• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.401-409
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• 2006

We consider a generalized partly-parametric additive risk model which generalizes the partly parametric additive risk model suggested by McKeague and Sasieni (1994). As an estimation method of this model, we propose to use the weighted least square estimation, suggested by Huffer and McKeague (1991), for Aalen's additive risk model by a piecewise constant risk. We provide an illustrative example as well as a simulation study that compares the performance of our method with the ordinary least squares method.

• Korean Journal of Computational Design and Engineering
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• v.14 no.3
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• pp.159-167
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• 2009

For the robust exchange of parametric CAD model data, it is very important to perform mapping rightly and accurately between different CAD models. However, data model mapping is usually performed on a case-by-case basis. This results in the problem that mapping quality fluctuates very widely depending on the abilities of developers. In order to solve this problem, the concept of symantic distance is adapted and applied to the translation of parametric CAD model data in order to measure the difference between different CAD models quantitatively in a computer-interpretable form and systematize the mapping process.

• Communications for Statistical Applications and Methods
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• v.21 no.4
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• pp.285-296
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• 2014

The quantile residual life function has been effectively used to interpret results from the analysis of the proportional hazards model for censored survival data; however, the quantile residual life function is not always estimable with currently available semi-parametric regression methods in the presence of heavy censoring. A parametric regression approach may circumvent the difficulty of heavy censoring, but parametric assumptions on a baseline hazard function can cause a potential bias. This article proposes a Bayesian semi-parametric regression approach for inference on an unknown baseline hazard function while adjusting for available covariates. We consider a model-based approach but the proposed method does not suffer from strong parametric assumptions, enjoying a closed-form specification of the parametric regression approach without sacrificing the flexibility of the semi-parametric regression approach. The proposed method is applied to simulated data and heavily censored survival data to estimate various quantile residual lifetimes and adjust for important prognostic factors.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.176-181
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• 2011

A role of cost modeler is that of facilitating design process by the systematic application of cost factors so as to maintain sensible and economic relationships between cost, quantity, utility and appearance. These relationships help to achieve the client's requirements within an agreed budget. The purpose of this study is to develop a parametric cost estimating model for the early design stage by using the multi-dimensional system of OLAP (On-line Analytical Processing) based on the case of quantity data related to architectural design features. The parametric cost estimating models have been adopted to support decision making in the early design stage. These models typically use a similar instance or a pattern of historical case. In order to effectively use this type of data model, it is required to set data classification and prediction methods. One of the methods is to find the similar class in line with attribute selection measure in the multi-dimensional data model. Therefore, this research is to analyze the relevance attribute influenced by architectural design features with the subject of case-based quantity data used for the parametric cost estimating model. The relevance attributes can be analyzed by Analytical Characterization. It helps determine what attributes to be included in the OLAP multi-dimension.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.685-695
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• 2017

The traditional mixture of experts (ME) modeled the gate network using a certain parametric function. However, if the assumed parametric function does not properly reflect the true nature, the prediction strength of ME would become weak. For example, the parametric ME often uses logistic or multinomial logistic models for the network model. However, this could be very misleading if the true nature of the data is quite different from those models. Although, in this case, we may develop more flexible parametric models by extending the model at hand, we will never be free from such misspecification problems. In order to alleviate such weakness of the parametric ME, we propose to use the semi-parametric mixture of experts (SME) in which the gate network is estimated in a non-parametrical way. Based on this, we compared the performance of the SME with those of ME and neural networks via several simulation experiments and real data examples.

• Journal of the Korean Statistical Society
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• v.30 no.4
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• pp.645-660
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• 2001

In this paper we propose a goodness-of-fit test statistic for testing the (null) parametric model versus the (alternative) nonparametric model. Most of existing nonparametric test statistics are based on the residuals which are obtained by regressing the data to a parametric model. Our test is based on the bootstrap estimator of the probability that the smoothing parameter estimator is infinite when fitting residuals to cubic smoothing spline. Power performance of this test is investigated and is compared with many other tests. Illustrative examples based on real data sets are given.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.12
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• pp.2061-2071
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• 2003

The macro-parametric approach, which is a method of CAD model exchange, has recently been proposed. CAD models can be exchanged in the form of a macro file, which is a sequence of modeling commands. As an event-driven commands set, the standard macro file can transfer design intents such as parameters, features and constraints. Moreover it is suitable for the network environment because the standard macro commands are open, explicit, and the data size is small. This paper introduces the concept of the macro-parametric method and proposes its representation using XML technology. Representing the macro-parametric data using XML allows managing vast amount of dynamic contents, Web-enabled distributed applications, and inherent characteristic of structure and validation.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.155-162
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• 1999

This paper is concerned with the empirical Bayes estimation of one of the two shape parameters(${\theta}$) in the Burr(${\beta},\;{\theta}$) type XII failure model based on type-II censored data. We obtain the bootstrap empirical Bayes confidence intervals of ${\theta}$ by the parametric bootstrap introduced by Laird and Louis(1987). The comparisons among the bootstrap and the naive empirical Bayes confidence intervals through Monte Carlo study are also presented.

• Fibers and Polymers
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• v.5 no.1
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• pp.12-18
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• 2004

A parametric body model generation system has been developed. Using various mathematic and geometric algorithms of this system, a three-dimensionally scanned human body can be converted into a resizable body model. Once a parametric body model is formed, its size and shape can be modified instantaneously by providing appropriate anthropometric data. To facilitate the subsequent pattern arrangement process for garment drape simulation, a bounding box generation algorithm has been developed in this study. Also the model can be converted into a set of parametric surfaces that it can also be used for three-dimensional garment pattern design system.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.11
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• pp.6751-6755
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• 2013

Background: Research on cancers with a high rate of mortality such as those occurring in the stomach requires using models which can provide a closer examination of disease processes and provide researchers with more accurate data. Various models have been designed based on this issue and the present study aimed at evaluating such models. Materials and Methods: Data from 330 patients with gastric cancer undergoing surgery at Iran Cancer Institute from 1995 to 1999 were analyzed. Cox-Snell Residuals and Akaike Information Criterion were used to compare parametric and semi-parametric Cox models in modeling transition rates among different states of a multi-state model. R 2.15.1 software was used for all data analyses. Results: Analysis of Cox-Snell Residuals and Akaike Information Criterion for all probable transitions among different states revealed that parametric models represented a better fitness. Log-logistic, Gompertz and Log-normal models were good choices for modeling transition rate for relapse hazard (state $1{\rightarrow}state$ 2), death hazard without a relapse (state $1{\rightarrow}state$ 3) and death hazard with a relapse (state $2{\rightarrow}state$ 3), respectively. Conclusions: Although the semi-parametric Cox model is often used by most cancer researchers in modeling transition rates of multistate models, parametric models in similar situations- as they do not need proportional hazards assumption and consider a specific statistical distribution for time to occurrence of next state in case this assumption is not made - are more credible alternatives.