• Communications for Statistical Applications and Methods
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• 제26권1호
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• pp.69-78
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• 2019

A structured model with both single-index and varying coefficients is a powerful tool in modeling high dimensional data. It has been widely used because the single-index can overcome the curse of dimensionality and varying coefficients can allow nonlinear interaction effects in the model. For high dimensional index vectors, variable selection becomes an important question in the model building process. In this paper, we propose an efficient estimation and a variable selection method based on a smoothing spline approach in a partially linear single-index-coefficient regression model. We also propose an efficient algorithm for simultaneously estimating the coefficient functions in a data-adaptive lower-dimensional approximation space and selecting significant variables in the index with the adaptive LASSO penalty. The empirical performance of the proposed method is illustrated with simulated and real data examples.

• 한국경영과학회지
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• 제37권1호
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• pp.29-43
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• 2012

We design a DEA-AR model using multiple regression analysis with new methods which limit weights. When there are multiple input and single output variables, our model can be used, and the weights of input variables use the regression coefficient and coefficient of determination. To verify the effectiveness of the new model, we evaluate the efficiency of the Regional Corporations in Korea. Accordance with statistical analysis, it proved that there is no difference between the efficiency value of the DEA-AR using AHP and our DEA-AR model. Our model can be applied to a lot of research by substituting DEA-AR model relying on AHP in the future.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 춘계학술대회
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• pp.1148-1153
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• 2004

Recently the high speed end-milling processing is demanded the high-precise technique with good surface rougj1ness and rapid time in aircraft, automobile part and molding industry. The working factors of high speed end-milling has an effect on surface roughness of cutting surface. Therefore this study was carried out to analyze the working factors to get the optimum surface roughness by design of experiment. From this study, surface roughness have an much effect according to priority on Spindle speed, feed rale, hardness and axial depth of cut By design of experiment, it is effectively represented shape characteristics of surface roughness in high speed end-milling And determination($R^2$) coefficient of regression equation had a satisfactory reliability of 89.7% and regression equation of surface roughness is made by regression analysis.

• 한국정밀공학회지
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• 제21권6호
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• pp.52-59
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• 2004

Recently the high speed end-milling processing is demanded the high-precise technique with good surface roughness and rapid time in aircraft, automobile part and molding industry. The working factors of high speed end-milling has an effect on surface roughness of cutting surface. Therefore this study was carried out to analyze the working factors to get the optimum surface roughness by design of experiment. From this study, surface roughness have an much effect according to priority on distance of cut, feed rate, revolution of spindle and depth of cut. By design of experiment, it is effectively represented shape characteristics of surface roughness in high speed end-milling. And determination($R^2$) coefficient of regression equation had a satisfactory reliability of 76.3% and regression equation of surface roughness is made by regression analysis.

• Communications for Statistical Applications and Methods
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• 제4권2호
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• pp.541-548
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• 1997

In regression model with nested error structure interval estimations on regression coefficients in different stages are proposed. Ordinary least square estimators and generalized least square estimators of the regression coefficients in this model are derived for between and within group model. The confidence intervals are dervied by using independent idstributional properties between regression coefficient estimators and quadratic froms obtained from the model.

• Journal of the Korean Statistical Society
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• 제8권2호
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• pp.107-109
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• 1979

Consider a linear model with n observations and k explanatory variables: (1)b $y=X\beta+u, u\simN(0,\sigma^2I_n)$. We assume that the model satisfies the ideal conditions. Consider the general linear constraints on regression coefficient vector: (2) $R\beta=r$, where R and r are known matrices of orders $q\timesk$ and q\times1$ respectively, and the rank of R is $qk+q$.

• Communications for Statistical Applications and Methods
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• 제6권1호
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• pp.1-10
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• 1999

When the component proportions in mixture experiments are restricted by lower and upper bounds multicollinearity appears all too frequently. The ridge regression can be used to stabilize the coefficient estimates in the fitted model. I propose a graphical method for evaluating the mixture component effects of ridge regression estimator with respect to the prediction variance and the prediction bias.

• Journal of the Korean Statistical Society
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• 제5권1호
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• pp.19-28
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• 1976

In empirical study for fitting a multiple linear regression model for successive cross-sections data observed on the same set of independent variables over several time periods, one often faces the problem of poor $R^2$, the multiple coefficient of determination, which provides a standard measure of how good a specified regression line fits the sample data.

• The Journal of Asian Finance, Economics and Business
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• 제7권10호
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• pp.51-61
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• 2020

The study aims to compare whether using Earnings Response Coefficient (ERC) is better than using the new concept of Accounting Earnings Response Coefficient (AERC) in determining the earnings quality response coefficient value. Also, the study seeks to explain the effect of company characteristics and corporate governance on AERC through voluntary disclosure and information asymmetry. Research samples include 69 manufacturing companies listed on the Indonesian Stock Exchange over the period 2014-2017. The data come from annual reports, stock market prices, CSPI, EPS, stock returns and market returns. The research model is tested using the structural equation model (SEM) with partial least square (PLS). The results showed the value of the earnings response coefficient produced by AERC and ERC was different. Earnings quality resulting from AERC regression by adding CFO values better reflects the actual earnings quality. These results are consistent with the concept built from the proposition about earnings quality at AERC, that quality earnings are informative accounting earnings. The theoretical findings of this study provide an explanation that operational cash flow plays a role in evaluating earnings quality, while providing reinforcement that the ERC regression model fails to detect stock market reactions to information relevant to the aggregated values of accounting earnings.

• 한국농공학회지
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• 제22권3호
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• pp.75-87
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• 1980

Most hydro]ogic phenomena are the complex and organic products of multiple causations like climatic and hydro-geological factors. A certain significant correlation on the run-off in river basin would be expected and foreseen in advance, and the effect of each these causual and associated factors (independant variables; present-month rainfall, previous-month run-off, evapotranspiration and relative humidity etc.) upon present-month run-off(dependent variable) may be determined by multiple regression analysis. Functions between independant and dependant variables should be treated repeatedly until satisfactory and optimal combination of independant variables can be obtained. Reliability of the estimated function should be tested according to the result of statistical criterion such as analysis of variance, coefficient of determination and significance-test of regression coefficients before first estimated multiple regression model in historical sequence is determined. But some error between observed and estimated run-off is still there. The error arises because the model used is an inadequate description of the system and because the data constituting the record represent only a sample from a population of monthly discharge observation, so that estimates of model parameter will be subject to sampling errors. Since this error which is a deviation from multiple regression plane cannot be explained by first estimated multiple regression equation, it can be considered as a random error governed by law of chance in nature. This unexplained variance by multiple regression equation can be solved by stochastic approach, that is, random error can be stochastically simulated by multiplying random normal variate to standard error of estimate. Finally hybrid model on estimation of monthly run-off in nonhistorical sequence can be determined by combining the determistic component of multiple regression equation and the stochastic component of random errors. Monthly run-off in Naju station in Yong-San river basin is estimated by multiple regression model and hybrid model. And some comparisons between observed and estimated run-off and between multiple regression model and already-existing estimation methods such as Gajiyama formula, tank model and Thomas-Fiering model are done. The results are as follows. (1) The optimal function to estimate monthly run-off in historical sequence is multiple linear regression equation in overall-month unit, that is; Qn=0.788Pn+0.130Qn-1-0.273En-0.1 About 85% of total variance of monthly runoff can be explained by multiple linear regression equation and its coefficient of determination (R2) is 0.843. This means we can estimate monthly runoff in historical sequence highly significantly with short data of observation by above mentioned equation. (2) The optimal function to estimate monthly runoff in nonhistorical sequence is hybrid model combined with multiple linear regression equation in overall-month unit and stochastic component, that is; Qn=0. 788Pn+0. l30Qn-1-0. 273En-0. 10+Sy.t The rest 15% of unexplained variance of monthly runoff can be explained by addition of stochastic process and a bit more reliable results of statistical characteristics of monthly runoff in non-historical sequence are derived. This estimated monthly runoff in non-historical sequence shows up the extraordinary value (maximum, minimum value) which is not appeared in the observed runoff as a random component. (3) "Frequency best fit coefficient" (R2f) of multiple linear regression equation is 0.847 which is the same value as Gaijyama's one. This implies that multiple linear regression equation and Gajiyama formula are theoretically rather reasonable functions.