• 대한인간공학회지
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• 제22권4호
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• pp.59-78
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• 2003

Most regression models of anthropometric variables use stature and/or weight as regressors; however, these 'flat' regression models result in large errors for anthropometric variables having low correlations with the regressors. To develop more accurate regression models for anthropometric variables, this study proposed a method to estimate anthropometric variables in a hierarchical manner based on the relationships among the variables and a process to develop and improve corresponding regression models. By applying the proposed approach, a hierarchical estimation structure was constructed for 59 anthropometric variables selected for the occupant package design of a passenger car and corresponding regression models were developed with the 1988 US Army anthropometric survey data. The hierarchical regression models were compared with the corresponding flat regression models in terms of accuracy. As results, the standard errors of the hierarchical regression models decreased by 28% (4.3mm) on average compared with those of the flat models.

• Journal of Information Technology Applications and Management
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• 제11권1호
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• pp.39-51
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• 2004

Application systems outsourcing is an important part of IT outsourcing services. Application systems outsourcing costs is determined by service levels of outsourcers. Recent researches show there is a strong need to build industry-specific cost estimation models. In this study, an industry-specific application systems operation cost estimation model is suggested. We reviewed operation cost models of previous researches, and proposed a cost estimation model for security industry. Industry-specific service factors are defined and service levels are determined by Interviews with experts. The proposed model is tested and adjusted with empirical data. The new model shows more accurate prediction than previous general models. Future research will be needed to develop outsourcing cost estimation models for other industries and to refine cost models developed in this study.

• 전자공학회논문지B
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• 제32B권1호
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• pp.85-95
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• 1995

The error intensity models for the ML estimation of a signal parameter have been developed in a companion paper [1]. While the methods described in [1] are applicable to any estimation problem with continuous parameters, our main application in this paper is the time delay estimation, and comparisons among the models derived in [1] (i.e. LC, LM, and ALM models)have been made. We first consider the case where only additive Gaussian noise is involved, and then the shot noise environment where coherent impulsive noise is also involved in addition to the Gaussian noise. We compare the models in terms of the probability of error, MSE(Mean Squared Error), and the computational complexity, which are the most important performance criteria in the analysis of parameter estimation. In conclusion, the ALM model turned out to be the most adequate model of all from the viewpoints of the criteria mentioned above.

• 경영과학
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• 제14권1호
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• pp.131-149
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• 1997

Software cost estimation is an important both for buyers and sellers(developers). We reviewed domestic and foreign researches and practices on software cost estimation with function point method comprehensively, In this paper, we derived four promising alternative function point models. They are an IFPUG(International Function Point User Group)-based model(Model I), a shorthand model for client/sever software systems(Model II), a data-oricnted model for relatively large software projects(Model III), and a general- purpose function point model for non business application softwares as well as business applications(Model IV). Empirical data shows that Model I, II, and IV are very useful function point models. In particular, model II and IV look very useful models since they are concise and accurate. These models can be incorporated in a new improved guideline for software cost estimation. General opinion survey shows that Model I, II and IV are preferable. There are no significant differences in preference between buyers and sellers. The survey also shows that users think function point method is better than step(line of code)-oriented cost estimation methods in many ways including objectivity and estimation accuracy.

• 한국경영과학회지
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• 제38권1호
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• pp.201-216
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• 2013

Diffusion is a basic mathematical tool for modern financial engineering. The theory of the estimation methods for diffusion models is an important topic of the financial engineering. Many researches have been tried to apply the likelihood estimation method for estimating diffusion models. However, the likelihood estimation method for diffusion is complicated and needs much amount of computing. In this paper we develop the estimation methods which are simple enough to be compared to the Euler approximation method, and efficient enough statistically to be compared to the likelihood estimation method. We devise pseudo-likelihood and propose the maximum pseudo-likelihood estimation methods. The pseudo-likelihoods are obtained by approximating the transition density with normal distributions. The means and the variances of the distributions are obtained from the delta expansion suggested by Lee, Song and Lee (2012). We compare the newly suggested estimators with other existing estimators by simulation study. From the simulation study we find the maximum pseudo-likelihood estimator has very similar properties with the maximum likelihood estimator. Also the maximum pseudo-likelihood estimator is easy to apply to general diffusion models, and can be obtained by simple numerical steps.

• Journal of the Korean Statistical Society
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• 제31권1호
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• pp.93-107
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• 2002

Random effects generalised linear models are useful for analysing clustered count data in which responses are usually correlated. We propose a Bayesian approach to parameter estimation and variable selection in random effects generalised linear models for count data. A simple Gibbs sampling algorithm for parameter estimation is presented and a simple and efficient variable selection is done by using the Gibbs outputs. An illustrative example is provided.

• Journal of Information Processing Systems
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• 제8권4호
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• pp.621-652
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• 2012

Software Estimations provide an inclusive set of directives for software project developers, project managers, and the management in order to produce more realistic estimates based on deficient, uncertain, and noisy data. A range of estimation models are being explored in the industry, as well as in academia, for research purposes but choosing the best model is quite intricate. Estimation by Analogy (EbA) is a form of case based reasoning, which uses fuzzy logic, grey system theory or machine-learning techniques, etc. for optimization. This research compares the estimation accuracy of some conventional data mining models with a hybrid model. Different data mining models are under consideration, including linear regression models like the ordinary least square and ridge regression, and nonlinear models like neural networks, support vector machines, and multivariate adaptive regression splines, etc. A precise and comprehensible predictive model based on the integration of GRA and regression has been introduced and compared. Empirical results have shown that regression when used with GRA gives outstanding results; indicating that the methodology has great potential and can be used as a candidate approach for software effort estimation.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2003년도 추계학술대회논문집
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• pp.60-65
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• 2003

In this study, for estimating responses of a controlled structure and determining the maximum control force of velocity-dependent damping devices, three estimation models such as Fourier envelope convex model, probability model, and Newmark design spectrum are used. For this purpose, a procedure proposed by Gupta (1990) for estimating spectral velocity using pseudo-spectral velocity which is given by the estimation models is used and modified to consider the effects of increased damping ratio by the damping device. Time history results indicate that Newmark design spectrum gives the best estimation of maximum control force for over all period structures.

• Communications for Statistical Applications and Methods
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• 제10권1호
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• pp.125-134
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• 2003

Standard estimation methods for linear mixed models are sensitive to influential observations. However, tools and concepts for linear mixed model diagnostics are rudimentary until now and research is heavily demanded in linear mixed models. In this paper, we consider two diagnostics to evaluate the effects of individual observations in the estimation of fixed effects for linear mixed models. Those are Cook's distance and COVRATIO. Results of our limited simulation study suggest that the Cook's distance is not good statistical quantity in linear mixed models. Also calibration point for COVRATIO seems to be quite conservative.

• 디지털산업정보학회논문지
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• 제7권2호
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• pp.115-125
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• 2011

An accurate estimation of software development size is an important factor in calculating reasonable cost of project development and determining its success. In this study, we propose estimation models, using function point based on the functional correlation between software, with empirical data. Three models($FP_{est}(I)$, $FP_{est}(II)$, $FP_{est}(III)$) are developed with correlation and regression analysis. The validity of the models is evaluated by the significance test by comparing values of Mean Magnitude of Relative Error (MMRE) and predictions of each model at level n%. Model $FP_{est}(III)$ proved to be superior to other models such as IFPC(Indicative Function Point Count), EFPC(Estimated Function Point Count), EPFS(Early Prediction of Function Size), $FP_{est}(I)$, and $FP_{est}(II)$. As a result, the accuracy of the model appears to be very high to determine the usefulness of the model to finally overcome weakness of other estimation models. The model can be efficiently used to estimate project development size including software size or manpower allocation.