Ordinary Least-squares Method Research Articles

PENGARUH SUBSIDI BBM TERHADAP IMPOR MINYAK BUMI PERIODE 1980 – 2013

The study on the effect of fuel subsidies toward oil import is a controversial topicdiscussions. This study will explore the effect of fuel subsidies on oil import by addingseveral independent variables, consist of; the number of vehichles, the exchange rateand inflation. Data use time series data from 1980-2013. The tool of analyze is OrdinaryLeast Squares Method (OLS).Based on the results show that the simultaneous testexplains that the fuel subsidies, the number of vehichles, the exchange rate, and inflationhave a significant effect on oil import. However partially, the variables of fuel subsidies,the number of vehichles, and the exchange rate have a positive and significant effecton oil import. Inflation does not affect on oil import. The coefficient of determinationuses Adjusted R-square test is about 98%. The implication of this study is governmentscan increase oil production Indonesia. The government should facilitate the licensing ofinvestment and rejuvenate the old oil wells. It aims to reduce Indonesia dependence onoil import so that it can save foreign exchange reserves.

Factors Affecting Milk Production:A Case Study in Andhra Pradesh

India ranks first in milk production, accounting for 18.5 per cent of world production, achieving an annual output of 146.3 million tonnes during 2014-15. Production of milk depends on various factors like feeding, breeding and management of the animals. It is evident that among the non-agricultural and allied activities of agricultural sector, dairying is providing gainful self-employment and generating the incomes to the rural masses. The cross breed variety of buffaloes is being reared in large number; as a consequence substantial improvement in milk yield is recorded. To study the milk yield and the dairying aspects of households in Prakasam district of Andhra Pradesh who are rearing the cross breed buffaloes, the data and information is elicited from the households rearing cross breed cattle in the two selected mandals, namely Martur and Cumbum. To study the factors influencing milk yield, the multiple linear regression model is adopted by the method of ordinary least square. The regression results for the total sample and also for sub-sample reveal that on an average 60 to 80 per cent of the variations in the value of milk yield per day per animal could be explained by fodder, green fodder and concentrates used per animal per day, and the age of the animal. Of these explanatory variables, fodder used per animal, green fodder used per animal per day are highly significant with positive impact and in terms of magnitude of impact on dependent variable, the concentrates used per animal per day is the highest. However, the explanatory variable, number of labour hours required per day did not show any significant impact on the dependent variable. The model fitted for various samples are valid as indicated by their respective p-values of the statistic.

A Peer-Relative Perspective of the Contract Year Phenomenon Using Bayesian Analysis

This research examines the contract year phenomenon in a new context, by evaluating player performance relative to other players, rather than the same player over time. Since ordinary least-squares (OLS) methods assume independent observations, results may be biased as player performance is dependent on other players. As such, a Bayesian approach to allow for non-independence among player performance is used to evaluate players in contract years relative to their peers. Using novel performance data of National Football League (NFL) players and controlling for player salary, position, experience, and team effects, the results show that contract year players under-perform relative to players not in a contract year. Furthermore, increasing salary exhibits a greater negative effect on the performance of contract year players. The findings suggest managers would get more performance from players, on average, by not having players play during a contract year. Subscribe to JASM

Debt Financing dan Dampaknya terhadap Perkembangan Usaha Mikro di Bogor

Micro and small enterprises (MSEs) have an important role in supporting the national economy. Nevertheless, MSEs still have a major problem in its development, called limited capital. The presence of BPRS which is part of sharia financial institutions is expected to provide financing assistance for MSEs. The financing provided by BPRS is not only given in the form of equity financing but also in the form of debt financing. This study analyzes the effect of debt financing on MSEs development and also the factors influenceing the income and profit of MSEs after getting debt financing. The method used in this research is paired sample t test and Ordinary Least Square (OLS). The result of paired sample t test showed that the variable of earnings and profit of MSEs have significant change after getting financing. Furthermore, the result of analysis with OLS method showed that labor variable, amount of financing, profit rate, gender dummy, and education dummy have a significant effect to earnings and profit of MSEs

A robust regression method based on exponential-type kernel functions

Robust regression methods appear commonly in practical situations due the presence of outliers. In this paper we propose a robust regression method that penalize bad fitted observations (outliers) through the use of exponential-type kernel functions in the parameter estimator iterative process. Thus, the weights given to each observation are updated at each iteration in order to optimize a suitable objective function. The convergence of the parameter estimation algorithm is guaranteed with a low computational cost. Its performance is sensitive to the choice of the initial values for the vector of parameters of the regression model as well as to the width hyper-parameter estimator of the kernel functions. A simulation study with synthetic data sets revealed that some width hyper-parameter estimators can improve the performance of the proposed approach and that the ordinary least squares (OLS) method is a suitable choice for the initial values for the vector of parameters of the proposed regression method. A comparative study between the proposed method against some classical robust approaches (WLS, M-Estimator, MM-Estimator and L1 regression) and the OLS method is also considered. The performance of these methods are evaluated based on the bias and mean squared error (MSE) of the parameter estimates, considering synthetic data sets with X-space outliers, Y-space outliers and leverage points, different sample sizes and percentage of outliers in a Monte Carlo framework. The results suggest that the proposed approach presents a competitive performance (or best) in outliers scenarios that are comparable to those found in real problems. The proposed method also exhibits a similar performance to the OLS method when no outliers are considered and about half of the computational time if compared with MM-Estimator method. Applications to real data sets corroborates the usefulness of the proposed method.

Geographically weighted regression for modelling the accessibility to the public hospital network in Concepción Metropolitan Area, Chile.

Accessibility models in transport geography based on geographic information systems have proven to be an effective method in determining spatial inequalities associated with public health. This work aims to model the spatial accessibility from populated areas within the Concepción metropolitan area (CMA), the second largest city in Chile. The city's public hospital network is taken into consideration with special reference to socio-regional inequalities. The use of geographically weighted regression (GWR) and ordinary least squares (OLS) for modelling accessibility with socioeconomic and transport variables is proposed. The explanatory variables investigated are: illiterate population, rural housing, alternative housing, homes with a motorised vehicle, public transport routes, and connectivity. Our results identify that approximately 4.1% of the population have unfavourable or very unfavourable accessibility to public hospitals, which correspond to rural areas located south of CMA. Application of a local GWR model (0.87 R2 adjusted) helped to improve the settings over the use of traditional OLS methods (multiple regression) (0.67 R2 adjusted) and to find the spatial distribution of both coefficients of the explanatory variables, demonstrating the local significance of the model. Thus, accessibility studies have enormous potential to contribute to the development of public health and transport policies in turn to achieve equality in spatial accessibility to specialised health care.

Elastic-Net Copula Granger Causality for Inference of Biological Networks.

AimIn bioinformatics, the inference of biological networks is one of the most active research areas. It involves decoding various complex biological networks that are responsible for performing diverse functions in human body. Among these networks analysis, most of the research focus is towards understanding effective brain connectivity and gene networks in order to cure and prevent related diseases like Alzheimer and cancer respectively. However, with recent advances in data procurement technology, such as DNA microarray analysis and fMRI that can simultaneously process a large amount of data, it yields high-dimensional data sets. These high dimensional dataset analyses possess challenges for the analyst.BackgroundTraditional methods of Granger causality inference use ordinary least-squares methods for structure estimation, which confront dimensionality issues when applied to high-dimensional data. Apart from dimensionality issues, most existing methods were designed to capture only the linear inferences from time series data.Method and ConclusionIn this paper, we address the issues involved in assessing Granger causality for both linear and nonlinear high-dimensional data by proposing an elegant form of the existing LASSO-based method that we call “Elastic-Net Copula Granger causality”. This method provides a more stable way to infer biological networks which has been verified using rigorous experimentation. We have compared the proposed method with the existing method and demonstrated that this new strategy outperforms the existing method on all measures: precision, false detection rate, recall, and F1 score. We have also applied both methods to real HeLa cell data and StarPlus fMRI datasets and presented a comparison of the effectiveness of both methods.

EmpiricalMw–ML,mb, andMsConversions in Western China

Local magnitude ( M L), body‐wave magnitude ( m b), and surface‐wave magnitude ( M s), which are saturated at certain values and may lead to an incorrect energy estimation of a large earthquake, are largely used in quantifying the size of an earthquake in western China. Based on the catalog from the China Earthquake Data Center (CEDC) and moment magnitude ( M w) provided by the Global Centroid Moment Tensor (Global CMT) Project, we test the general orthogonal regression (GOR) and the ordinary least‐squares (OLS) methods in M w– M L, M s, and m b conversions for earthquakes in three different tectonic structures of western China. For M L and m b, linear trends vary according to tectonic structures, which implies different body‐wave attenuations in the three structures. For M s, the results are similar, whether using the GOR or the OLS method, but slopes of regression lines are not close to 1, which indicates routine misestimates of M s in western China. The GOR slopes are uniformly larger than the OLS slopes in all magnitude conversions. Standard deviations are between 0.06 and 0.13 for the GOR method but are between 0.12 and 0.25 for the OLS. Thus, the GOR is found to be superior to the OLS method and its use is recommended. Conversions of different magnitudes to M w not only imply different patterns of seismic‐wave attenuations but will also benefit immediate assessment of seismic damage after occurrence of a destructive earthquake in the future. Online Material: Earthquake catalog and tables showing differences between magnitude estimates for M w, M L, m b, and M s.

Direct-on-Filter α-Quartz Estimation in Respirable Coal Mine Dust Using Transmission Fourier Transform Infrared Spectrometry and Partial Least Squares Regression.

In order to help reduce silicosis in miners, the National Institute for Occupational Health and Safety (NIOSH) is developing field-portable methods for measuring airborne respirable crystalline silica (RCS), specifically the polymorph α-quartz, in mine dusts. In this study we demonstrate the feasibility of end-of-shift measurement of α-quartz using a direct-on-filter (DoF) method to analyze coal mine dust samples deposited onto polyvinyl chloride filters. The DoF method is potentially amenable for on-site analyses, but deviates from the current regulatory determination of RCS for coal mines by eliminating two sample preparation steps: ashing the sampling filter and redepositing the ash prior to quantification by Fourier transform infrared (FT-IR) spectrometry. In this study, the FT-IR spectra of 66 coal dust samples from active mines were used, and the RCS was quantified by using: (1) an ordinary least squares (OLS) calibration approach that utilizes standard silica material as done in the Mine Safety and Health Administration's P7 method; and (2) a partial least squares (PLS) regression approach. Both were capable of accounting for kaolinite, which can confound the IR analysis of silica. The OLS method utilized analytical standards for silica calibration and kaolin correction, resulting in a good linear correlation with P7 results and minimal bias but with the accuracy limited by the presence of kaolinite. The PLS approach also produced predictions well-correlated to the P7 method, as well as better accuracy in RCS prediction, and no bias due to variable kaolinite mass. Besides decreased sensitivity to mineral or substrate confounders, PLS has the advantage that the analyst is not required to correct for the presence of kaolinite or background interferences related to the substrate, making the method potentially viable for automated RCS prediction in the field. This study demonstrated the efficacy of FT-IR transmission spectrometry for silica determination in coal mine dusts, using both OLS and PLS analyses, when kaolinite was present.

Predicting Loblolly Pine Site Index from Soil Properties Using Partial Least-Squares Regression

Data from 15 sites with loblolly pine (Pinus taeda L.) plantations located in the southeastern United States were used to predict site index (SI) from soil physical and chemical properties from the top 15 cm of the mineral soil. Two modeling approaches were used to predict SI from soil properties. First, the ordinary least-squares method of multiple linear regression was used, which selected calcium, potassium, and sand percentage as the significant predictor variables. Second, partial least-squares regression was used, which selected total nitrogen, carbon, calcium, magnesium, and sand percentage as the significant predictor variables. The partial least-squares regression approach addressed multicollinearity in the data and produced a better model to predict SI. The partial least-squares regression model explained 77% of the variation in SI.

Identification and characterization of the hydraulic unit in an anti-lock brake system

The present paper identifies single-input single-output linear minimum-order models for the hydraulic unit of an anti-lock brake system in the increase mode and the decrease mode. The input variable and the output variable in the models for the increase mode and the decrease mode are the pressure of master cylinder and the pressure of the brake caliper respectively. In the process of model identification, excitation signals are designed by introducing a novel method to change the states of the solenoid supply valve and the solenoid discharge valve and to generate different operating modes. It is shown that the excitation signals are persistently exciting of a certain order and cover the bandwidth of the system. In this study, the structure models (the output error model, the autoregressive with exogenous input model, the autoregressive moving-average with exogenous input model and the box Jenkins model) are identified for the increase mode and the decrease mode by employing the experimental data of a test automobile and using the ordinary least-squares method and the prediction error method. After validation of the model outcome by comparison with the experimental results, the best models are presented for the increase mode and the decrease mode of the hydraulic unit in a test automobile.

Latent Instrumental Variables: A Critical Review

This paper considers the estimation problem in linear regression when endogeneity is present, that is, when explanatory variables are correlated with the random error, and also addresses the question of a priori testing for potential endogeneity. We provide a survey of the latent instrumental variables (LIV) approach proposed by Ebbes (2004) and Ebbes et al. (2004, 2005, 2009) and examine its performance compared to the methods of ordinary least squares (OLS) and IV regression. The distinctive feature of Ebbes’ approach is that no observed instruments are required. Instead ‘optimal’ instruments are estimated from data and allow for endogeneity testing. Importantly, this Hausman-LIV test is a simple tool that can be used to test for potential endogeneity in regression analysis and indicate when LIV regression is more appropriate and should be performed instead of OLS regression. The LIV models considered comprise the standard one where the latent variable is discrete with at least two fixed categories and two interesting extensions, multilevel models where a nonparametric Bayes algorithm completely determines the LIV’s distribution from data. This paper suggests that while Ebbes’ new method is a distinct contribution, its formulation is problematic in certain important respects. Specifically the various publications of Ebbes and collaborators employ three distinct and inequivalent statistical concepts exchangeably, treating all as one and the same. We clarify this and then discuss estimation of returns of education in income based on data from three studies that Ebbes (2004) revisited, where ‘education’ is potentially endogenous due to omitted ‘ability.’ While the OLS estimate exhibits a slight upwards bias of 7%, 8%, and 6%, respectively, relative to the LIV estimate for the three studies, IV estimation leads to an enormous bias of 93%, 40%, and -24% when there is no consensus about the direction of the bias. This provides one instance among many well known applications where IVs introduced more substantial biases to the estimated causal effects than OLS, even though IVs were pioneered to overcome the endogeneity problem. In a second example we scrutinize the results of Ferguson et al. (2015) on the estimated effect of campaign expenditures on the proportions of Democratic and Republican votes in US House and Senate elections between 1980 and 2014, where ‘campaign money’ is potentially endogenous in view of omitted variables such as ‘a candidate’s popularity.’ A nonparametric Bayesian spatial LIV regression model was adopted to incorporate identified spatial autocorrelation and account for endogeneity. The relative bias of the spatial regression estimate as compared to the spatial LIV estimate ranges between -17% to 18% for the House and between -25% to 7% for the Senate.

Curvas de índices de local em povoamentos de eucalipto obtidas por regressão quantílica

Resumo: O objetivo deste trabalho foi obter curvas de índice de local, em povoamentos de eucalipto, por meio de regressão quantílica, e compará-las às obtidas com o uso de regressões ajustadas pelo método de mínimos quadrados ordinários. A regressão quantílica pode ser utilizada para a geração de curvas de índice de local, especialmente em presença de dados discrepantes. O método gera um feixe de curvas mais bem ajustadas aos dados observados, em comparação ao feixe gerado com as estimativas de mínimos quadrados ordinários, seja pelo método da curva-guia (curvas anamórficas), seja pela geração de curvas polimórficas (com o uso de diferentes percentis).

The Effect of Regional Gross Domestic Product (GDP) and Development Expenditure on Employment in the District Luwu

This study aims to assess the economic growth and the factors that influence the economic growth in the local government system by increasing the production of goods and services measured by Gross Domestic Product (GDP) and the policy of government expenditure (development) that can directly drive growth to finance economic development in the field of social economy and the public, both the development of physical and non-physical. This study uses multiple regression analysis by the least squares method (Method of Ordinary Least Square) OLS. These results indicate that the budget Revenue Expenditure Luwu, particularly in development spending has been a major contribution to regional development Luwu. With increasing economic growth and rising Luwu regency Regional Budget Builders Luwu district each year, then it will affect the employment opportunities for local people Luwu.

FW: An R Package for Finlay-Wilkinson Regression that Incorporates Genomic/Pedigree Information and Covariance Structures Between Environments.

The Finlay–Wilkinson regression (FW) is a popular method among plant breeders to describe genotype by environment interaction. The standard implementation is a two-step procedure that uses environment (sample) means as covariates in a within-line ordinary least squares (OLS) regression. This procedure can be suboptimal for at least four reasons: (1) in the first step environmental means are typically estimated without considering genetic-by-environment interactions, (2) in the second step uncertainty about the environmental means is ignored, (3) estimation is performed regarding lines and environment as fixed effects, and (4) the procedure does not incorporate genetic (either pedigree-derived or marker-derived) relationships. Su et al. proposed to address these problems using a Bayesian method that allows simultaneous estimation of environmental and genotype parameters, and allows incorporation of pedigree information. In this article we: (1) extend the model presented by Su et al. to allow integration of genomic information [e.g., single nucleotide polymorphism (SNP)] and covariance between environments, (2) present an R package (FW) that implements these methods, and (3) illustrate the use of the package using examples based on real data. The FW R package implements both the two-step OLS method and a full Bayesian approach for Finlay–Wilkinson regression with a very simple interface. Using a real wheat data set we demonstrate that the prediction accuracy of the Bayesian approach is consistently higher than the one achieved by the two-step OLS method.

La competitividad del algodón colombiano frente a los principales países productores mediante el enfoque de costos de producción

En este trabajo se analiza la competitividad de la producción de algodón en Colombia, a través de un análisis comparativo de los patrones de costos de producción de las regiones productoras de Colombia, con relación a los principales países productores de la fibra de algodón. La información básica para este estudio fueron las estadísticas provenientes, en su mayoría, de Conalgodón, organizaciones de productores y la industria textil. Se usaron técnicas estadísticas para la medición económica y estimaciones por el método de mínimos cuadrados ordinarios (MCO), especialmente para la estimación de las funciones de oferta y demanda. Para el análisis de competitividad se comparó el costo unitario de producción de Colombia frente a otros países productores de fibra de algodón. Los resultados permiten concluir, por un lado, que la producción de fibra corta en Colombia es factible de aumentar y dedicar más áreas a este tipo de cultivo dada la tendencia creciente de la demanda por parte de la industria, la cual supera, en estos momentos, a la hilatura de anillos o fibra larga; y, por otra parte, en las condiciones actuales, el país no es competitivo produciendo algodón y, por esta razón, el componente de este material importado es creciente a través del tiempo.

Quasi-distributed birefringence dispersion measurement for polarization maintain device with high accuracy based on white light interferometry.

A white light temporal interferometric technique for measurement of the quasi-distributed birefringence dispersion (BD) in a polarization maintain (PM) device with high accuracy based on weighted least square (WLS) method is presented. It is verified theoretically and experimentally that the accuracy of WLS method and the conventional ordinary least square (OLS) method both are proportional to the signal-to-noise ratio (SNR) of interferogram, whereas the WLS method holds a higher scaling factor because it is more suitable for heteroscedastic model that has unequal error variance. The experiment results show a repeatability of ~4.6 × 10(-5) ps/nm @ 1550 nm with WLS method for 100 sets of data, and ~4.3 × 10(-4) ps/nm with OLS method, for an interferogram with SNR of 30 dB. Besides, WLS method without iterative operation is carried out by using power spectrum of interferogram as weight value. The feasibility of this technique is demonstrated by distinguishing the quasi-distributed BD of every part for a packaged Y-waveguide with two 1m-long PM pigtails.

CEO power, corporate social responsibility, and corporate performance

The chief executive officer(CEO)plays an irreplaceable role in the corporate social responsibility(CSR)decision-making and implementation process,but the effect of CEO's power relationships on corporate social performance is less mentioned.A-share listed companies who released CSR reports from 2009 to 2011were studied using the OLS(ordinary least squares) method to test the relationships among CEO power, CSR and corporate finance performance(CFP).The results show that structural power can help CEOs more effectively solve social-environmental problems,but it is not sufficient to improve CFP with the influence of ownership power having opposite effects on CSR and CFP.CEOs with prestige power and expert power can better relate social and business interests. The results show that companies should carefully structure the CEO's power with effective power restrictions to protect the owner's equity and maintain sustainable development.

Prediction precision for lean meat percentage in Norwegian pig carcasses using ‘Hennessy grading probe 7’: Evaluation of methods emphasized at exploiting additional information from computed tomography

ABSTRACTThe present study aims at improving the prediction of lean meat percentage (LMP) for pig carcasses based on on-line measurements from the slaughterhouses using the ‘Hennessy Grading Probe 7’ (HGP7) and auxiliary information such as gender and breed. The prediction performance is evaluated using an empirical Bayes method capable of utilizing information from a surrogate variable, that is, LMP from computed tomography. HGP7 measures thicknesses of fat and meat layers. The HGP7 measurements of subcutaneous fat, sirloin height and interior fat layer should be included as predictor variables together with gender. For efficiency at the slaughter-line gender might be omitted. The empirical Bayes method improved prediction precision only marginally compared with the standard ordinary least-squares method when applied to the full set of data. However, simulations show that the empirical Bayes method enables a considerable reduction of the data sample size without appreciable loss of prediction precision.

Continuing Medical Education via Telemedicine and Sustainable Improvements to Health.

Background. This research aims to investigate the quantitative relationship between telemedicine and online continuing medical education (CME) and to find the optimal CME lectures to be delivered via telemedicine to improve the population's health status. Objective. This study examines the following: (1) What factors foster learning processes in CME via telemedicine? (2) What is the possible role of online CME in health improvement? And (3) How optimal learning processes can be integrated with various health services? Methods. By applying telemedicine experiences in Taiwan over the period 1995–2004, this study uses panel data and the method of ordinary least squares to embed an adequate set of phenomena affecting the provision of online CME lectures versus health status. Results. Analytical results find that a nonlinear online CME-health nexus exists. Increases in the provision of online CME lectures are associated with health improvements. However, after the optimum has been reached, greater provision of online CME lectures may be associated with decreasing population health. Conclusion. Health attainment could be partially viewed as being determined by the achievement of the appropriately providing online CME lectures. This study has evaluated the population's health outcomes and responded to the currently inadequate provision of online CME lectures via telemedicine.