• Journal of applied mathematics & informatics
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• 제25권1_2호
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• pp.489-493
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• 2007

Two diagnostic measures based on the likelihood distance for constrained regression with linear constraints on regression coefficients are derived. They are used for identifying influential observations in constrained regression. A numerical example is provided for illustration.

• Communications for Statistical Applications and Methods
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• 제14권2호
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• pp.281-286
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• 2007

Cook's distance is generalized to the multiple linear regression with linear constraints on regression coefficients. It is used for identifying influential observations in constrained regression models. A numerical example is provided for illustration.

• Communications for Statistical Applications and Methods
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• 제15권2호
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• pp.229-234
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• 2008

A Cook-type distance is considered for investigating the influence of observations in constrained regression models. Its exact sampling distribution is derived, which is used for judging whether each observation is influential or not. A numerical example is provided for illustration.

• Communications for Statistical Applications and Methods
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• 제16권3호
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• pp.495-500
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• 2009

Two diagnostic plots, added variable plot and partial residual plot, are proposed when a new explanatory variable is linearly added to constrained regressions. They are useful for investigating the effect of adding an explanatory variable to the constrained regression. They visually give an overall impression of the strength of linear relationship between response variable and added variable. A numerical example is provided for illustration.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권8호
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• pp.3086-3101
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• 2021

To supply precise marketing and differentiated service for the electric power service department, it is very important to predict the customers with high sensitivity of electric power failure. To solve this problem, we propose a novel grouped 𝑙_1/2 sparsity constrained logistic regression method for sensitivity assessment of electric power failure. Different from the 𝑙₁ norm and k-support norm, the proposed grouped 𝑙_1/2 sparsity constrained logistic regression method simultaneously imposes the inter-class information and tighter approximation to the nonconvex 𝑙₀ sparsity to exploit multiple correlated attributions for prediction. Firstly, the attributes or factors for predicting the customer sensitivity of power failure are selected from customer sheets, such as customer information, electric consuming information, electrical bill, 95598 work sheet, power failure events, etc. Secondly, all these samples with attributes are clustered into several categories, and samples in the same category are assumed to be sharing similar properties. Then, 𝑙_1/2 norm constrained logistic regression model is built to predict the customer's sensitivity of power failure. Alternating direction of multipliers (ADMM) algorithm is finally employed to solve the problem by splitting it into several sub-problems effectively. Experimental results on power electrical dataset with about one million customer data from a province validate that the proposed method has a good prediction accuracy.

• 융합경영연구
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• 제7권3호
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• pp.43-49
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• 2019

Purpose - This paper empirically investigates what factors contribute to management decisions by corporate governance in the Korean stock market. In the paper, dividends and investments were imployed as management decisions and major stockholders' shares and foreign investors' shares were used as corporate governance. Research design, data, and Methodolog - Samples are constructed by manufacturing firms listed on the stock market of Korea as well as those who settle accounts in December from 2001 to 2018. Financial institutions are excluded from the sample as their accounting procedures, governance and regulations differ. This study adopted the panel regression model to assess the sample construction including yearly and cross-sectional data. Results - This results support the literatures that major shareholders showed insignificance to dividends, positive significance to investment in financially unconstrained firms and negative significance to investment in financially constrained firms. Whereas foreign investors favor firms to increase dividends but they decrease investments only in financially constrained firms. Conclusion - This paper documented evidence that financial constrained firms use dividends for their investment and foreign investors decrease investments under financial constraints. But for dividends decisions, foreign investors give significant positive impacts irrespective of financial constraints.

• 경영과학
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• 제14권1호
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• pp.1-10
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• 1997

We consider a problem of estimating the economics of scale for the natural gas industries. The goal programming/constrained regression is employed for estimating the economics of scale for the natural gas industry, and the problem is formulated as a linear programing problem. Also the translog cost function is used to represent the cost structure for the natural gas industry. The Korean Gas Corporation was selected as a case study, and we demonstrate that the suggested goal programming/constrained regression approach is appropriate for estimating the economies of scale for the Korean nature gas industry.

• Communications for Statistical Applications and Methods
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• 제10권2호
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• pp.519-524
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• 2003

We suggest a method of identifying outliers, using local influence, in regression when linear constraints are imposed on the regression coefficients. An example is given for illustration.

• Communications for Statistical Applications and Methods
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• 제9권3호
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• pp.619-627
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• 2002

It is unpractical for the optimal design theory based on the given model and assumption to be applied to the real-world experimentation. Particularly, when the experimenter feels it necessary to consider multiple objectives in experimentation, its modified version of optimality criteria is indeed desired. The constrained optimal design is one of many methods developed in this context. But when the number of constraints exceeds two, there always exists a problem in specifying the lower limit for the efficiencies of the constraints because the “infeasible solution” issue arises very quickly. In this paper, we developed a sequential approach to tackle this problem assuming that all the constraints can be ranked in terms of importance. This approach has been applied to the polynomial regression model.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1986년도 한국자동제어학술회의논문집; 한국과학기술대학, 충남; 17-18 Oct. 1986
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• pp.73-77
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• 1986

Optimal input design problem for linear regression model with constrained output variance has been considered. It is shown that the optimal input signal for the linear regression model can also be realized as an ARMA process. Monte-Carlo simulation results show that the optimal stochastic input leads to comparatively better estimation accuracy than white input signal.