the NSFC [Grants 72125004, 71821002]. W. Zhou acknowledges financial support from the NSFC [Grants 72192823, 71821002]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.04241 .

Are There Variations in Personality Traits Between Men and Women? The Evidence is from Indonesian Residents

Non-cognitive skills, such as personality traits, play a vital role in various facets of human life due to their association with individual decision-making. However, personality traits can vary significantly among individuals globally, particularly between males and females. Hence, the primary objective of this study is to analyze the disparities in personality traits between men and women and to investigate how non-cognitive skills influence well-being. The study utilizes nationally representative data from Indonesia, encompassing 17,560 men and 18,825 women. Employing distinct mean t-tests and linear regression estimations, we observe that men tend to exhibit higher levels of openness, conscientiousness, and agreeableness in their personalities compared to women. Conversely, women demonstrate a propensity for higher level of extraversion and neuroticism. Moreover, our analysis reveals that male well-being is positively correlated with extraversion, while female well-being is linked to openness and extraversion traits. These findings underscore the significance of considering personality attributes in crafting economic development policies. Presently, policymakers in emerging nations like Indonesia heavily prioritize cognitive skills such as technical training and technological innovation. This study suggests that incorporating residents' non-cognitive talents into policy-making could potentially lead to enhanced achievements and progress.

Performance Evaluation of Predictive Models for Coconut Crop Production in Karnataka Using Weather Parameters

Coconut farming is a major industry in Karnataka that helps the state's agriculture sector. Karnataka is the third-largest coconut producer in India, behind Tamil Nadu and Kerala, with 3.38 billion coconuts produced on 0.42 million hectares of coconut agriculture in 2019–20, yielding an average of 8,095 nuts per hectare. These statistics come from the Directorate of Economics and Statistics. The production trends of the coconut crop in Karnataka are assessed in this study using linear, cubic, exponential, and log-logistic models. The best-fitting model is the one with the lowest Root Mean Square Error (RMSE). Between 1950 and 2019, the area under coconut crops grew cubically, while production showed loglogistic model to be the best fit, according to our data. Furthermore, we evaluate predictive models with coconut production as the dependent variable and independent factors including area, rainfall, temperature (both greatest and lowest recorded), and relative humidity. For this assessment, Stepwise MLR (SMLR) and Multiple Linear Regression (MLR) methods are used. Notably, the minimum temperature (T) and relative humidity (RH) have negative correlations with coconut production according to both stepwise regression estimates and maximum linear regression (MLR) estimates. These results imply that the minimum temperature in Karnataka and relative humidity have an inverse association with coconut crop productivity.

Functional Linear Regression: A Case Study from the Food Industry

A problem of analyzing functions in statistics is a very recent phenomenon that has been extensively investigated. The functionality in observed data, that is, when data are generated through a process naturally described as functional, occurs in many areas of sciences. For example, time series in financial engineering, imagining records in medicine, or spectrometric wavelengths in chemometrics can be considered as a discrete approximation of a continuous set of mathematical objects. In the initial step, observed real-valued data are transposed into functional data by smoothing or interpolation technique. These functional objects can then be directly used in the framework of statistical regressions. The aim of this paper is to show an estimation of the functional linear regression, where the observed covariate is functional, and its corresponding response is scalar. In the empirical study, we utilize spectrometric data with an objective to predict the fat content in a meat sample given the spectrum of absorbances, which are recorded through the infrared analyzer, by using the estimated functional linear regression. The functional regression coefficient is estimated through the basis spline expansion, for which we evaluate a different number of basis splines and propose the best predictor estimator.

A Comparison Between Local Linear Regression and K- Nearest Neighbor Methods in Estimating the Nonparametric Regression Model with Simulation and Application

In this paper, the nonparametric regression model was estimated using two common estimation methods (Local Linear Regression Estimator (LLRE) and K-Nearest Neighbor Estimator (KNNE). The simulation process was conducted using the statistical programming language, R, for these methods, and they were compared using the Average Root Mean Squares Error (ARMSE) criterion, with three sample sizes (50, 100, 150), three levels of error variance (0.3, 0.7, 1) and two non-linear models. The results for the first model showed the superiority of the nearest neighbor estimator (KNNE) method in most cases, and for the second model the local linear regression estimator (LLRE) was superior at all sample sizes and for all levels of variance. On the applied side, the effect of hemoglobin (Hb) on the packed cell volume (PCV) of 150 patients with chronic kidney disease was studied. The estimation process was made using the two methods, and the comparison between them was made using the root mean square error (RMSE) criterion. It was shown through the results of the applied side that the preference was for the nearest neighbor estimator (KNNE) method.

Assessment of Government Liberalization Policy and the Growth in Nigeria’s Trade Industry

Following the outbreak of COVID-19 and the contraction of government revenue, it has become increasingly difficult for government to continue with “subsidy oriented” policies hence, this paper evaluates Nigeria’s liberalization policies as a major drivers of trade growth in the country. A quantitative approach was adopted in this paper. Data used in this paper were secondary data sourced from World bank indicators and Central Bank of Nigeria Statistical Bulletin (1980-2018 version). This paper used Error Correction Classical Linear Regression estimate to analyse the data. The result of the study showed that changes in the key liberalization policy variables have significant influence on output growth but not statistically significant to trade growth. Industrialization oriented policies was therefore recommended for Nigeria to actualize her dreams of being an industrialized country, a strong and competitive institution that foster a sustainable industrial sector through policy is also advised. This study adds to the limited available evidence concerning the contribution of government liberation policy on the growth of Nigeria trade sector. It stands as the first to consider trade openness, government liberation policy and the Nigeria industrial sector. &#x0D; &#x0D; Received: 18 June 2023 / Accepted: 15 February 2024 / Published: xx March 2024

Agricultural Price Fluctuation and Its Effects on Small-Scale Farmer Income: A Case of Maize Crop in Kilolo District

This study examined the agriculture crop price fluctuation and its effects on small-scale farmer income in Tanzania, specifically in Kilolo District in Iringa Region. The study sampled 90 respondents, where 84 respondents were for structured questionnaire and 6 for interviews. Simple random sampling and purposive sampling techniques were used to collect data. The mixed-methods data were analysed using linear regression estimation techniques, estimated Ordinary Least Squares estimator, and content analysis for qualitative data. The study revealed that Agriculture cash crops positively influence the yearly income of small-scale farmers, signifying that increased engagement in cash crop cultivation contributes to higher income levels for these farmers. Conversely, the study unveiled the negative implications of crop price fluctuations on small-scale farmers’ yearly income, demonstrating that erratic price variations can substantially challenge their economic stability. These findings were robustly supported by statistical analyses, including the t-test and regression analysis, which underscored the significance of Agriculture cash crops and crop price fluctuations as determinants of small-scale farmers’ yearly income. The adjusted R² value of 0.607 further substantiated these results, signifying that the independent variables, Agriculture cash crops, and crop price fluctuations account for approximately 60.7% of the variation observed in the dependent variable. In such a sense, small-scale farmers’ yearly income is determined by agricultural cash crops done by small-scale farmers and the crop prices fluctuation in the markets. There is a need for marketing officers to provide marketing information to small-scale farmers from time to time for small-scale farmers to be aware of cash crop price fluctuations and what kind of cash crops will have a higher market value, hence lowering the level of price fluctuation in the markets. Such a move would increase small-scale farmers’ yearly income in Kilolo District

DETERMINANTS OF FINANCIAL DISTRESS: REVIEW OF THE ASPECTS OF PROFITABILITY, LIQUIDITY, LEVERAGE, AND ACTIVITY

This research aims to determine the determinants of financial distress in wholesale and retail trade subsector companies listed on the Indonesian Stock Exchange. In this research, financial distress is measured by Zscore, profitability is measured by Return on Assets, liquidity is measured by Current Assets, Debt measures leverage to Assets Ratio, and Total Assets Turnover measures activity. The research methods used in this research are descriptive and verification methods. The sample for this research was 29 companies obtained based on a purposive sampling technique with a five-year research period of 5 years, so the observation data totalled 145. The statistical analysis used is a linear regression analysis of panel data with time lag using the Eviews 10—Tektik program panel data linear regression estimation using the Fixed Effect Model. The coefficient test results show that profitability hurts financial distress, liquidity does not affect financial distress, leverage hurts negatively, and activity positively affects financial distress.

Data-driven strategies for accurate volume-to-biomass conversions

ABSTRACT This document presents a method for estimating conversion factors from volume to green weight and from volume to oven dry weight using ratio and linear and nonlinear regression estimators, and the results of its use in nine raw materials, including roundwood from harvest and sawmill offcuts. Although no differences were detected in either the value or accuracy of the linear factors, the linear estimator tended to have slightly higher accuracy than the ratio estimator. Linear conversion factors obtained using regression through the origin were accurate, especially those for roundwood. In general, the conversion factors from volume to green weight were more precise than those from volume to dry weight. Conversion factors from volume to dry weight are useful only in situations in which the moisture range of application is like that used for obtaining those average factors. In addition, given that the green volume-weight factor can be adjusted to the observed moisture in the raw material for which dry weight is intended to be determined, it is advisable to concentrate efforts on this type of factor in future studies. Nonlinear regression estimators are an alternative to linear regression estimators through the origin for determining conversion factors from volume to dry weight. In all the analyzed raw materials, a decrease in the conversion factor from volume to dry weight was observed as the moisture content increased. This is an unexpected outcome that cannot be explained by the variables recorded in this study.

Performance comparison between maximum likelihood estimation and variational method for estimating simple linear regression parameter

Variational estimation method is a deterministic approximation technique which involves Bayesian framework while giving a point estimate instead of the usual Bayesian interval estimation. The linear regression model, which has always been a popular model, can benefit from the implementation of variational estimation method. In this paper, the theoretical basis on why variational method can reduce overfitting in linear regression is reviewed. Based on the review, in theory, variational method is more robust to overfitting than MLE. This paper also performed a simulation study. The simulation is done in a manner such that the simulation represents the situation of predicting for new or hidden data. The simulation starts from generating random explanatory data and generates the appropriate response data based on linear regression equation. Then, the randomly generated data is used to estimate the linear regression parameters. The simulation is performed to compare the parameters estimation results from variational method with the method of MLE. The comparison is done using the estimation values and the squared differences between true parameters value and the estimates. Empirical findings show that both methods have relatively close estimate values. It can be seen as the simulation study concludes that both variational and ML yield rather close parameters estimates for simple linear regression case. The estimates closeness gets more obvious as the sample size grows. The study also found that Variational method has performs better in terms of parameters estimation in linear regression when the sample size is small or the data has large variance.

Variance estimation in high-dimensional linear regression via adaptive elastic-net

Variance estimation in high-dimensional linear regression via adaptive elastic-net