• Journal of the Korean Statistical Society
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• v.32 no.2
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• pp.93-101
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• 2003

This paper deals with testing the equality of the coefficients of variation in two inverse Gaussian populations. The likelihood ratio, Lagrange-multiplier and Wald tests are presented. Monte-Carlo simulations are performed to compare the powers of these tests. In a simulation study, the likelihood ratio test appears to be consistently more powerful than the Lagrange-multiplier and Wald tests when sample size is small. The powers of all the tests tend to be similar when sample size increases.

• Journal of the Korean Statistical Society
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• v.24 no.1
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• pp.77-90
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• 1995

The Lagrange multiplier test statistics for seasonal unit roots are derived and the asymptotic distributions of the derived statistics are obtained. The relationship between the derived LM test statistics and the "DHF" type regression t statistics are shown.are shown.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.101-114
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• 1995

In this paper we consider the multiple unit root tests both for the regular and seasonal unit roots based on the Lagrange Multiplier(LM) principle. Unlike Li(1991)'s method, by plugging the restricted maximum likelihood estimates of the nuisance parameters in the model, we propose a Lagrange multiplier test which does not depend on the existence of the nuisance parameters. The asymptotic distribution of the proposed statistic is derived and empirical percentiles of the test statistic for selected seasonal periods are provided. The power and size of the test statistic for examined for finite samples through a Monte Carlo simularion.

• Communications of the Korean Mathematical Society
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• v.20 no.1
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• pp.145-159
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• 2005

In this paper we consider the problem of testing statistical hypotheses for unknown parameters in nonlinear regression models and propose three asymptotically equivalent tests based on regression quantiles estimators, which are Wald test, Lagrange Multiplier test and Likelihood Ratio test. We also derive the asymptotic distributions of the three test statistics both under the null hypotheses and under a sequence of local alternatives and verify that the asymptotic relative efficiency of the proposed test statistics with classical test based on least squares depends on the error distributions of the regression models. We give some examples to illustrate that the test based on the regression quantiles estimators performs better than the test based on the least squares estimators of the least absolute deviation estimators when the disturbance has asymmetric and heavy-tailed distribution.

• Transactions of the Korean Society of Mechanical Engineers
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• v.15 no.2
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• pp.544-556
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• 1991

The optimization of many engineering design problems requires a nonlinear programming algorithm that is robust and efficient. A general-purpose nonlinear optimization program IDOL (Interactive Design Optimization Library) is developed based on the Augmented Lagrange Mulitiplier (ALM) method. The ideas of selecting a good initial design point, using resonable initial values for Lagrange multipliers, constraints scaling, descent vector restarting, and dynamic stopping criterion are employed for computational enhancement to the ALM method. A descent vector is determined by using the Broydon-Fletcher-Goldfarb-Shanno (BFGS) method. For line search, the Incremental-Search method is first used to find bounds on the solution, then the bounds are reduced by the Golden Section method, and finally a cubic polynomial approximation technique is applied to locate the next design point. Seven typical test problems are solved to show IDOL efficient and robust.

• Communications for Statistical Applications and Methods
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• v.11 no.3
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• pp.475-484
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• 2004

This paper consider the testing problem of grouped heteroscedasticity in the linear regression model. We provide the Lagrange Multiplier(LM), Wald, Likelihood Ratio (LR) test statistis for testing of grouped heteroscedasticity. Monte Carlo experiments are conducted to study the performance of these tests.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.11
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• pp.223-234
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• 2021

This study aims to examine whether operating cash flows influence banks' financial stability in Pakistan. The study employed annual panel data collected from annual reports of 20 commercial banks listed on the Pakistan Stock Exchange for the year 2011 to 2019. Free cash flow yield was taken as the dependent variable while cash flow ratio was selected as the independent variable, and net interest margin, income diversification, asset quality, financial leverage, the cost to income ratio, advance net of provisions to total assets ratio, capital ratio, financial performance, breakup value per share and bank size were taken as control variables. The study performed ordinary least square technique, random and fixed effects models, Hausman test, Lagrange multiplier test, descriptive and correlation analysis. Results showed that operating cash flows and net interest margin significantly and positively influenced banks' financial stability while the cost to income ratio and advances net of provisions to total assets ratio significantly and negatively associated with banks' financial stability. To improve financial stability, banks should become more cost-effective and enhance their liquidity levels by lowering lending activities. In the future, it would be useful to compare commercial and investment banks, also Islamic and conventional banks in the same research setting.

• Journal of the Korean Statistical Society
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• v.28 no.4
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• pp.489-501
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• 1999

In this paper, we consider the panel data regression model in which the disturbances have nested error component. We derive a Lagrange Multiplier(LM) test which is jointly testing for the presence of random individual effects and nested effects under the normality assumption of the disturbances. This test extends the earlier work of Breusch and Pagan(1980) and Baltagi and Li(1991). Further, it is shown that this LM test has the same asymptotic distribution without normality assumption of the disturbances.

• Journal of Advanced Marine Engineering and Technology
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• v.26 no.3
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• pp.313-319
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• 2002

Since the oil crisis in 1970, a great deal of effort has been made to develop automatic electric load sharing systems as a part of the efforts to save energy. A large scale electric generating system composes more than two generators whose characteristics may be different. When such a system is operated individually or in parallel, the lagrange multiplier's method has difficulty in achieving optimal load distribution because generators usually have the limitations of the operating range with inequality constraints. Therefore, a suitable operating mode of generators has to be decided according to the selection of the generators to meet electric power requirements at the minimum cost. In this study, a method which solves the optimal electric load distribution problem using the dynamic programming technique is proposed. This study also shows that the dynamic programming method has an advantage in dealing with the optimal load distribution problem under the limitations of the operating range with inequality constraints including generator operation mode. In this study, generator operating cost curve of second order equation by shop trial test results of diesel generators are used. The results indicate that the proposed method can be applied to the ship's electric generating system.

• Journal of the Korean Statistical Society
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• v.29 no.2
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• pp.231-245
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• 2000

This paper considers a linear regression model with space and time data in where the disturbances follow spatially correlated error components. We provide the best linear unbiased predictor for the one way error components. We provide the best linear unbiased predictor for the one way error component model with spatial autocorrelation. Further, we derive two diagnostic test statistics for the assessment of model specification due to spatial dependence and random effects as an application of the Lagrange Multiplier principle.