Regression Estimates Research Articles

Wavelet estimation for the nonparametric additive model in random design and long-memory dependent errors

We investigate the nonparametric additive regression estimation in random design and long-memory errors and construct adaptive thresholding estimators based on wavelet series. The proposed approach achieves asymptotically near-optimal convergence rates when the unknown function and its univariate additive components belong to Besov space. We consider the problem under two noise structures; (1) homoskedastic Gaussian long memory errors and (2) heteroskedastic Gaussian long memory errors. In the homoskedastic long-memory error case, the estimator is completely adaptive with respect to the long-memory parameter. In the heteroskedastic long-memory case, the estimator may not be adaptive with respect to the long-memory parameter unless the heteroskedasticity is of polynomial form. In either case, the convergence rates depend on the long-memory parameter only when long-memory is strong enough, otherwise, the rates are identical to those under i.i.d. errors. In addition, convergence rates are free from the curse of dimensionality.

On the Most Effective Use of Continuous Auxiliary Variables in Regression Estimation in Survey Sampling

SummaryAuxiliary variables with known population totals are extensively used in survey sampling to construct generalised regression (GR) estimators or optimal regression (OR) estimators of totals or means of study variables. This article explores the possibility of improving the efficiency of such estimators when continuous auxiliary variables are used in the regression estimation jointly with appropriate power functions of them, provided that the values of the auxiliary variables are known for all units in the population. The efficiency gain is determined analytically in the case of the OR estimator. A practical criterion for choosing the power functions that maximise the efficiency gain, involving the coefficient of determination in the regression fit of the study variable, is proposed for both the OR estimation and the more practicable, but generally less efficient, GR estimation. Furthermore, the effect of adding a power function of a continuous auxiliary variable in regression estimation is investigated when this variable is also used at the design stage. A simulation study shows that the joint use of a continuous auxiliary variable and a power function of it chosen according to the proposed criterion may improve considerably the efficiency of OR estimation, and much more the efficiency of GR estimation.

Financial leverage, percentage of female borrowers and financial sustainability of microfinance institutions

PurposeThis paper examines the influence of financial leverage on the financial sustainability of microfinance institutions (MFIs) and the moderating role of the percentage of female borrowers (PFB).Design/methodology/approachThe study uses a global sample of 646 MFIs drawn from the World Bank Mix Market and panel data for 2010–2018. The study employs ordinary least squares (OLS) and the one-step system generalized method of moments (SGMM) as regression estimation methods.FindingsThe findings of this study reveal that financial leverage and the PFB have a negative and significant effect on financial sustainability. The findings further show that the interaction between financial leverage and the PFB positively affects the financial sustainability of MFIs.Practical implicationsThe findings inform MFIs' managers on the adverse effect of financial leverage and the PFB in their quest for financial sustainability. The findings also demonstrate that MFIs can leverage female borrowers to reverse the adverse effect of financial leverage on financial sustainability of MFIs.Originality/valuePrevious studies examined the direct effect of financial leverage and reported incongruent results. Because female borrowers are at the epicenter of MFI lending, this study fills the gap in the literature by examining whether the proportion of female borrowers moderates the relationship between financial leverage and MFIs' financial sustainability using a global dataset.

The Non-Accelerating Infection Rate of Unemployment as an alternative to the Non-Accelerating Inflation Rate of Unemployment for the period of the COVID-19 pandemic: The example of the industries of the Czech Republic

The objective of this paper is to use a meso-level empirical analysis to confirm the usefulness of the concepts of the Non-Accelerating Inflation Rate of Unemployment (NAIRU) and the Non-Accelerating Infection Rate of Unemployment (NARRU). The atypically negative values are consistent with the findings in an economy in transition and during a global financial and economic recession. A higher value of NARRU than NAIRU signals that the intensity of the government's counter-pandemic measures is inadequate to match the depth of the COVID-19 pandemic's impact on the labour market. The lower value of the positive gap between unemployment for the NAIRU concept versus that of NARRU implies that the impact of counter-pandemic measures on the labour market was smaller than that required by the pandemic burden on society. The government of the Czech Republic should have implemented a stricter lockdown or ensured a sufficient supply of respirator masks. Localised negative Phillips curve (PC) slopes at the meso level confirm the suitability of applying the NAIRU and NARRU concepts during the period of COVID-19. Ad hoc analysis and regression estimation of PC shifts on a rolling basis and a Break model in the last period analysed (April 2021 to March 2022) found that the PC in the first phase (April to August 2021) and in the fourth phase (February to March 2022) typically had a negative slope in both concepts. In the second phase (September and October 2021) and third phase (November 2021 to January 2022), the analysis located a PC with an atypically positive slope, which is related to fluctuations associated with the pandemic and measures against it.

Robust correlation coefficients that deal with bad leverage points

Consider the usual linear regression model. A well-known concern is that a bad leverage point, which is a type of outlier, can result in a poor fit to the bulk of the data, even when using any one of many robust regression estimators. In terms of measuring the strength of the association, bad leverage points can mask a strong association among the bulk of the data, and bad leverage points can suggest a strong association when in fact there is, in general, a weak association. This issue can be addressed by using an analog of Pearson’s correlation that is eliminates outliers. But this approach can have a negative impact because it eliminates what are known as good leverage points. The paper suggests a class of robust measures of association that deals with this issue.

Testing for coefficient distortion due to outliers with an application to the economic impacts of climate change

Outlying observations can bias regression estimates, requiring the use of outlier-robust estimators. Comparing robust estimates to those obtained using ordinary least squares (OLS) is a common robustness check, however, such comparisons have been mostly informal due to the lack of available tests. Here we introduce a formal test for coefficient distortion due to outliers in regression models. Our proposed test is based on the difference between OLS and robust estimates obtained using a class of Huber-skip M-type estimators (such as Impulse Indicator Saturation or Robustified Least Squares). We show that our distortion test has an asymptotic chi-squared distribution by establishing the asymptotics of the corresponding Huber-skip M-estimators using an empirical process Central Limit Theorem recently developed in the literature. The test is valid for cross-sectional, as well as panel, and stationary or deterministically-trending time series models. To improve finite sample performance and to alleviate concerns on distributional assumptions, we explore several bootstrap testing schemes. We apply our outlier distortion test to estimates of the macro-economic impacts of climate change allowing for adaptation.

DISSECTING RACIAL DIFFERENCES IN THE PERCEPTION OF DOMAIN-SPECIFIC LIFE SATISFACTION

Abstract This study examines factors contributing to racial differences in domain-specific life satisfaction. Our study offers a major contribution to prior research that has tended to pay greater attention to psychological well-being outcomes such as life satisfaction which do not directly pertain to material circumstances of older adults. Those studies generally report better life satisfaction and fewer depressive symptoms among Black older adults than their White counterparts. Our study is based on 409 randomly selected older adults from Cleveland (mean age 79 and 25% African American). We considered socioeconomic status, stressors such as recent negative life events and coping resources such as religiosity to understand their impact on domain specific life- satisfaction (e.g., satisfaction with neighborhood, housing, and standard of living). Our ordinary least squares regression estimates suggest that, on average, Black older adults reported lower levels of domain-specific life satisfaction (b=-0.81, p< 0.01) compared to their White counterparts, despite having stronger social support and being more religious. Even though social support and religiosity did not contribute to racial differences in domain specific life satisfaction, the inclusion of education, income, and recent negative life events fully explain such differences. Findings indicate that interrelated factors such as socioeconomic disadvantage and greater life adversities such as negative life events contribute to lower domain life satisfaction among Black older adults. This finding highlights the need for further exploration of factors shaping material-related well-being outcomes for Black older adults. Our study informs future policies addressing racial well-being inequities.

FAKTOR-FAKTOR YANG MEMENGARUHI PILIHAN PETANI MELAKUKAN ALIH FUNGSI LAHAN USAHATANI KAKAO MENJADI USAHATANI CENGKEH

This study aims to determine and analyze the factors that influence the choice of farmers to transfer cocoa to clove farming in Ponggiha Village, Lasusua District, North Kolaka Regency. The number of samples used in this study were 70 people.the sample used is farmers who make the transition of commodities either partially or wholly from cocoa farming toclove farming. The data analysis method used in this study is logistic regression. The results showed that from the logistic regression estimation it was known that the factors that influenced the conversion of cocoa farming into cloves were land area, age of cocoa plants, cocoa production cost, cocoa production, cocoa farming income and cocoa farming experience.

Effect of Discussion Method on the Performance of Mathematics Students in Public Secondary Schools in Rwanda: A Case of Karongi District

Purpose: In class discussion method can be used while studying Mathematics as it is intended to be a free give and take between teacher and students and among students on the current topic of concern in the course. It is characterized by probing questions from the teacher designed to elicit student interpretations, opinions, and questions. The purporse of study is to assess the effect of group discussion method on the performance of Mathematics students in public day Schools in Rwanda” A case of Karongi District’.
 Materials and Methods: The study used descriptive research design. Stratified sampling was used to determine a sample of 6 schools among other public secondary schools in Bwishyura sector in Karongi District. The head teachers of the sampled schools were purposively selected to participate in the study. Simple random sampling technique was further applied to obtain Mathematics teachers and students of senior 4,5 and 6, making 783 target students. The study used a sample of 230 respondents from the sampled schools. The collected data was analyzed using descriptive statistics, correlation, and regression analysis through the statistical package for social science (SPSS) version 21. The data collected were analyzed using multiple linear regression analysis. The probability value of the regression estimate was used in testing the hypotheses of the study.
 Findings: The result of the regression analysis indicates that Demonstration Method has a positive effect on Performance of Mathematics students in public secondary Schools in Karongi District and the relationship is statistically significant (p=>0.05). Results showed significant positive relationship between discussion method and the performance of Mathematics students (r = 0.860; p= 0.000). Multiple linear regression analysis showed that discussion method contributed to 72% of variation on the performance, hence plays a vital role in performance of Mathematics students in public secondary schools in Karongi District.
 Implications to Theory, Practice and Policy: The study recommended that Mathematics teachers should be trained and encouraged in the use of discussion teaching method and the framework for planning lessons and teaching mathematics in our secondary schools should be on group discussion teaching strategy. Moreover, it was also useful to enable learners to take part in discussion, reflection, feedback and to consolidate learning, clarify understanding and explore ideas and concepts.

Explaining employment and environmental degradation nexus with environmental employment curve (EEC): A sector-wide threshold estimation for China

China is the world's largest emitter of carbon dioxide and has experienced significant economic growth in the last decade. The country also has significant employment capacity and has witnessed rapid economic growth in the recent years. This study aims to investigate the existence of the environmental employment curve (EEC) in China's main economic sectors. This study examines the relationship between carbon emissions, economic growth, energy consumption, renewable energy consumption and employment (industry, services, agriculture) in China over the period 1990–2020 using threshold autoregressive regression estimation. According to the EEC hypothesis, there exists an inverted U-shaped relationship between environmental pollution and employment rate. Empirical results show that the EEC hypothesis seems to hold in China. Further, an analysis of the impact of employment on environmental pollution in China's three main sectors – industry, service and agriculture – shows that the EEC hypothesis cannot be rejected in the industrial and the agricultural sectors. In addition, empirical results show that a 1% increase in energy consumption leads to a 0.92% increase in carbon emissions, while the use of renewable energy leads to a 0.18% decrease. These results suggest that investments in green energy transformation do not cause employment loss. Hence, it is important to drive policies that promote investments in renewable and clean technologies, stimulate research in renewable energy-related institutions and support public-private partnerships. Additionally, to generate employment without compromising on the environment or prosperity, policymakers should choose to implement alternative technologies that are less polluting or do not produce any carbon emissions.

Finance Option and Growth Prospect of Production Based Firms in Nigeria: A Post-Financial Crisis Analysis

Purpose: Firms' funding strategies have grown more fluid and unpredictable since the global financial crisis of 2007, particularly for developing nations like Nigeria. This research looked at the years 2010–2022, after the financial crisis, to see how different types of financing affected the expansion opportunities for production-based businesses in Nigeria. In particular, the research looked at how different types of financing—equity, debt, and retained earnings—impacted the expansion of assets for Nigerian production-based businesses. Design/Methodology/Approach: A panel regression estimate was used to evaluate the data gathered from the financial accounts of fifteen production-based enterprises. Findings: The most effective and reliable estimate, which was a random effect, showed that total equity had a positive and statistically significant influence on asset growth rate. Asset growth rates were negatively and insignificantly affected by total debt and retained earnings, according to the result. Compared to the internal option (retained earnings), this study found that the external source of finance option—particularly equity—has a strong propensity to accelerate the rate of expansion of a production-based corporation in Nigeria. Original/Value: A post-financial analysis of production-based enterprises is the innovative aspect of the study that aligns with Pecking Order Theory's postulates about the relationship between finance and economic prospects in developing nations like Nigeria. Practical implications: Consequently, producing organizations should include whole equity in their financing options for growth enhancement rather than seeing it as a last resort.

Automated image quality assessment for selecting among multiple magnetic resonance image acquisitions in the German National Cohort study

In magnetic resonance imaging (MRI), the perception of substandard image quality may prompt repetition of the respective image acquisition protocol. Subsequently selecting the preferred high-quality image data from a series of acquisitions can be challenging. An automated workflow may facilitate and improve this selection. We therefore aimed to investigate the applicability of an automated image quality assessment for the prediction of the subjectively preferred image acquisition. Our analysis included data from 11,347 participants with whole-body MRI examinations performed as part of the ongoing prospective multi-center German National Cohort (NAKO) study. Trained radiologic technologists repeated any of the twelve examination protocols due to induced setup errors and/or subjectively unsatisfactory image quality and chose a preferred acquisition from the resultant series. Up to 11 quantitative image quality parameters were automatically derived from all acquisitions. Regularized regression and standard estimates of diagnostic accuracy were calculated. Controlling for setup variations in 2342 series of two or more acquisitions, technologists preferred the repetition over the initial acquisition in 1116 of 1396 series in which the initial setup was retained (79.9%, range across protocols: 73–100%). Image quality parameters then commonly showed statistically significant differences between chosen and discarded acquisitions. In regularized regression across all protocols, ‘structured noise maximum’ was the strongest predictor for the technologists’ choice, followed by ‘N/2 ghosting average’. Combinations of the automatically derived parameters provided an area under the ROC curve between 0.51 and 0.74 for the prediction of the technologists’ choice. It is concluded that automated image quality assessment can, despite considerable performance differences between protocols and anatomical regions, contribute substantially to identifying the subjective preference in a series of MRI acquisitions and thus provide effective decision support to readers.

What drives public debt in SAARC countries? An empirical assessment

PurposeThis study aims to explore the determinants of public debt in selected South Asian Association for Regional Cooperation (SAARC) countries for 19 years, from 2001 to 2019.Design/methodology/approachUsing ordinary fixed and random effect models, the authors examine the role of internal and external factors in determining the composition of public debt. Furthermore, for robustness, they compare the results with two-stage least square (2SLS) regression estimates after considering the problem of endogeneity, overidentification, under-identification and weak instruments.FindingsThe findings show that among the selected macroeconomic variables, inflation, exchange rate and broad money have significant negative effects on the debt-GDP ratio. In contrast, military spending, corruption and interest rates appear to positively influence the same as per 2SLS results. From the policymaking perspective, SAARC countries should focus more on reducing military spending and make a concerted effort to augment investments in productive projects. Further, with strong fiscal consolidation and institutional quality, it is important to mitigate the frequent occurrence of corruption conundrums in emerging economies for the development of a transparent economic system.Originality/valueThe study is distinct from previous studies in two ways. First, to the best of the authors’ knowledge, there are no studies focusing on SAARC countries in the context of public debt. Second, the study expands the existing literature on public debt by taking into account both external and internal debts to decipher the within-country and cross-country determinants of debt accumulation. More specifically, this model considers accountability and transparency in the public sector, cross-border security challenges and benefits of globalization by including explanatory variables such as corruption, military expenditure spending and capital inflows.

Sales performance of direct food marketers: African American‐led operations

AbstractThis paper examines the impact of participation in direct marketing on the entire distribution of farm sales for African American (AA) operations using the unconditional quantile regression (UQR) estimator. Our analysis yields unbiased estimates of the unconditional impact of direct marketing on farm sales and reveals the heterogeneous effects that occur across the distribution of farm sales. The sales gap for AA‐led operations is about 11% across all quantiles when we control for farming and marketing experience along with key features of the farm operation such as crop choice and geographic location. The relative sales gap for AA operations declines across the distribution of farm sales and actually disappears for the largest operations. The network effect that we identify is associated with a sales premium for AA‐led operations relative to other operations engaged in direct marketing. Marketing experts and extension professionals can use this information to guide farmers who are considering initiating or expanding direct marketing activities.

Self-truncated sampling produces more moderate covariation judgment and related decision than descriptive frequency information: The role of regressive frequency estimation.

Covariation judgment underlies a diversity of psychological theories and influences various everyday decisions. Information about covariation can be learned from either a summary description of frequencies (i.e., contingency tables) or trial-by-trial experience (i.e., sampling individual instances). Two studies were conducted to investigate the impact of information learning mode (i.e., description vs. self-truncated sampling) on covariation judgment and related decision. In each trial under the description condition, participants were presented with a contingency table with explicit cell frequencies, whereas in each trial under the self-truncated sampling condition, participants were allowed to determine when to stop sampling instances and thus the actual sample size. To eliminate sampling error, an other-yoked (i.e., between-subject) design was used in this research so that cell frequencies shown in a trial under the description condition were matched with those experienced in a trial under the self-truncated sampling condition. Experiment 1 showed that the self-truncated sampling condition led to more moderate covariation judgments than the description condition (i.e., a description-experience gap). Experiment 2 demonstrated further that the same gap extended to covariation-related decisions in terms of relative contingent preference (RCP). Regressive frequency estimation under self-truncated sampling appeared to underlie the consistent gaps found in the two studies, whereas the impact of regressive diagnosticity (i.e., the same sample of instances was viewed as less diagnostic under description than under self-truncated sampling) or simultaneous overestimation and underweighting of rare instances under experience was not supported by the observed data. Future research might examine alternative accounts of the observed gaps, such as the impacts of belief updating and stopping rule under self-truncated sampling.

PENGARUH KEPEMIMPINAN, DISIPLIN DAN MOTIVASI TERHADAP KINERJA DOSEN FAKULTAS KEGURUAN DAN ILMU PENDIDIKAN

This research was conducted with the aim of determining the influence of leadership, discipline and motivation on lecturer performance at FKIP HKBP Nommensen Pematangsiantar University. With a sample of 53 Permanent Foundation Lecturers. The tool used is a questionnaire. All questionnaires tested are valid and reliable. This research is descriptive explanatory, namely research that aims to explain the position of the variables studied and the relationships between the variables. The leadership coefficient gives a value of 0.316, which means that if leadership gets better assuming other variables remain constant, lecturer performance will increase. The discipline coefficient gives a value of 0.343, which means that if discipline is higher, assuming other variables remain constant, lecturer performance will increase. The Motivation coefficient gives a value of 0.222, which means that if motivation becomes stronger assuming other variables remain constant, lecturer performance will increase. The results of the research show that the three independent variables (Leadership, Discipline and Motivation) have a positive and significant influence, both partially and simultaneously on the dependent variable (Lecturer Performance). Regression analysis is used to test the hypothesis regarding the partial influence of the independent variables on the dependent variable . Based on multiple regression estimates with the SPSS program, the results obtained are Y = 0.316 X1 + 0.343 X2 + 0.222 X3. From the results of the regression calculations, it can be seen that the coefficient of determination (adjusted R2) obtained is 0.57 or 57%.

The second-order bias and mean squared error of quantile regression estimators

The second-order bias and mean squared error of quantile regression estimators

Mathematical Modeling Using Multiple and Fully-Connected Linear Regressions

This article is devoted to the synthesis of traditional multiple linear regression models with fully connected linear regression models. It has been noted that these models, in a certain sense, complement each other — the disadvantages of multiple regression are compensated by the fully connected one, and the disadvantages of the fully connected one are compensated by the multiple one. Multiple regression estimates with partial multicollinearity of factors are unstable, and with complete multicollinearity they do not exist at all. There are no obstacles to the use of fully connected regression in such conditions, and its estimates, on the contrary, do not exist in the complete absence of linear dependence between factors. The estimated fully connected regression is an equation of a line in space, as opposed to a multiple regression, which is a hyperplane in space, so you can find estimates of a fully connected regression with only two observations at your disposal. An algorithm for estimating fully connected regressions using the maximum likelihood method is considered. Based on a sample size of 21, a multiple and fully connected linear regression of passenger rail traffic in the Irkutsk region was constructed, containing 23 input variables. During the construction process, it was possible to cope with multicollinearity and ensure protection of all input variables in the model. The constructed multiple and fully connected regression is adequate and fully satisfies the substantive meaning of the problem being solved, therefore its interpretation is given. Based on the results of the study, we can conclude that a combination of multiple and fully connected regressions can be very effective in solving data analysis problems.

Bayesian variable selection and estimation in binary quantile regression using global-local shrinkage priors

In this paper, we construct a Bayesian hierarchical model with global-local shrinkage priors for the regression coefficients, which includes the horseshoe prior and normal-gamma prior. This model is used for high-dimensional quantile regression models with dichotomous response data. We have developed an efficient sampling algorithm to generate posterior samplings for making posterior inference. We use a location-scale mixture representation of the asymmetric Laplace distribution. We assess the performance of the proposed methods through Monte Carlo simulations and two real-data applications in terms of parameter estimation and variable selection. Numerical results demonstrate that the proposed methods perform comparably with existing Bayesian methods under a variety of scenarios.

Statistical analysis of entropy generation on the Maxwell-graphene based nanofluid passing through a squeezing channel

The present research focuses on the analysis of the flow behavior of a Graphene and Ethylene Glycol based nanofluid under the influence of magneto hydrodynamics (MHD) between two parallel plates that are being compressed or moved apart, considering the effect of thermal radiation. The study extensively investigates the system’s energy efficiency through the utilization of the Bejan number. To simplify the governing partial differential equations, a similarity transformation is employed, leading to a set of coupled non-dimensional ordinary differential equations. The numerical solution is obtained using the shooting technique in conjunction with the fourth-order Runge-Kutta method. Additionally, a statistical method is utilized to analyze the quadratic regression estimation of skin friction coefficients and Nusselt numbers, aiming to comprehend the connection between the rate of heat transfer and emerging flow parameters. Furthermore, the study examines the influence of various flow parameters viz. Magnetic field, Deborah number, squeezing parameter, radiation and Eckert number on nanofluid velocity, temperature, entropy generation (quantified by the Bejan number), skin friction coefficient, and heat transfer rate. As a result, this research holds promising applications in the fields of biomedical engineering automobile engineering and lubrication technology.